quantum machine learning by 陈啊光

Quantum Machine Learning Project 3UAU0 Group 4 Group Members

T.M.P. Broekema (1018395) R.C.A. de Bruijne (1016912) S. Buntinx (0970667) G. Chen (1281089)

Supervisors Tech-S: S. Musolino USE-S: E.M. Mas Tur

Responsible Lecturers Prof. Dr. F. Alkemade Dr. Ir. J. Beckers

Eindhoven, April 5, 2019

3UAU0

Information technologies of the future

Abstract In this report an application is proposed for quantum machine learning. This is done by looking at a few examples of machine learning techniques and their implementation on a quantum computer. Mostly KNN algorithms and neural networks are explained, both have been tested on a quantum computer but only on a small scale (5 qubits). By looking at the applications of classical machine learning, a few applications were found as they would be benefit the most from the quantum speedup. We chose mass surveillance with quantum machine learning as our application and compared it to alternatives, such as classical computers and physical ways of surveying. We narrowed our application to tracking an finding people using cameras and also predicting or detecting threatening behaviour. A business model is developed for the application and pay per minute cost structure is used as it seems the most fitting way because it allows our customers to start small without a big initial investment. For the cost estimation, the price of the D-wave quantum computer was used as this is currently the only sort of quantum computer for sale. As for now, its not feasible to do surveillance on a quantum computer as there is still development needed in both the hardware and software that should be used. However, when one quantum computer can analyze about 168 Full HD camera streams simultaneously, break even should be possible and the application will be financially feasible.

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Information technologies of the future

Contents Introduction

1 Technology description 1.1 Quantum computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Machine learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4 4 7

2 Possible applications 2.1 General overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Economic recession forecasting . . . . . . . . . . . . . . . . . . . 2.3 Training of (deep) neural networks . . . . . . . . . . . . . . . . . 2.4 Mass surveillance . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Current status of FIT . . . . . . . . . . . . . . . . . . . . . . . . 2.6 The chosen application: mass surveillance for safety enhancement

. . . . . .

12 12 12 13 13 14 16

3 Competing technologies 3.1 Overview of chapter . . . . . . . . . . . . . . . . . . . . . . 3.2 Algorithms written by humans . . . . . . . . . . . . . . . . 3.3 Machine learning methods on classical computers . . . . . . 3.4 Competing to other forms of criminal search and terminate 3.5 Competing mass surveillance business models . . . . . . . .

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17 17 17 17 18 19

4 Our 4.1 4.2 4.3 4.4

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20 20 23 24 25

application Business model canvas . . . . . . . . . . . . . . . . . . . Financial overview . . . . . . . . . . . . . . . . . . . . . Description of the technology . . . . . . . . . . . . . . . Technological development needed for market readiness

. . . .

5 Recommendations

6 Conclusion

References

A Appendix: Project organisation A.1 Roles and responsibilities of the team members . . . . . . . . . . . . . A.2 Planning of team meeting and weekly meeting with supervisors. . . . . A.3 Brief initial plan to resolve defined RQ(â&#x20AC;&#x2DC;s) . . . . . . . . . . . . . . . . A.4 Planning and intermediate deliverables for each of the team members

. . . .

33 33 33 34 35

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Information technologies of the future

Introduction Background on subject Quantum computers are one of the most interesting and promising developing techniques present at the moment. Many possibilities for simulating classical nature are already being considered, as quantum computers should be able to simulate nature in just the way nature works. On the other hand, the field of machine learning is currently quickly developing. Where machine learning previously was more a theoretical subject, lately more and more practical examples are emerging. Examples include face recognition, self-driving and classification. A very exciting combination can be made between quantum computing and machine learning: quantum machine learning. Because quantum computers are basically large ‘parallel’ computers, an exponentially large number of computations can take place at the same time. For an important branch of machine learning, neural networks, this increased computational power could result in much quicker training of the network and therefore the possibility to use data sets of unprecedented sizes. Also, other forms of machine learning can be sped up using quantum computing. In an age of ever-increasing data, these functionalities sound very promising. However, much work needs to be done in order to get quantum learning as good as necessary in order to achieve quantum ‘supremacy’. Currently, mostly only small quantum computers and small machine learning tasks have been created in real life. There is a lot of work to do in the coming years to develop these techniques to where they are every day usable. In this report, we will work towards a possible application in the quantum machine learning domain. Project goals In order to bring this project to a good end, we proposed various sub-questions that should be answered in order to get a satisfactory final result. These questions can be found in Appendix A.3. The second goal that this project serves, is to get us more familiar with quantum machine learning and the business side that is also involved within this project. Approach followed during project During the project, much literature research was done. Many of the papers are very recently written, as quantum machine learning is a very new and evolving field. Also, a researcher of the quantum department of the Eindhoven University of Technology was interviewed to get a feeling for the subject and if we are on the right track. Some of the information is also retrieved from research blogs from IBM or Google. Not all the information that the researchers find at these companies is directly translated into papers, therefore the research blogs also provide a valuable source of information. Structure of the rest of the report In the following chapters, the process of creating a suitable business model for a quantum machine learning application will be addressed. In the first chapter, the technology is described; on one hand the quantum computing, on the other hand, a couple of machine learning practices. In the second chapter, possible applications are described. Chapter three describes various possible competing technologies. Chapter four addresses our application in more detail and presents the business canvas. In chapter 5, some recommendations are given. Finally, the report is concluded with a conclusion.

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Technology description

In this chapter first the differences between a quantum computer and a classical computer shall be explained, more specifically the differences between the qubit and the classical bit. Then, to show why quantum computers are faster, an example of a quantum algorithm will be provided. Namely, the Grover search algorithm which can be used for an unstructured search. Then the different types of machine learning will be elaborated upon and the workings of two important algorithms shall be explained (KNN and K-means). Again, the quantum speed-up for these algorithms that can be achieved will be addressed. Lastly, neural networks will be introduced.

1.1

Quantum computing

In a classical computer, information encoded in binary numbers is manipulated. The smallest chunk of information is called a bit, which basically is just a switch which can be in two states: on and off. So if we want to represent this by numbers, the bit can have two values: either 1 or 0. By stringing these bits together, any number can be made. By performing operations like addition and subtraction on these numbers, a computer can alter these numbers. Unfortunately, when the amount of information gets large or the calculation complicated, it will take the computer a very long time to complete its computational task. A quantum computer can in some cases speed up these processes. This is done by using a very fundamental principle of quantum mechanics, superposition. Where a classical bit is either a 1 or a 0, a qubit (quantum bit) is in a superposition state of 1 and 0 [14]. Meaning that it is a combination of 1 and 0, but at the moment you measure it, it will collapse in one of the two states. You can think of this by imagining a spinning coin on the table. If heads corresponds to 1 and tails to 0, the moment the coin is spinning it is in a superposition state. It is neither heads or tails it is in a combination of both. Only when you stop the coin (make a measurement) you force the coin to collapse in one of the two possible states. A coin is equally heavy on both sides, so therefore both heads and tails have a 50/50 chance. However, when one side is lighter the probabilities will not be the same, for example 60% to land on tails. In that case, when the coin is spinning it is in a superposition of 0.6 tails and 0.4 heads. When you add another qubit, the system can be in an arbitrary superposition of 4 states(11,10,01,00). More generally, a quantum computer with n qubits can be in a superposition of 2n states. On the other hand a classical computer can only be in one of these states at the time. The superposition state can be represented by the following equation[14]: |Ψi = c0 |0i + c1 |1i ,

(1)

where |Ψi represents the qubit in a superposition state. Furthermore c0 is a term related to the the probability of getting a zero when a measurement on the state is done, c1 is related to the probability of getting a 1 upon measurement. Of course these probabilities add up to one as you will always measure one of the two possibilities. This is done by the following relation [14], | c0 |2 + | c1 |2 = 1.

(2)

These qubits can be made by using a particle with two spin states for example, spin up and spin down. When clever algorithms are implemented to operate on these states a quadratic speed up for certain processes can be achieved. The quantum machine learning technology doesn’t really have performance dimensions yet, mostly because of the research done on quantum machine learning still is very conceptual. Quantum algorithms used for machine learning have been tried on small quantum computers(5 qbits) although this doesn’t give proper performance dimensions for the application we propose. The quantum computers that already for sale (the D-Wave Two for example) which might not even be a ’true’ quantum computer but use so called quantum 4

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Information technologies of the future

annealing instead of quantum gates. These do not run the algorithms used in paper but give us some measure on how much such a system would cost. Some physical dimensions can be estimated, the D-wave two, for example, is approximately 10 square metres and its costs roughly 15 million dollar[12]. 1.1.1

Algorithms

To understand exactly why a quantum computer is faster, it is the best look at the algorithms which have already been constructed. A well-known application is for example the quantum unstructured and unsorted database search. Classically, this is done by considering each piece of a given set until the right element is found. However, on a quantum computer using Grover’s algorithm, the time it takes until the right thing is found, scales as the square root of the number of inputs. Opposing to the classical computers which will scale linearly, this is a huge improvement. The actual algorithm itself is really complicated, so in figure 1.1 a graphical overview of the components is presented.

Figure 1.1: Graphical overview of Grover’s algorithm. The top part of the figure shows the entire circuit which uses a couple of Grover operators each indicated with a label G. The drawing at the bottom displays one of these operators worked out in more detail. To complete the task at hand, the number of operators that is needed is approximately equal to N if the database has a size 2N.[14] To make the explanation more concrete, a specific case will be explained. One classical example is that of finding which person belongs to a certain given phone number by using a telephone book. Doing the task the other way around is easy because the names are sorted alphabetically. Therefore when the name is known you can do a structured search to find the right number. The phone numbers, however, are not sorted in anyway. Only a brute force method can be utilised to find the name matching with a certain telephone number. With Grover’s algorithm, this searching process can be sped up. The data (the telephone book) first will be put in a superposition state using Hadamard gates. This state can be written mathematically: X αx |xi , (3) x

where |xi is each input so each phone number in our case, αx is the coefficient indicating how much of this phone number is present in the superposition state. For example, if half of the superposition state consists of the number 06 12345678 then α0612345678 =0.5. At first, all phone numbers will be equally present in the superposition state meaning that if it would be measured, the probability of finding a certain number is equal for all. This means we can graphically represent this like displayed in the figure 1.2 A.

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Information technologies of the future

Furthermore we also need two operations to work on the data set, a phase inversion and an inversion about the mean. A phase inversion of a certain phone number means that its superposition coefficient stays equal in size but with a minus sign added up front (it is mirrored in the x-axis). An inversion about the mean moves a point which is at a certain distance above the average value to below this average but still with the same distance to the average (the point is mirrored in the average line). These two operations will have the effect that every time they are performed the superposition coefficient of the phone number of interest is enlarged. This is best explained by steps B and C of 1.2. At step B the amplitude of x* is mirrored in the x-axis. Keep in mind that is effect is reached by letting the transformation work on the entire superposition state and not only on the state you are looking for. So it is not necessary to go through every phone number until you have found the right one to flip, if that were the case the algorithm would take even longer than a classical computer. This can be done because it is known which number we are looking for but not where it is stored precisely. The information about probabilities is stored in a super position and there is no way to access this entire probability distribution with 1 measurement. The dotted line is the average of all coefficients, at A this coincides with the continuous line. But at B, this line is slightly lower because of the one amplitudes that is now negative. When all amplitudes are flipped around, the coefficient of the number that was searched for, is relatively larger than the other coefficients. If after this a measurement would be done on the superposition state the chance of getting the number you are interested in would be slightly larger then the probability of getting any other random wrong phone number. But doing a measurement now is not yet really useful as your final result still has a large probability of being wrong. Therefore, it is necessary to run the algorithm multiple times and each time the probability coefficient of your number of interest increases a little bit. When this is done until the probability is approximately 100 % then we can do a measurement and be pretty confident that we have found the right number.

Figure 1.2: Graphical explanation of Groverâ&#x20AC;&#x2122;s search. Grover search: three situations where the input x is plotted against superposition Îą. A: Starting state of the superposition before any transformations. B: The state after the first phase inversion where the dotted line is the average of all coefficients C: The state after the inversion about the mean

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Groverâ&#x20AC;&#x2122;s algorithm is a simple explanation of why quantum computing could give a huge speedup in certain problems. In the next section, we will discuss the possibilities of quantum computing in machine learning, a field that usually is heavy computation and data dependent.[14]

1.2

Machine learning

As the name implies, machine learning is used to let the computer process data without explicit instructions and build a model from the learning data. This model is then is able to make predictions or decisions on future data. These algorithms are already used for email filtering and computer vision as they allow for processing of big data sets where it is not feasible to create an algorithm. For instance, Google uses these kinds of algorithms to classify pictures on the internet or train a computer to play the game Go where a lot of matches are analysed. The machine learning algorithms can be divided into the following three categories. supervised learning The data-set is already classified and the algorithm will try to derive patterns from the training data. This is useful for pattern recognition. unsupervised learning The data supplied is not labelled or classified and the algorithm is used to find commonalities in the data by for example clustering. This can be seen as a human making sense of unknown input and will try to see patterns and logic in the unstructured data. reinforcement learning The algorithm is reinforced by using a feedback system. For example, an algorithm that will try to find a strategy to play a game where there is a feedback for how well the strategy works. As these algorithms are computationally intensive, it is interesting to see if this process can be sped up by using quantum computing. A lot of classification algorithms are based on linear algebra which can be sped up by quantum computing. In the next sections, a couple of well known machine-learning techniques will be addressed. 1.2.1

K-nearest neighbour algorithm

In the K-nearest neighbour algorithm, or KNN algorithm, an unclassified data point can be classified using a data set of already classified points with certain parameters. Using these parameters a distance function can be defined. As a new data point (A) is supplied the algorithm will calculate the distance from A to already existing data points. By looking at the k closest points the algorithm can decide which class point A belongs to. For example the algorithm can be used to classify the family of unknown animal. All the measured parameters are compared to the existing data-set by calculating the euclidean distance between the parameter vectors. Next the k (for this example 3) nearest neighbours are picked. As two of the neighbours are a lizard and one is a fish, the algorithm will classify animal A as a lizard. As can be seen in 1.3 the choice of K can influence the outcome of the algorithm as for K=3 the new data point will be classified as a red triangle and for K=5 it will be a blue square.

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Figure 1.3: Example of KNN algorithm with two classes. The green point is classified, it can either be a red triangle or a blue square.It will be classified as the majority of the k nearest neighbours, here k=3(small circle) or k=5(bigger circle). Choosing the right k value can be important and depends on the data set. Attribution: Example of k-NN classification by Antti Ajanki / CC BY-SA 3.0[2] The choice of k can influence the outcome of the classification, a small k will result in a lot of noise. Choosing a big k is also not a good idea as the number of data points for each class can influence the outcome. In the instance of a binary system with only two classes the value of k has to be uneven to work. In this algorithm the parameters can be weighted in order to tune the influence of the parameter. For instance the parameters: number of legs(any positive integer number) and whether the animal can fly or not (0 or 1). If these parameters are then compared with an euclidean metric the amount of legs will be much more influential because they are clustered more closely due to their discrete nature. To still classify properly the parameterâ&#x20AC;&#x2122;s have to be carefully weighted. 1.2.2

K-Means

For the case of unsupervised learning, the data is not labelled and can be classified by trying to find clusters of data points with data points are likely to match. The algorithm works in an iterative way. First, k vectors are proposed as centroids of the clusters, these vectors are picked at random. The distance from these vectors to the data points is calculated and a set of clusters is formed by picking the closest points to the centroid. As this centroid is probably not the actual center of this cluster, a new centroid is calculated based upon the found cluster. These centroids are again processed using the steps above, hereby the clusters will also change as the centroid vectors are picked differently. These steps are repeated until newly calculated centroids are (nearly) the same as the centroids in the previous step. As expected this can lead to local minima and the final clusters can be influenced by the initial choice of the centroids.

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Figure 1.4: K-Means iterations. In the first step, random centroids are assigned. Points closest to the centroid are assigned to a class. Then a new centroid is calculated by taking the average of the class. Then closest points to the centroid are grouped as the new class. By calculating this over and over until the centroid of the previous step is equal to the centroid of the new step.[27] 1.2.3

Quantum speedup of KNN and K-means

The above discussed algorithms heavily rely upon calculation the distance between a large amount of data points. First we have to consider how to store these data points in a quantum state. The simplest approach is to represent classical information as a string of binary qubits, similar to storing data on a regular computer. This would give a |x1 , x2 ....xn i for n qubits to form a 2n dimensional Hilbert space. This might work for a search algorithm such as Groverâ&#x20AC;&#x2122;s algorithm, but for calculating distances there might be better solutions. for instance Seth Loyd proposed to encode classical information in the norm of the quantum state 1 (hx|xi = |~x|â&#x2C6;&#x2019; 2 ~x) [30] [21]. It is still interesting how to encode this data in a way so the distance can be calculated more efficiently. In [18] two algorithms for calculating the nearest neighbours are tested on IBMâ&#x20AC;&#x2122;s quantum simulator. The resulting calculation matches the classical algorithms in terms of accuracy. They are both based on calculating the hamming distance of the data points. For calculating the hamming distance, two strings of bits are compared and the number of bits that does not match is the hamming distance, as can be seen in 1.5.

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Figure 1.5: Hamming distance. The distance between to values is based upon how many bits have to be flipped in order to make both values equal.[19] The algorithms used are explained in [34] and [32]. As the quantum state is a superposition of the all the data points, the amount of calculations is not dependent on the amount of data points N, just on the number n of features per data point. This would result in a large speedup when used with big data. 1.2.4

Neural networks

A neural network is a form of machine learning that is loosely based on how the brain learns. A basic neural network consists of several layers of nodes, of which the first layer is the input layer, the middle layers are the hidden layers and the last layer is the output layer. A simple neural network is displayed in figure 1.6.

Figure 1.6: Example of a small neural network. This neural network consists of an input layer with three input nodes, a hidden layer with four hidden nodes and an output layer with 2 output nodes. Input is propagated through the network via weights and biases of the nodes. Attribution: Colored neural network by Glosser.ca / CC BY-SA 3.0 [42] Artificial neurons are often indicated by nodes. The connections between the nodes are called edges. These connections usually transmit a real number value. The output of the node is calculated as a function of the sum of all its inputs. Possibly, also a ‘threshold value’, or bias, is taken into account. In order to be able to ‘train’ the network, the edges have a weight that can be varied. The weight can be interpreted as a multiplication of the output of the node. A low weight might decrease the output, but a high weight might increase it. Also, the biases of all the nodes can be adapted in the learning process.

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To correctly train the network, a cost function is made that calculates the squared difference between the desired output and the actual output. The cost function can be optimised by changing all the weights and biases present in the network. In order to have optimal results, the output of the cost function must be as small as possible. To accomplish this, a gradient is taken of the cost function. The gradient shows to what direction values should be heading in order to minimise the cost function. By using back-propagation, new weights and biases are calculated from the output nodes to the input nodes. The intrinsic property of Quantum Mechanics of representing large vectors and matrices and performing linear operations on such vectors, is believed to lead to an exponential increase in either memory storage or processing power[37]. The implementation of quantum neural networks (QNNs) has only recently come to a more mature form. In 2014, a review article of the quantum neural network field comes to the conclusion that “QNN research has not found a coherent approach yet and none of the competing ideas can fully claim to be a QNN model according to the requirements set here”, and no proposals seem to lead to the construction of a mature QNN model [35]. Four years after this article, an artificial neuron implemented on a quantum processor was created, based on a so-called ‘perceptron’, a way of making a quantum artificial neuron. Although still some minor problems arose, an exponential speedup was expected and observed[37]. Concluding, the first concrete steps have been set to reach applications of QNNs. When quantum hardware scales up, the amounts of data that can be processed will be much higher than classical technologies. Therefore, bigger neural networks will be trained faster than with the current technologies.

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2 2.1

Information technologies of the future

Possible applications General overview

Quantum computing might speed up data intense tasks. A few examples are autonomous driving; Google PageRank; weather models; economic forecasting; automated medical diagnosis and intelligence services(surveillance). These applications shall be explained in these chapter by discussing their performance requirements and at what state of the development they are. Most of the applications have something to do with the classification of certain data. Machine learning is very good at this but it may be time-consuming as a large data set is required to train. This is where the quantum part comes into play. By using superposition principles like in a quantum computer it can process the data faster and therefore learn quicker. In this chapter, firstly three possible applications will be discussed. Then, the current status of quantum machine learning will be elaborated further upon. Lastly, the choice for an application to focus on in this report is made.

2.2

Economic recession forecasting

In economics, a recession is a business cycle contraction when there is a general slowdown in economic activity[23]. Even though machine learning has achieved great successes in forecasting some market outcomes in the past, there hasnâ&#x20AC;&#x2122;t been much progress when it comes to financial markets. And among these financial problems, recession forecasting is one of the most crucial ones. Performance requirements: The first requirement is to have a suitable machine learning algorithm for this application. At present, k-Nearest Neighbour(KNN) classification method, which is also mentioned in chapter 1, has been studied for economic recession forecasting. KNN as a data mining technique can discover the patterns and relationships in data. Therefore better predictions of the future can be made[33]. A KNN classifier can examine the already existed financial cases, which are from the historical databases, such as companies and/or countries who have already undergone a financial crisis in the past. If the classification of a new sample is unknown, then it could be predicted by considering the classification of its nearest neighbour samples[33]. The second requirement is the powerful computation ability. One of the main problem with economic recession forecasting is that a small change in variables like policy or rumours can make predictions almost impossibly complex. Compared with the classical computers, the quantum computers can be more suitable for this case as the number of variables increase. The third requirement is a potential market for this application. In 2008 financial crisis, America alone lost 12.8 trillion dollars.[11]One can imagine how much money can be saved if economic recession was accurately predicted. If we can successfully design this recession forecasting machine, a lot of money can be saved, so the willingness to pay is assumed to be very high. The stage of development: The KNN algorithm has high complexity, because its two main processes: similarity computing and searching are time-consuming. Especially in the era of big data, the problem is prominent when the amount of data to be classified is large. Research has shown that by reaching the same results, the complexity of quantum algorithm is much lower than classical algorithms[44]. When predicting recession, the more variables can be taken into account, the better. And in this case, classical computers typically require exponentially more resources and power. However, IBM was able to prove that quantum computers need only a fixed number of steps to solve the problem, even as the number of inputs increases. This makes the quantum computation much more efficient than the classical counterpart. The more complex the problem becomes, the more efficient the quantum

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computed solution should be too[5].

2.3

Training of (deep) neural networks

In order to use a ‘simple’ neural network as depicted in the section 1.2.4, it must first be trained with a large data set. This means that the network itself processes a ton of data and adjusts the weight of its nodes to the best possible situation. The amount of layers the network has, plays a big role in the calculation time. In figure 1.6 the network has only one layer. When layers are added, the complexity increases as well, and so does the training time. Performance requirements First, a suitable implementation for quantum neural network training should be available. These algorithms have already been proposed in the literature, for example in [13]. The second requirement is that there is a quantum computer that is powerful enough available for the code to run on. This is further elaborated in section 2.5. A relatively simple task, like recognising handwriting, doesn’t need a lot of layers. However, for more complex tasks, results can be improved if more layers are added. In the future, the requirements put to neural networks will probably increase. An interesting example is the image recognition that Facebook is developing in order to be able to describe pictures for vision impaired people. To give an idea about the time needed to train a network like this: “Since a single machine would have taken more than a year to complete the model training, we created a way to distribute the task across up to 336 GPUs, shortening the total training time to just a few weeks.”[22] Also, a market to apply the technology must be available. Virtually all the big cloud service providers, like Azure, Google Cloud and AWS cloud can currently be used to train a neural network. These providers are very big and relatively cheap for ’normal’ calculations. Training neural networks in the classical way basically boils down to solving many linear algebraic variables, which can be done quite quick by the servers. For quantum machine learning, we identify a market that works in the same way as the current market: doing the computations for clients in order to train their neural networks. Besides, quantum neural networks could provide a huge benefit when the networks even get bigger, caused by the exponential speedup. This could mean that experimenting with different configurations of neural networks could get easier and quicker, and by doing this, companies could experimentally select the best neural network for their business case. Rapid prototyping could get cheaper and easier. The stage of development of the algorithms Currently, there is much development in the field of quantum algorithms that are able to run or train a neural network. One of the already mentioned examples is [13]. Also, while previous algorithms were purely theoretical, currently more testing on IBM’s Q platform is happening as is shown in [37]. Yet, no algorithms have been tested on a large scale, which is also due to the fact that there are no large quantum computers available with many qubits. However, seeing the current developments, we expect that in the near future more large scale testing will be done and that the algorithms will be optimised.

2.4

Mass surveillance

With the use of machine learning current security systems can be enhanced. For example CCTV systems are used to monitor the security of cities or buildings. The data is mostly processed by security people watching the video feed. People get tired, make mistakes and don’t have a perfect memory. Using machine learning techniques such as CNN (convolution neural networks) it is possible to analyse the video feed using

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computers, which can greatly increase the efficiency of security surveillance. These neural networks can be trained to detect objects in the image, such as humans, bikes, cars and basically everything it has been trained on. This training takes a lot of time and increased in length when more complex networks are used and a bigger data set is used. Facial recognition can also play a big role in a better surveillance system, where a database of suspects can be cross-correlated with the detected faces. More complex networks can also be used to estimate the movements of people in the video. This can already be done by using classical computers as can be seen in [31], where the pose of a person can be tracked. An example where this is done with a drone and a camera is [36] where the spines of a large group of people are tracked. This makes it possible to detect certain behaviours such as fighting or panic and allows for instance police to quickly get noticed and go the situation. By combining the pose estimation and facial recognition allows for a lot of features to recognise and classify a person on the street. This allows us to quickly find a suspect and track him around a city. Performance requirements This system could also be deployed by using classical computers but as the number of data increases and the neural networks gets more complex the parallel execution of quantum computers can make this possible in real-time. The current state of quantum computers is not yet ready be implemented on a large neural network because of the lack of qubits. The algorithms themselves to train these networks are still in the early stages and will improve over time. As the quantum computers are still expensive they will mostly be used for training as this is where probably the most speedup will happen. The quantum computers will have to be faster than the classical computer, but this is not as simple as it sounds as the speedup and usefulness depend on the application and the used algorithms. The stage of development of the algorithms The algorithms used are based upon existing machine learning techniques but use quantum computers to speed up certain elements of these algorithms as discussed in chapter 1. Mainly training of the neural networks and the classification of features will be sped up using quantum computers. Current algorithms are mostly tested on small scale quantum computers or quantum simulators. Large scale quantum computers will suffer from more noise as more gates will be used, it has to be tested if these algorithms also will work on larger scale quantum computers. For the training of neural networks, it will be likely to be quite resistant to the noise as a gradient descent method is used to find the optimal weights of the network and there is not one solution to this. If there is a small error in the gradient it might converge a bit slower but in the end, the solution will converge if the error is not too big. This will reduce the need for error correcting qubits. As this solution can also be checked using a classical computer, the noise in the system might not be as problematic as for situation where an exact solution is needed or a more chaotic system is modelled such as weather models.

2.5

Current status of FIT

Currently, quantum computers are still very slow and on a very small scale. However, there are some problems where quantum computers already nearly outperform normal computers, namely sampling, which is like tossing a coin, except that each outcome isnâ&#x20AC;&#x2122;t a single side, but a string of values that might be influenced by some (or all) of the other values in the string[7]. Computer scientist Scott Aarenson and his doctoral student Alex Arkhipov suggested that in these kinds of problems, quantum computers might already be faster when 30 qubits could be utilised[1]. This is nearly reached, as for example, the IBM Q Tokio utilises 20 qubits. However, to be able to quantify the actual computational power of quantum computers, not only the qubits matter. Where the ability of classical (super)computers can be compared with the amount of floating point operations per second, this makes no sense for quantum computing because their computations are fundamentally different. A measurement scale was proposed by IBM[10], that measures the computational 14

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power of quantum computers in a so-called quantum volume. A sentence from this paper explains the quantum volume pretty well: “Quantum computing systems with high-fidelity operations, high connectivity, large calibrated gate sets, and circuit rewriting toolchains are expected to have higher quantum volumes.”[10] Instability of the system empirically reduces the score. The current state of the art quantum computers is scoring up to an 8 on this scale[10]. In an IBM research blog, the researchers estimate that “To achieve Quantum Advantage in the 2020s, we need to at least double Quantum Volume every year”[16]. The researchers at IBM have plotted the currently developed quantum computers on a time and quantum volume scale, which is displayed in figure 2.1.

Figure 2.1: Quantum volume against time. Researchers have proposed a scale to measure the computational power of quantum computers, namely quantum volume. An exponential relation can be found between time and quantum volume. Plotting the y-axis on log scale creates a straight line through the points[16]. It would be interesting to compare this graph to the classical Moore’s law, but this comparison is unfair due to the nature of the calculations being fundamentally different in quantum computers. Some researchers have tried to make the comparison between quantum computers: “We argue that while chaotic states are extremely sensitive to errors, quantum supremacy can be achieved in the near-term with approximately fifty superconducting qubits.”[8] An article in Nature summarises the current development in quantum supremacy very well: “Thus the early stages of future quantum-supremacy experiments are likely to be characterised by an iterative process in which the proposed experiments are challenged by efficient classical simulations. Nevertheless, given the speed of recent experimental developments, it seems plausible that quantum supremacy could be convincingly demonstrated in a matter of years.”[17]

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2.6

Information technologies of the future

The chosen application: mass surveillance for safety enhancement

While we think economic forecasting is an interesting application of quantum machine learning, it doesn’t come as close to us as civilians than mass surveillance. To some degree, we all are experiencing some form of mass surveillance. Of course, in most of the countries, this is still only used for finding criminals, and we hope that it stays this way without wanting to control the population. However, times change and so might governments. It is very interesting what could happen when mass surveillance networks are combined with machine learning and used on a big scale to enhance safety. So, the technology and the ethical sides of mass surveillance with powerful quantum machine learning behind it, are very interesting, and therefore we chose this as our application. However, we realised that ‘mass surveillance’ on its own is very broad. Therefore we tailor the application to ‘Mass surveillance for safety enhancement’, focussing on using mass surveillance for the greater good.

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Competing technologies

3.1

Overview of chapter

We defined the main three functions of surveillance for safety enhancement as: 1. localising wanted people (face recognition); 2. predicting threatening people (pose estimation, compare historical data) and 3. safety surveillance in real time(notify police). In this chapter, comparable competing technologies for each of these three functions will be described. The focus of our application is mainly ‘physical’ criminals and crimes. This means that, for example, fraud and banking crimes won’t be included here. The competing technologies shall be divided in technologies involving other approaches to machine learning and to different some more classical ways of criminal search.

3.2

Algorithms written by humans

Facial recognition in order to localise criminals, for example, is already being done. The first working algorithms were written by humans without using machine learning[41]. Here distances between facial features were measured and rotated mathematically if a person was in a different pose compared to the database. This has the advantage that the classification can be done by features that logically make sense. For example, if biological research shows that the distance between the centre of the eyes is unique for every person the programmer can incorporate this as classification criteria. With machine learning these criteria are a bit more arbitrary and more based on probability. If from a lot of photo’s of a person, a certain feature seems prominent, the algorithm will use this for classification. Another point which applies to all algorithms which do not use any quantum mechanics is the inefficient database search. The only way of identifying a person on the street is by comparing him to every data-point in the database. Of course, there are classically some ways of structuring the data. In other words, putting groups with similar features already together and then using a decision tree in order to classify. The advantage of quantum machine learning is here mainly visible in the speed √ aspect again. As discussed in the theory, a quantum computer finds the desired result proportional to N instead of N. Furthermore, machine learning algorithms are constantly improving and evaluating automatically. An algorithm made by humans will just be a finished product after the code is typed. This is an advantage which machine learning has over human written algorithms.

3.3

Machine learning methods on classical computers

Big competitors are Google, Amazon and Facebook as they all have advanced software to process images and recognise the content. For example, Amazon recognition (https://aws.amazon.com/rekognition/) where a cloud-based system allows users to easily upload content and have it identified by Amazon. Things that can be analysed include what kind of things are on the image/video and recognizing faces. The cost of the amazon Recognition for video is on a per minute basis. Analysing 1 minute of video costs 10 cents and analysing 1 minute of the real-time(streaming) video cost 0,12 cents. For images it is roughly 1 dollar per 1000 images. They also allow trying out their service and process the first 1000 minutes of video free per month for the first year. This makes it easy for customers to try out the service and embed the API of Amazon in their application without much initial cost. They also allow storing the metadata of faces recognised in the process on their servers for 1.2 cents per 1000 faces[3]. Currently, this is not directly focused on security applications but can be easily adapted for this use case as can be seen in [4].

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In a sense, machine learning methods on classical computers are a competitive technique because the underlying structure and math are the same. The major advantage that quantum machine learning has over classical machine learning is faster training times of neural networks and classification where the processing speed is less dependent on the data size. So, it could be useful to combine classical and quantum computing. Initially, the time spend on a quantum computer will be quite costly compared to a classical computer. Therefore splitting up the tasks in jobs where it is really beneficial to use a quantum computer. This combination gives a big advantage in speed where necessary while still being able to take advantage of classical computers where beneficial. The â&#x20AC;&#x153;speedâ&#x20AC;? advantage will also result in a better-trained model allowing for less false positives in the situation as identification based upon facial recognition. In instances as locating a suspect, this can be a huge benefit as a false positive can have negative consequences for the person itself but also for finding the right person as it will delay the process of finding the actual suspect.

3.4 3.4.1

Competing to other forms of criminal search and terminate Police force

A study in Karachi, Pakistan, listed the traffic injuries data from both surveillance and the police. The main finding of the surveillance was the high number of deaths and injuries compared to the numbers reported by police, the official source of road traffic injuries and deaths. This study showed that at best, only half of all road traffic injury deaths and only 2-3 percent of non-fatal injuries are reflected in the police records. This major difference indicates that surveillance performs much better in quantity than police force[29]. Also, the police force cannot monitor every corner of the country. Instead, they start to investigate a crime as soon as a criminal complaint is stated. However, by showing up to catch the criminals or stop the fight, the police can stop the crime and mitigate the damage effectively. In conclusion, police force and surveillance both have its strength and weaknesses. With the widespread of street cameras, less police force will be needed for patrol. But this does not mean the police can be completely replaced by the computer. The policeman is still needed to deal with the crime scene and arrest criminals.

3.4.2

Location trackers

GPS tracking is a mature technology, which lets officers monitor offenders without actually keeping them inside the prison. Due to the high costs, some states in America are using a GPS system to track prisoners as an alternative to keeping them locked up. The location tracker allows authorities to monitor the criminals at all times without the effort to keep them in the cell. It is believed that assigning a criminal, especially the sex offender, a location tracker after release from jail may prevent him from committing in future crimes. The criminal will know he is being watched, and will likely realise that another illegal act will lead to a permanent arrest[25]. Currently, this technology only monitors the convicts on records and cannot detect new criminals. However, it is not impossible to apply the location trackers to everyone in society. In this case, all locations can be seen, but it is still impossible to see where and when a crime happens. When combined with security footage with an exact time, the concerning person can be found. However, having a physical location tracker makes the public very aware of the tracking of their location and might cause public uproar.

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Information technologies of the future

Paying civilians to watch other people

Big internet companies like Google or Facebook all offer a job called content reviewer. People who do this job need to go through the website and identifies contents like videos or feeds that need to be removed or modified[20]. This can prevent violent or pornographic contents spreading on the internet. Similar to this, we can also hire normal civilians to watch the public. This might be a cheap solution to having very big computer systems. However, the people who have to do this job will be heavily burdened by the crimes they have to watch and report. Also, in order to watch many cameras accurately, many employees are needed, as it is hard for a human to multitask and watch multiple camera streams.

3.5

Competing mass surveillance business models

Mass surveillance business models that exist now often have something to do with trading customers information for some service or discounts. Facebook could be seen as an example of mass digital surveillance, used for good purposes. With Facebook, for example, you can get free access to the app but in exchange for that, it will collect data about your behaviour in order to send you more targeted ads. So the money doesnâ&#x20AC;&#x2122;t come from the actual users but from the advertisers paying Facebook to place an add on their website. Also, Facebook has the advantage that if most of your friends use it you will probably also start using it because of the social communication that is going through this platform. Actually Facebook hides their digital surveillance very well, but still manage to make money out of the collected data. Also supermarkets apply some kind of mass surveillance techniques. For example customers can get a discount cards but in exchange for these discounts the supermarket will register all purchases a certain customer makes. This information can then be used in order to improve prices and see which products are popular and why. Some businesses also just collect the data and donâ&#x20AC;&#x2122;t use it to advertise on their own website. Instead they sell it to other companies so they can place targeted adds. So in their business model they collect the data by providing a valuable service to the customers and make profiles for the different customers. This information will then be sold to a different company which will probably use it to sent targeted adds. However, little examples can be found of using mass surveillance as a safety tool. Currently one can buy a small piece of equipment to enhance safety, for example a camera, but that is to no extent comparable to quantum mass surveillance. Something that might be a little bit comparable are security companies that offer services for a fixed price per month to guard some specific object. Also, currently mass surveillance is mainly done by governments, who donâ&#x20AC;&#x2122;t have to have a business model for it.

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Our application

In this chapter, our application is described. We elaborate on the process of bringing the application to the market. First, the business canvas with explanation is presented. Then, the technology is more deeply described. Lastly, in section 4.4, the technological developments needed for market readiness are described.

4.1

Business model canvas

In order to have an overview of the business side of mass surveillance for the greater good, a business canvas was filled in. The business canvas is displayed in figure 4.1, explanation of the subheadings are given in the sub chapters below.

Figure 4.1: Business model canvas 4.1.1

Key partners

The key partners can be public transport operators, banks and mobile phone providers. These parties have much data about the people using the services they offer. These data cannot be obtained by cooperation but can be used to track the subjects in the mass surveillance system. It is very important to convince the government of the safety enhancement aspect of mass surveillance. If the government is convinced, the law can force the partners to supply their data â&#x20AC;&#x2DC;for the greater goodâ&#x20AC;&#x2122;. Also, providing data to our system gives the partners possibly a better system, because our system will be able to detect more clues from the data than current systems because of our high tech systems. As mass surveillance costs much computational time, it is good to set up our own quantum computer instead of buying computational time on otherâ&#x20AC;&#x2122;s quantum computers. Potential future quantum computer vendors, such as IBM or Google might be able to offer this hardware. Another reason for buying our own computer

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is privacy issues. With our own hardware and knowledge, all the information of the mass surveillance stays in the house and is not transferred and processed by a third party. 4.1.2

Key activities

In order to be able to analyse large amounts of data from surveillance systems for our customers, various important other activities must happen. Implementation of the system for the customers is one of these activities. A good implementation helps with safely transferring and analysing data from the customers. Other key activities are maintenance of hardware to guarantee an as high as possible up time, development of software to ensure that we stay at the front of quantum surveillance technology development. Also maintaining and building customer relations are very important key activities.

4.1.3

Key resources

In order to start a company like this, there are two very important requirements: the quantum hardware should be available, as well as the knowledge to create the software in order to perform mass surveillance. Currently, these resources are not yet available. A very important third resource is knowledge about security. As the data that will be processed by our system is very privacy-sensitive, the security should be top notch. A security break would majorly impact the trustworthiness of our company. It is therefore very important to also invest in this. Furthermore, there are a couple of secondary resources like knowledge to operate and maintain the hardware and software, managerial personnel and some initial money to be able to afford the beginnings of the company. On the software side, machine learning experts should collaborate with behavioural specialists. Together algorithms to detect threatening behaviour should be developed and enhanced to prevent false positives and to create an as high as possible hit-rate.

4.1.4

Value proposition

The three main activities of the software are localising wanted people, predicting threatening people and real-time surveillance. Localising wanted people, for example, can be used when a serious crime has happened and the suspect should be found. Predicting threatening people can be used to notify police officers in advance when someone is showing alarming behaviour. This technique may also be used in crowd control. If somewhere in a large event things tend to go wrong, a quick reaction is possible with this system to prevent very bad accidents. Real-time surveillance notices the police if something is stolen or if a law has been violated. This can directly be used in combination with localising wanted people: the people that have made the violation can directly be tracked to arrest them. We do this faster and better than currently available organisations due to our access to quantum computers. The current product that we envision is a plan with a price that depends on the amount of data that is analysed. The price can be adapted to the data flow per time unit. All the implementation are tailored to the wishes of the customers, together with our specialists. We also add value in having the total hardware on our side, which makes sure that no third party data falls in the hands of another party.

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Information technologies of the future

Customer relations

Establishing relationships will be relatively easy if we are first movers and start building our relationships early enough. In order to ensure long-term relationships, it is very important to build trust. This means no data leaks, which translates into a high level of security. This costs money, but will reward itself in the long term. The customer relations should be very transparent of nature. We give the customer insight in our key activities, cost structures etc. This is another mechanism to build trust and a good relationship. Reaching the customers (governments for example) might be quite difficult. It is therefore important to try to attract as many employees that have connections in governmental circles as possible.

4.1.6

Channels

In regards to channels, it is firstly important to convey a professional message. Words with a negative association like â&#x20AC;&#x2DC;spying on all civiliansâ&#x20AC;&#x2122; are not used, but the emphasis lies on the positive side of mass surveillance: enhancing safety for the greater good. Of course, contact with clients must happen in a secure and safe way. Therefore we want to use a direct peer to peer connection, possibly also physically disconnected from the internet to prevent safety breaks. A possible marketing channel is offering a pilot together. We will offer a (local) government a very cheap deal to see if our system is suitable for their needs. After a short piloting period, normal prices will be calculated.

4.1.7

Customer segments

The most import customers are governments that want to enhance safety in their country. The current ongoing trend of using more and more data to see where things are going wrong. We are tuning in on this trend by offering a comprehensive monitoring solution. The first customer segments that we want to address are the local governments, for city protection for example. These systems are not extremely big and provide a good starting point. When these tests go well, up-scaling to a country or even a continent is possible. 4.1.8

Cost structure

There are two important cost sources, the first one being the maintenance and acquisition of new and up to date quantum hardware. It is currently very unclear what these costs are going to be. This totally depends on how the technique will continue to develop. However, to make an estimate, in 2017, D-Wave, the creator of quantum annealing computers sold its first computer to a company for 15 million dollars [9] which is roughly equivalent to 15 million euros. To start of, one quantum computer must be bought. This means an initial investment of 15 million euros. A building can be rented. Data centres, for example, provide goods and secure facilities to use for our quantum computers. Based on our renting experience, we estimate a renting price of 100000 euros per year. In comparison to 15 million euros, 100000 euros is quite small. If the rental price would double, this still wouldnâ&#x20AC;&#x2122;t be a big deal. With 10 software engineers and 5 maintenance engineers, enough development is probably possible to stay on the edge of surveillance technology. The salary of high qualified personnel is around 100000 euros per year. Add some other staff like management (2 persons) and supportive personnel, the combined yearly expenses on salary are about 2.5 million euros. Additionally some small costs will be present, like operational costs and PR costs. These might account to about 10000 euro yearly. The expenses are summed up in tabular form in table 4.1.

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4.1.9

Information technologies of the future

Revenue streams

It is not clear how much the governments want to pay for this. As one could imagine, companies that do these sort of things do not state prices or whatsoever. These are very application specific and will be discussed with each customer. However, the safety of the public is usually one of the largest concerns of the government. With the recent increasing amounts of attacks with terrorist motives and polarisation happening in many cities, demand for systems like these will only increase. Because of these reasons, we feel confident that governments will pay for our service. As a starting point, the prices of Amazons surveillance are taken, which is about 0.10$ per minute for a full HD stream. In order to give us competitive advantage, we want to be cheaper for our basic service. This lead to the following price plans: • Identification/localisation: e0.05 per minute, cheaper than Amazon • Real-time surveillance: e0.10 per minute, cheaper than having officers everywhere • Predicting threatening behaviour: e0.15 per minute. When customers decide that they want a combination of services, also the following bundles will be available: • Identification + real-time surveillance: e0.13 per minute • Everything together: e0.25 per minute All the listed prices apply to one full HD stream.

4.2

Financial overview

The costs are listed in table 4.1. Table 4.1: Overview of costs Fixed costs Quantum computer Building rent Variable costs (per year) Salary of personnel Additional cost (PR, cleaning etc) Total costs first year

Prices in million e 15 0.1

2 0.5 17.6

In order to calculate when the break-even point is reached, it is interesting to calculate the amount of cameras needed in order to reach a revenue stream of 17.6 million euros. To do this calculation, first the yearly revenue per camera is calculated. We assume that a customer pays on average e 0.20 per stream, and has their cameras streaming 24/7. Then, yearly revenue from 1 camera is equal to revenue = 0.20 · nminutes = 0.15 · 60 · 24 · 365 = e105120/year.

(4)

The total amount of cameras needed to break even is then calculated by ncameras =

17700000 costsyearly = = 168 cameras. revenueyearly,camera 105120

(5)

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This includes the assumption that the investment on the quantum computer must be earned back in one year, however, this could as well be a long-term investment that can be earned back in a couple of years. Then the needed amount of cameras to break-even is even lower. If we assume that a full HD camera generates about 1.5 GB on data per hour, 0.41 MB should be stored per second. This equals about 3360000 bits per second. For 168 cameras, 168 times 3360000 = 564 million bits per second of information are received. Taking the log2 of this number tells that 29 qubits could contain this information.

4.3 4.3.1

Description of the technology Recognition

In section 3.1, three main functions are stated which our product will be able to execute. The first one (localising wanted people) is done using face recognition. This application can be used when the police already know what a certain suspect looks like and they want to find him/her. They could have gotten this information for example from an eye witness. Using quantum mechanics, a KNN algorithm will be developed by us in order to full fill this task. First, all available data from the video cameras shall be put in a superposition state. To be more precise, the facial feature of the people (biometrics) and their location and time they were filmed are the data points in this case. If one would graph this data all people with similar features would be close together in the vector space. This is then done for all relevant biometric features. The features of the wanted person can then be compared to the data points and assigned to a group of people who look alike for that specific feature. Then, the system searches for an overlap in the groups. For example, the distance between the eyes the suspect is classified to group A and for the length of their face to group B. Then we make a list of all people in both groups and the person that is in both groups is the one the police is looking for. When this is done for multiple features the person which has the highest frequency of occurring in the groups is most likely the wanted person. Because the facial features are linked to the location and time they were filmed it is possible to find out where they were at a certain time. If there would be a lot of cameras available this could actually be done in real time, and it would be possible to determine not only where they were 15 minutes ago but where they are actually right now. 4.3.2

Predicting behaviour

The possibility of tracking a lot of people in video footage makes it possible to analyse their behaviour. When enough overlapping cameras are used its possible to track individual persons throughout an airport for example. By using pose estimation of the individual person its possible to determine their activity at that moment, for example sitting, running or fighting. A contest to see who can build the best algorithm to calculate the pose is organised (https://posetrack.net/) and quite accurate results are already achieved using complex CNN [43]. The pose of the person can be compared to other already known poses to see what that person is doing, this can be done by comparing the movement of individual splines(bones) to known movements using a KNN algorithm. This can also be extended to see how this person is acting; aggressively, scared, focused or distracted. There are already companies focusing on these kinds of technologies for security and military purposes such as [39]. This may be quite hard sometimes as some poses such as scratching your head and waving somebody goodbye can be quite similar. The use of context can make this easier to differentiate. Human behaviour analysis of a single person can be interesting, but it more valuable to get an analysis of group behaviour and human interaction. Parameters such as the distance between people, where the person is looking at and velocities between the people in the group can be used to analyse what kind of group behaviour

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is present. This context can be used in a combination of individual behaviour to determine group activity. In case of fighting or panic, a request can be sent to nearby police forces in order to quickly control the situation. From a security standpoint, it is most interesting to know when the behaviour is deviating from normal behaviour. For every location, normal behaviour can be different. People queuing in line or people running to train when they are late result in two completely different but normal behaviours. A person peeking into different cars instead of walking to a car and get in the car is a sign that the person can be up to no good. Using unsupervised machine learning such as clustering using K-means algorithms, normal behaviour for certain places can be learned and deviating behaviour can be reported to a human supervisor who can take further actions. In some cases, signs of an event that is likely to happen can be predicted, such as fights outside a pub in a city where the behaviour can be linked to previous events where similar behaviour escalated into a fight. Although this is a problem with a social psychological side of it, machine learning has shown it can be used to tackle these more complex problems. In the beginning, most of the output of the computer will have to be checked by a human operator as false positives still might occur. As the system will get better more cameras can be used for less security personal increasing the efficiency of the security system.

4.4 4.4.1

Technological development needed for market readiness Amount of necessary qubits

It is not clear when a quantum computer is faster than a normal computer for our application, because it is still not sure how the machine learning algorithms will scale. To get an estimation of the difference in speed, Grovers search algorithm is compared to a classical search algorithm. The instructions needed to complete the search and the instructions per second that a classical or quantum computer can deliver are compared. For a estimate of the quantum computer, we use the IBM quantum computer with 5 qubits. It has a performance of about 5 · 106 ips (instructions per second) [15]. For comparison we took the fastest nvidia GPU (NVIDIA Tesla V100: 14 · 1012 ips)[24] and the the fastest super computer(2 · 1017 ) [40] now(2019) available. To calculate the number of elements in the search where quantum and classical computation time is approximately equal, the following equation is used: N =(

instruction/sclassic 2 ) . instruction/squantum

(6)

To calculate the amount of qubits necessary to store this amount of data in a superposition we can simple use log2 (N ) which results in qubits = log2 (N ). (7) Using these formulas and data gives us a rough comparison of needing a 71 qubit quantum computer to compete with the super computer and 43 qubits to compete with the GPU. However, these are only comparisons based on the computational power. For our application, the financial comparison is also very interesting, as quantum computations might be more expensive than normal calculations. 4.4.2

Obtaining data

As was stated before, our customers can be local governments. The municipality of Eindhoven is an interesting example. In 2013 it was stated by the local government that there probably are ten thousand’s of cameras in Eindhoven but surprisingly little of them are owned by the government, only 29[26]. There is a project however called ‘Camera in Beeld’ where shop owners, for example, can share their videos with the police. Currently, these are not connected to a police station in real time. They can only be accessed after the 25

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crime has already happened. However with adaptations, it should be possible to make the system real-time, just like all the available IP-cameras that can be watched on the internet. We believe that real-time data will start to become more important in the future. We think that either the municipalities will buy more cameras, or request live streams of the cameras of the â&#x20AC;&#x2DC;Camera in Beeldâ&#x20AC;&#x2122; project. 4.4.3

Decoherance

Decoherence is a very fundamental problem in a quantum computer. We cannot observe a system without inducing a disturbance in the system over which we have no control. In order to prevent this from happening we would have to nearly perfectly isolate the system from the outside world. If this is not done, there is no way of reliably storing and processing data. It is also necessary that the qubits interact strongly with each other in order to take full advantage of the quantum effects. But despite this isolation from the outside world, we still want to control the system from the outside in order to read out the qubits and find the result to the performed calculations. It is expected that problems like these can be solved by using highly entangled to create error correcting qubits. These states have the properties that if the environment interacts with the system it is not able to glimpse the encoded information and therefore it cannot damage it. Writing this information in a protected quantum state requires many additional qubits so a quantum computer using this principle will not be available in the near future. [28]

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Recommendations

In this chapter, firstly some financial recommendations are described. Then some privacy issues are addressed and thereafter some business model and security recommendations follow. As mentioned in chapter 4 the main technological developments that are crucial for market introduction were addressed. The first one is a working quantum computer that can run calculations for 168 cameras. The second crucial needed development is that the coherence of the quantum states needs to be fixed. To fund the developments that need to be done in order to reach market readiness, there are a few options. Firstly, a lot of fundamental research is already done by universities and research groups, this is basically just free knowledge. Furthermore, investors seeing the potential of this product will also contribute money in exchange for their share of the company. Governments also might be interested in subsidising our projects because they see the potential. It would even be possible to set-up an agreement where a few local governments would get our services for free once everything is up and running on the condition that at the start they provide us resources to continue our development. Mass surveillance has a lot of privacy implications. It is important that our marketing is on point to avoid getting an image of just spying on people for money. Instead, we want the general public to know for what purpose this data is used, catching criminals. It is therefore vital that we are completely transparent to the media about what data we store and how we process it. Although our customers are local governments and not normal people, it is still important that we get the public on our side. If the general public starts rioting against our technology no local government will probably use it. On May 25, 2018, also the GDPR (The General Data Protection Regulation) was agreed upon by the European Union. This applies to all companies doing business in the EU and storing personal information. GDPR puts biometrics in the sensitive category meaning it requires robust protection. [38] The law concerning biometric data is not the same however over the whole world. But most of the principles are very similar. Firstly, the business must have informed consent from its clients before collecting the data. Also, the companies have limited rights for disclosure of this information. In the state Illinois in the US for example, it is even forbidden to profit from biometric data, which is of course exactly what we are doing. In the Netherlands, the AVG (Algemene verordening gegevensbescherming) made an exception for this. The prohibition does not apply when the end goal is authentication or safety. [6] Also to gain more trust it is possible to first only collect real-time data and not store it. Then a few applications we intended to implement are not possible anymore, but the analysing of behaviour in real time is still possible. Unfortunately, then there will be no proof afterwards of what happened. However, the police can be notified significantly faster than when our technology is not deployed. This is a viable intermediate step between right now and our end product. In the beginning, our system will be connected to existing security camera system networks by creating a custom made software solution depending on their existing software system. This will involve converting all their footage to a standard format such as h264 or h265 and implementing a network to connect to our servers. As the data transferred and stored is very privacy-sensitive it is necessary to provide a safe way to transfer the data. The safest way would be a direct connection through glass fiber but a secured tunnel connection such as a VPN could also be a possibility if implemented well. As an alternative to storing all the data, the data could be processed real-time and only behaviour analysis could be done and match facial data with a list of wanted persons. If something suspicious is found it could be stored for evidence or verification by a human. Similar to Amazon Recognition the possibility to try our system for free will attract more customers as more developers of security systems can implement our system and customers can try it out without paying in the beginning. This is comparable to the online services added into smart TVâ&#x20AC;&#x2122;s where manufacturers implement the software necessary to use online services 27

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of third-party companies directly on their device.

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Conclusion

A quantum computer speeds up the calculations by doing them in parallel, contrary to a classical computer which can do only one at the time. When this is used for machine learning, an exponential speed up can be achieved. The main strength of machine learning is pattern recognition, so an application surrounding this theme was picked. To give our product also societal impact it was finally chosen to apply the technology for catching criminals by mass surveillance. The subfunctions of the proposed system are specified as: 1. Localising (wanted) people (face recognition) 2. Predicting threatening people (pose estimation, compare historical data) 3. Safety surveillance in real time (notify police) Using the KNN algorithm, wanted criminals can be localised with face recognition. This can be sped up because the data(biometrics) from all cameras are going to be put in a quantum superposition state, allowing for the performance of calculations on the entire data set. All filmed persons will be classified by facial features and the human being which has the most overlap in groups with the wanted criminal is probably the one the police is looking for. In order to turn this goal into a realisation, a quantum computer of 71 qubits has to be technologically possible. However, in this report the speed was calculated by comparing a supercomputer to the Grover search algorithm. If a neural network is used (as is needed for mass surveillance) this estimate is probably a bit too low. Also, collaboration with quantum hardware producers and surveillance camera manufacturers need to be made. The market that is interested in our product would be mainly local governments. Then our main competitors would be other traditional methods for catching criminals and of course also classical computers. The fixed costs have been estimated at 15 million euro and the variable cost at 2.5 million euro. In our business model, the most suitable business model is a pay per minute system. The identification/localisation feature will cost 0.05 euro/min. Real-time surveillance will cost 0.10 euro/min and the function of predicting threatening behaviour 0.15 euro/min. Also, 2 bundling packets will be put together in order to increase revenue. Identification and real-time surveillance will cost 0.13 euro/min and all functions together will be priced at 0.25 euro/min. In order to financially break even, a quantum computer should be able to analyze about 168 full HD streams simultaneously. The privacy issues concerning our services are present. Fortunately, it is allowed by Dutch law to use biometric data for safety purposes. However, transparent communication will still be our weapon of choice in order to build and maintain the trust of the general public. This is actually vital to success as most of the cameras in the city of Eindhoven are not owned by the local government. We need the data from these cameras and therefore we need them to be on our side. In conclusion; we think that mass surveillance is a promising application for quantum machine learning. Currently, the hardware and software developments are not on the necessary level. Seeing the recent developments in both the quantum computing field and in the machine learning field, we believe that in the (near) future, quantum mass surveillance might be possible.

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Appendix: Project organisation

A.1

Roles and responsibilities of the team members Table A.1: Roles and responsibilities Person Tim Broekema Robert de Bruijne

Sjors Buntinx

Guangyu Chen

A.2

Role and responsibility Responsible: Technical part: quantum; Presentations Other work: Business part Responsible: Business part: recommendations; agenda & meeting planning Other work: Technical part: possible applications; poster; presentation Responsible: Technical part: machine learning; Report setup & supervision Other work: Business part; Presentation work Responsible: Business part: business model Canvas; Poster + pitch; Feedback sessions Other work: Technical part: possible applications;

Planning of team meeting and weekly meeting with supervisors. Table A.2: Meeting schedule

Week number 6

Week number quartile 1

carnival holiday

Team meetings Mo: 9:30 – 12:30 Th: 13:30 – 17:30 Mo: 9:30 – 12:30 Th: 13:30 – 17:30 Mo: 9:30 – 12:30 Th: 13:30 – 17:30 Mo: 9:30 – 12:30 Th: 13:30 – 17:30

Supervision meetings Meet & greet with USE-S

Mo: Th: Mo: Th: Mo: Th: Mo: Th:

USE-S+Tech-S; Monday 10:00

9:30 – 12:30 13:30 – 17:30 9:30 – 12:30 13:30 – 17:30 9:30 – 12:30 13:30 – 17:30 9:30 – 12:30 13:30 – 17:30

Tech-S; Tuesday 17:00 USE-S+Tech-S; Monday 10:00 USE-S; Monday 10:00

USE-S; Monday 10:00 USE-S+Tech-S; Monday 10:00

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A.3

Information technologies of the future

Brief initial plan to resolve defined RQ(â&#x20AC;&#x2DC;s)

The main research questions proposed in the syllabus are divided in technical and business related questions. These question then are split up in sub-question which will give the final result when the answers to these questions are combined. To find an application we will mainly be investigating the current algorithms used in machine learning and see in which ones quantum mechanics will be significantly beneficial. Also we see possible applications when the actual input data is quantum mechanical. All of this will be found out by studying the literature given by the Tech-S, the sources in the bibliography these papers and also literature we found by ourselves. Furthermore, also the conversations with the Tech-S will be an information source. Technical sub-questions 1. How quantum mechanics can speed up certain problems (like pattern recognition) of machine learning? 2. What are the classical machine learning algorithm (mainly for pattern recognition)? 3. What are the main (dis)advantages respect to the classical counterpart? 4. What kind of improvements can be made using a quantum computer? 5. How much better will these improvements be (quantitative analysis)? 6. Which algorithms are not disturbed significantly by noise? 7. How many qubits will be necessary to deal with the amount of big data produced every year? Business sub-questions 1. What would be realistic investment if one is able to build such a quantum machine? 2. Is the best product a complete physical product or a digital product on subscription basis? 3. To what kind of market is the product useful? 4. How big is the market for quantum machine learning? 5. What would be willingness to pay (WTP) on average in this type of market for a quantum machine learning product? 6. How is competitive advantage ensured? 7. How long will it take to break even? 8. How should the intellectual property be protected?

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A.4

Information technologies of the future

Planning and intermediate deliverables for each of the team members Table A.3: Planning & intermediate deliverables

Week 2

Deliverables Sjors & Tim: Initial plan to resolve RQ’s; Guangyu & Robert: Planning and organizational stuff. Sjors & Tim: Technology overview with enough references; Guangyu & Robert: Start finding applications All: Brainstorm and possible applications; write an application each Robert & Sjors: Reasoning behind choice of application Tim & Guangyu: Presentation slides finished (see rubric) Sjors & Tim: Competing technologies Robert & Guangyu: Business Model Canvas Sjors & Tim: More detailed business Canvas Robert & Guangyu: Needed developments for market introduction Sjors & Tim: Technological development recommendations Robert & Guangyu: Proposal for suitable business model & goto market strategy Guangyu: Make a start on the poster Sjors: Executive summary

Deadlines (End of the week, Sunday 23:00) Monday: Appendix A (Robert & Guangyu responsible)

Chapter 1: Technology overview (Sjors & Tim responsible)

Feb. 27: intermediate presentation finished (Tim responsible) – Chapter 2: Possible applications (Sjors & Tim responsible)

Chapter 3: Competing technology (Robert & Guangyu)

Chapter 4: Your application 4 (Robert & Guangyu)

Chapter 5: Recommendations (Robert & Guangyu)

Wed. April 3: Poster & Pitch (Guangyu responsible) – Final report (all responsible)

Tim: Conclusions Robert: Introduction Guangyu: Poster & pitch All: checking; proofreading