The Academic Digital Radar by BTO by lhoft

BTO Academic Radar Focus on Banking

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1. BTO Overview

Research Agenda

2. BTO Academic Radar in a nutshell 3. Relevant topics in banking

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MISSION

THE BRIDGE BETWEEN ACADEMIA AND COMPANIES TO ACCELERATE DIGITAL REINVENTION

ACADEMIA

KNOWLEDGE

METHODOLOGIES

COMPANIES

LEARN DEEPER

TALENTS

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TRANSFORM FASTER

RUN BETTER

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KEY FACTORS

INDEPENDENT

ACADEMIC DRIVEN

BUSINESS ORIENTED

We are vendor-agnostic and this allow us to offer our Partners objective and independent information.

Over the years, we have developed a wide and global network of leading university professors that actively collaborate with our Research Team.

We are able to mix and match the best solutions to company specific issues and needs by leveraging its know-how and network, together with a constant consideration of the business values and the associated choices that are taken.

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ACADEMIC NETWORK UNITED STATES

01 ACADEMIC & BUSINESS LITERATURE REVIEW

02 INNOVATIVE STARTUPS

NYU Stern School of Business

Michigan State University

New York, UNITED STATES

Wien, UNITED STATES

Carlson School of Management

University of California, Irvine

Minneapolis, UNITED STATES

Irvine, UNITED STATES

University of Denver Denver, UNITED STATES

The design structure set by the BTO Research Center is enhanced by the link with the universities, by access to the most accredited research sources and by the link with the incubators and start-up accelerators

EUROPE Wirtschaftsuniversität Wien

Brunel University

Wien, AUSTRIA

London, UNITED KINGDOM

Ludwig Maximilians Universität

Imperial College London

Munchen, GERMANY

London, UNITED KINGDOM

Universidad Carlos III

Grenoble Ecole de Management

Madrid, SPAIN

Grenoble, FRANCE

University of Piraeus

University of Luxembourg

Piraeus, GREECE

Luxembourg City, LUXEMBOURG

EPFL – École polytechnique fédérale de Lausanne

Universität der Bundeswehr München

Lausanne, SWISS

Munchen, GERMANY

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AREAS AND RELATED SERVICES

RESEARCH

INSPIRATION

• •

Experience Program Education & Team Redesign

• •

Digital Sourcing Path Integrated Digital Enterprise Model

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CONSULTING

•

Project & Portfolio Management Process Redesign & Support

•

BTO OFFERING

OUR PARTNERS BANKING

FASHION

GAMING INSURANCE

OTHER INDUSTRIES

AUTOMOTIVE

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1. BTO Overview

Research Agenda

2. BTO Academic Radar in a nutshell 3. Relevant topics in banking

BTO ACADEMIC RADAR

GOAL AND SOURCES THE «BTO ACADEMIC RADAR» AIMS AT IDENTIFYING GLOBAL TECHNOLOGY TRENDS FROM AN ACADEMIC PERSPECTIVE. THE RELEVANCE OF TOPICS IS EVALUATED BY ACCESSING THREE TOP SOURCES OF ACADEMIC PUBLICATIONS: IEEE, ACM, AND SCIENCE DIRECT.

IEEE (Institute of Electrical and Electronics Engineers): it is the world’s largest association of technical professionals with more than 423,000 members in over 160 countries around the world. Its objectives are the educational and technical advancement of electrical and electronic engineering, telecommunications, computer engineering and allied disciplines.

Science Direct: it is a large database of scientific research. It hosts over 12 million pieces of content from 3,500 academic journals and 34,000 ebooks. The journals are grouped into four main sections: Physical Sciences and Engineering, Life Sciences, Health Sciences, and Social Sciences and Humanities.

ACM (Association for Computing Machinery): is an international learned society for computing. It was founded in 1947 and is the world's largest scientific and educational computing society. It’s a non-profit professional membership group, claiming nearly 100,000 student and professional members as of 2019. The ACM is an umbrella organization for academic and scholarly interests in computer science.

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BTO ACADEMIC RADAR

RESEARCH METHODOLOGY BTO FOLLOWS A RIGOROUS APPROACH FROM THE ANALYSIS OF THE DIFFERENT SOURCES TO THE APPLICATION OF OUR RANKING FRAMEWORK AND TO THE IDENTIFICATION OF TECHNOLOGY RELATED, RELEVANT USE-CASES & CASE STUDIES.

IDENTIFICATION OF RELEVANT EMERGING TECHNOLOGIES BY BTO EXPERTS

EXTRACTION OF RESULTS FROM SOURCES

PREPARATION OF SEARCH KEYWORDS CONSIDERING SEMANTIC EQUIVALENCES & DEFINITION OF THE TIMEFRAME OF ANALYSYS

CALCULATION OF THE WEIGHT ACCORDING TO RESULTS FROM ALL SOURCES

EXTRACTION OF RESULTS FROM SOURCES APPLYING INDUSTRY SPECIFIC FILTERS

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IDENTIFICAZION OF USECASES & CASE STUDIES

RANKING AND IDENTIFICATION OF TOP-TEN TECHNOLOGIES

BTO ACADEMIC RADAR

GLOBAL RELEVANCE

GENERATIVE ADVERSARIAL NETWORKS Ranking 9

AUTONOMOUS ROBOTICS & DRONES Ranking 8 BLOCKCHAIN Ranking 10

AUTONOMOUS VEHICLES Ranking 4 3D PRINTING Ranking 5 COMPUTER VISION Ranking 2 AUGMENTED WORLDS Ranking 6

NLP & SPEECH RECOGNITION Ranking 1

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5G Ranking 3

CLOUD EDGE Ranking 7

11Reference Timeframe: January 2019 – February 2020

BTO ACADEMIC RADAR

RELEVANCE FOR FINANCIAL SERVICES

OPEN DATA Ranking 9 OPEN API Ranking 7 CONVERSATIONAL UIs Ranking 5 COMPUTER VISION Ranking 4 COGNITIVE CYBERSECURITY Ranking 3

BLOCKCHAIN Ranking 8

RPA Ranking 2 NLP & SPEECH RECOGNITION Ranking 1 AUGMENTED WORLDS Ranking 6

QUANTUM COMPUTING Ranking 10

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12Reference Timeframe: January 2019 – February 2020

BTO ACADEMIC RADAR

EXPECTED WIDESPREAD ADOPTION OF THE TECHNOLOGY IN FINANCIAL SERVICE

Near future An adoption of this trend is recommended. Start initiatives and consider facets and implications of this trend on your business unit.

Mid term This trend will affect your business. But: Only those units acquainted with risk should activate resources for adequate initiatives.

BLOCKCHAIN Ranking 8 Business Technology Organization

Long term This trend might affect your business unit, but not at this point in time. Keep it on your watch list!

QUANTUM COMPUTING Ranking 10 13

1. BTO Overview

Research Agenda

2. BTO Academic Radar in a nutshell 3. Relevant topics in banking

NATURAL LANGUAGE PROCESSING & SPEECH RECOGNITION Natural Language Processing & Speech Recognition Natural Language Processing (NLP) allows for humancomputer interaction and enables machines to understand and act on the basis of natural-language content. Advances in this field are remarkably improving; for example, Microsoft's Skype Translator, which translates in real time from one spoken language to another, or Google's information cards that provide answers instead of a list of page links. For most enterprises, the most immediate use cases regard customer service and support to employees’ day-by-day activities

OPPORTUNITIES

RISKS

•

Bankers can make use of this technology to develop services helping customers understand their contracts (explain wordings, technical terms, ...)

•

As microphones need to be turned on permanently, large bulks of personal data are generated, which will increase demand for cyber coverage

•

Data collected through speech recognition can be leveraged for other business purposes

•

Speech Recognition (SR) can significantly accelerate paperwork since documentation can be handled much quicker, leading to higher efficiency

Unauthorized use of NLP & SR devices can lead to the contracts signatures or products purchases without the legal prerequisites being fulfilled (e.g. children or people who are not legally capable might conduct transactions)

•

Technological providers must ensure that user commands are not misinterpreted and therefore translate into mistaken transactions

RELATED TOPICS Deep Learning Smart Bots Hardware-embedded AI Conversational Interfaces

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Source: based upon BTO’s internal and secondary sources

NATURAL LANGUAGE PROCESSING & SPEECH RECOGNITION Facts

75% of large banks (+ $100 Bn in assets) are currently planning to deploy AI-enabled solutions

Using NLP in the sales area reduces the average engagement time of sales employees by 30%

NLP market will grow from $7.63 Bn in 2016 to $16.07 Bn in 2021 with a CAGR of 16.1%

Business value RETENTION

A higher response improve customer satisfaction, retention, thus generating a revenue uplift

INVESTMENT ANALYSIS

Document analysis and ‘listening’ to analyst calls to determine the sentiment of company management, which can provide insights for equity analysis Business Technology Organization

PAPER SEARCH

Extracting key data and clauses among large volumes of documents to help loan officers review commercial agreements

Source: based upon BTO’s internal and secondary sources

NATURAL LANGUAGE PROCESSING & SPEECH RECOGNITION

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Source: based upon BTO’s internal and secondary sources

Get to know Lingua Custodia Olivier Debeugny, CEO

Company Track Record Cutting-edge technology regularly rewarded and recognised by our customers

Founded by 2 Finance Professionals

2011

Release of 2 first products for financial domain translations

2014

Integration of Neural Machine Translation technology for faster and deeper learning

2015

Labelled by the French Finance Innovation cluster

2016

Founding member of France Fintech Association

Roll out of first Asian languages Commercial lift off and global brand building

2017

Elected Most Innovative Fintech of the Year

Extensive list of clients: AXA, BNP Paribas, HSBC, CrĂŠdit Agricole, Rothschild, Sycomore, Idinvest, ...

2018

Only French FinTech selected by the Monetary Authority of Singapore

Opening of Lingua Custodia first international branch in Luxembourg

2019

Opening of Lingua Custodia 3rd office in the center of Paris

2020

Fintech Solution of Silver Medal at the Selected by JIAM for the Year at the Fintech Awards its FinTech European Finance Luxembourg Showroom in Tokyo Summit Luxembourg

Translation Why and how? 2 main reasons for translating documents

To understand

To communicate

3 ways to perform financial translations

Translation agencies

In-house team

Do it yourself

Translation 3 clear challenges: speed, cost, quality 2 main reasons for translating documents

3 challenges usually encountered

15 man-days for an Annual Report

To understand

To communicate

Slow speed

Up to â&#x201A;Ź0,30 per word

Lack of expertise for technical texts

High cost

Variable quality

Machine Learning The Translation Tech solution 2 main reasons for translating documents

To understand

To communicate

Specialised Machine Translation or Post-editing

Quick process

Cost efficient

Homogeneous quality

Our solution 100% Finance

Specialised

By financial document type

Customised

By client team if necessary

Secured

No cloud! Dedicated server

Format friendly

30+ document formats supported: Word, Ppt, Excel, Indesign, Pdf

100% Finance

Our Machine Translation engines are fed by the specific terminology of the financial industry. Machine Translation Each Machine Translation engine is “trained” on a focused set of linguistic data, thoroughly selected and prepared for training by our Lab and Data teams.

Post-editing option LC Annual Report-IFRS

LC Equity research

LC Macro Research

LC Asset Management Marketing

LC Finance (news)

LC Prospectus-KIIDS

Document is translated by a specialised engine.

LC Corporate Actions

LC Fund Factsheet

LC Regulatory

It is reviewed and proof-read by a native financial professional translator.

LC Life Insurance

… More to come in 2021

Selecting the right engine for the right document is instrumental to the performance of the machine translation process.

Natural Language Processing

Machine Translation

Question Answering

Text summarization

Challenges in Machine Translation (1/2) Gender Bias

1 Source

The doctor asked the nurse to help her in the procedure.

Machine Translation

Le médecin a demandé à l'infirmière de l'aider dans la procédure.

Long-term dependencies issue

2 Source

My car is missing. It was stolen this morning.

Machine Translation

Ma voiture a disparu. Il a été volé ce matin.

Challenges in Machine Translation (2/2) Terminology Control

3 Source

<Name of the Company> Sélection Dynamique PEA Objectif de gestion : De classification Actions internationales, le FCP a pour objectif, sur un horizon d'investissement de 5 ans minimum, la recherche de la meilleure performance (nette de frais) en ayant une exposition moyenne sur les marchés d'actions européens de 80%.

Generic Engine 1

<Name of the Company> Dynamic Selection PEA Management objective: With the International Equity classification, the FCP's objective, over an investment horizon of at least 5 years, is to seek the best performance (net of fees) by having an average exposure to the European equity markets of 80%.

Generic Engine 2

<Name of the Company> Dynamic Selection PEA Management objective: With a classification of international equities, the objective of the FCP is, over a minimum investment horizon of 5 years, to seek the best performance (net of fees) by having an average exposure to European equity markets of 80%.

Lingua Custodia Engine

<Name of the Company> Sélection Dynamique PEA Investment objective: Classified as an International Equities fund, the Fund's objective, over a minimum investment horizon of 5 years, is to seek the best performance (net of fees) by having an average exposure to European equity markets of 80%.

+ +

Name of the Fund not translated Accuracy of financial terminology Understanding of financial acronyms

Terminology Control - Research Objectives — Comparative study of various Neural Machine Translation (NMT) models with Terminology Control published in the NLP Research field

— Quality assessment: BLEU Score + Human Evaluation — Push the best model into production

Transformer + Terminology Control

— Publish our results in International NLP Conferences, e.g. ACL (Association for Computational Linguistics), EMNLP (Empirical Methods in Natural Language Processing), …

Lingua Custodia: LE LAB — SOULT Research Project in collaboration with the LIMSI-CNRS and the « Direction Générale de l’Armement (DGA) »

— SOULT = Supervision Optimale, Utilisation de Lexiques et Terminologie (en Traduction Automatique Neuronale) • • •

Terminology Control (sentence vs document level) Terminology consistency and long-term dependencies at document level New strategies of quality evaluation

—

France +33 1 80 82 59 70 Luxembourg +352 2 786 76 11

contact@linguacustodia.com

www.linguacustodia.finance

Lingua Custodia

BLOCKCHAIN Blockchain The blockchain is a subfamily of technologies in which the register is structured as a chain of blocks containing transactions and whose validation is entrusted to a consensus mechanism, distributed on all nodes of the network (permissionless or public blockchains) or on all nodes the nodes that are authorized to participate in the transaction validation process to be included in the register (permissioned or private). The main characteristics of blockchain technologies are the immutability of the register, transparency, traceability of transactions and security based on cryptographic techniques

OPPORTUNITIES •

The system and data are resistant to errors and attacks: each node in the network can replicate and archive a copy of the database and a single node that goes offline does not affect the security of the entire network

•

Once the blocks have been registered in the blockchain, the data is extremely difficult to remove or modify; this makes it an ideal technology for keeping financial records

•

RELATED TOPICS Cybersecurity Distributed Ledger Risk Management Bitcoin

RISKS

A blockchain system denies the risk associated with the need to trust a single organization, reduces commissions and overall costs by eliminating intermediaries and third parties

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•

There are potential attacks that can affect blockchain networks such as 51% attacks: it could occur if a single entity manages to take control of more than 50% of the computing power of the network, managing to alter the network by modifying the order of transactions

•

Once a data is added to the blockchain it is very difficult to modify it: although stability is one of the advantages of the blockchain, it is not always positive

•

Each blockchain account has two corresponding keys, one public and one private: if a user loses his private key, the funds are effectively lost

Source: based upon BTO’s internal and secondary sources

BLOCKCHAIN Facts

By the end of 2024, it’s expected that corporations will spend $20 billion per year for blockchain technical services

The financial sector currently accounts for more than 60% of blockchain’s worldwide market value

More than $500 billion in assets are being managed with Blockchain

Business value COST CUTTING

INCREASING TRANSPARENCY

By connecting data across the value chain, blockchain can provide transparency and realtime sharing, minimizing risk Business Technology Organization

FOCUS ON PRODUCTIVY

Blockchain enables process automation and the removal of intermediaries to help organizations improve productivity and performance 32

Source: based upon BTO’s internal and secondary sources

BLOCKCHAIN

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Source: based upon BTO’s internal and secondary sources

Today, just 11% of euro-area households own an investment fund and just 8% of household assets are invested in funds

Challenges •

Reach Retail investors

•

Engaging retail investors in buying mutual funds to meet their long term savings needs, beyond Pension scheme

•

Easy access to products

•

Easy access to investment products designed to meet shorter-term goals such as house purchases, education etc, will help to build engagement amongst a younger generation.

•

Compliancy

•

Asset managers have been encouraging smaller distributors to operate their client accounts on platforms, but the uncomfortable consequence has been growing ignorance of who is selling what to whom.

•

Digital competition

•

Technological developments are radically altering the way people communicate and interact with each other and, as consequence, the way people do business today.

New generation of “point of sales”

Investors needs

Placing Agent concerns

Asset Manager needs

•

Personalized offerings

•

Developing a customer-centric business model

•

No single view of client across enterprises

•

Keen to share data for better services

•

Simplifying the business and operating model

•

Limited ability to turn data into insights

•

better integration across physical and digital channels.

•

Obtaining an information advantage

•

Slash costs by simplifying legacy systems

Disconnected tools across sales, service and marketing

•

Provide investors with better products

Prepare the technical architecture to connect to anything, anywhere

Fund distribution chain reengineering

Centralizing Agent

Registrar Agent

A digitized fund distribution with a shared platform built upon blockchain technology can generate efficiencies, transparency and better User Experience

Interoperating with the ecosystem

A cross-boundary decentralized platform

No more messaging – Just data sharing in in real-time

• Investor access through an intermediary’s Front End the infrastructure and is directly operating into the Funds’ registrar • Any involved Intermediary (IFA, Distributor, Insurance, Platform, etc.) monitor and control Investors’ activitities on the shared system • The related Asset Manager and its service providers monitor, control and execute Investors and Distributors operations on the same shared system

Blockchain protocol – Consensys QUORUM

•

Quorum is an enterprise-focused version of

Ethereum •

Quorum is ideal for any application requiring high speed and high throughput processing of private transactions within a permissioned group of known participants •

Privacy and transparency : Quorum supports both transaction-level privacy and network-wide transparency, customizable to business requirements

•

Performance & throughput: Quorum supports high institutional transaction volumes

•

Permission & governance: Quorum supports blockchain transactions among a permissioned group of known participants

Performance figures achieved 600 transactions per second 25 million transactions on 4 days without break 39

QUANTUM COMPUTING Quantum Computing Instead of working in bits of 1 or 0, Quantum Computing (QC) utilizes the so called qubits (quantum bits) which hold all the possible results at the same time and can be linked into an entanglement with other qubits; the whole process increases the speed of calculation many times over in scenarios where conventional computers would take impossibly long to provide results; the main reason is the huge amount of data that are needed to be processed in order to generate an accurate decision. A quantum computer is often referred to as a supercomputer; It has the capability to handle highly complex algorithms and, thus, more complex and urgent problems RELATED TOPICS

OPPORTUNITIES

RISKS

•

A disruptive advance in computing power may jeopardize technologies like Blockchain, which rely on the limitations of current generation computers due to its tamper-proof characteristic

•

Current standards of data security will no longer subsist once quantum computing becomes available, particularly if the wrong parties win the race to develop the first machine

•

General purpose quantum computers will probably never be realized; rather, they can run a limited range of algorithms and will be dedicated to niche uses

•

Particularly helpful for training and teaching AI devices since it can handle large amounts of data in a short time. Furthermore, due to the higher efficiency, AI could learn from its own experience or correct itself once a false decision is likely to be made Nowadays banks employ AI to improve workflows due to the limitations of with traditional processes; QC’s combination with it might allow to create interfaces able to predict needs basing upon past data, current conversations and future needs

Smart Bots Blockchain Generated AI Neuromorphic Hardware

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Source: based upon BTO’s internal and secondary sources

QUANTUM COMPUTING Facts

The research phase is expected to end in 2020, followed by a tenyear commercialization phase

By 2023, 20% of organizations will allocate budget for projects, compared to 1% in 2018

Banks are constantly overloaded with huge volumes of transactions; with QC, response time will be close to null

Business value VALUE SIMULATIONS

COMPETITIVE ADVANTAGE

DATA SECURITY

QC will foster advances in cryptography and encryption and improve data security

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QC optimizes business efforts in machine learning, AI and neural networks, thus generating a competitive advantage

Source: based upon BTO’s internal and secondary sources

QUANTUM COMPUTING

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Source: based upon BTO’s internal and secondary sources

What is a Quantum Computer ?

A quantum computer is a machine that performs calculations based on the laws of quantum mechanics, which is the behavior of particles at the sub-atomic level.

IBM Q, Inside a Quantum Computer

Qubit signal amplifier

The march to aboslute zero 4 Kelvins Input microwave lines 800 milli Kelvins

Superconducting coaxial lines 100 milli Kelvins Cryogenic isolators enable qubit signals to go forward while preventing noise from comprominsing qubit quality

Mixing chamber 15 milli Kelvins

Quantum amplifiers, inside of a magnetic shield capture and amplify processor readout signals while minimizing noise

Quantum CPU, inside a shiled that protects it from electromagnetic radiation

Harnessing the power of a quantum processor requires maintaining constant temperature near absolute zero, and protect it from external electromagnetic radiation and physical vibration.

Around a Quantum Computer

Picture from : Andrew Long

What makes the difference ? • Superposition + • Entanglement = Power of a Quantum computer 2N states with N qubits 300 qubits can encode more states than atoms in the univers!

• No-copy theory • Measurement

https://www.youtube.com/watch?v=R6iWFmLC2-E

Multiple qubit technologies Most Quantum computer are based on A - Superconducting Josephson Junction B - Trapped ion

Graphic representations of two 5 qubits systems. (A) the superconducting qubits connected by microwave resonators (credit: IBM Research). (B) The linear chain of trapped ions connected by laser-mediated interactions (Honeywell). (A and B, Insets) Qubit connectivity graphs: (A) star shaped and (B) fully connected. Picture from : Science, Dec 2016, Vol 354, Issue 6316

Two Quantum Computer models Circuit Model – Universal Computer

• Digital • Logical Gates • Predictable behaviour at scale • “Mainstream” approach

• IBM, Google, Righetti, Intel, Microsoft, ATOS*

Adiabatic Model – Specialized Computer

• Analogic

• Math problem solved physically • Solutions are low energy states • Hard to predict behavior at scale

• No error correction • D-Wave, Fujitsu*, Toshiba*

NISQ (Noisy Intermediate-Scale Quantum) Computer (less then 1000 qubits) *emulators

Quantum hardware development is accelerating

Source: StrangeWorks

1000 qubits for 2023 (IBM)

New languages & way to solve problems • Using traditional language (Python, C/C++) or specific (Q#, Cirq, …) with additional libraries • Using graphical editor • Graphical interface to program Quantum processor online

• Hybrid mode : offload specific calculation from classical to quantum, like GPUs

4 steps for a typical QC program 1. Initialize the Quantum Computer (mostly with 0|>) 2. Encode the initial state 3. Execute the transformation / interference 4. Measure the result

Quantum Computer as a Service (QCaaS)

In addition, D-Wave, IBM, Rigetti, IonQ and QuTech offer stand-alone cloud access to their own machines. Today, IBM Q is available for testing for FREE on real Quantum Computers. There are 9 different back ends (8 physical QC, and 1 simulator).

Hardware Decoherence / instability / noise Almost zero-temperature for some architectures Protection from electrical noise, radiation, etc. Very, very difficult engineering problems

Issues

Software Error correction Millions of qubits needed for some applications Using Quantum in hybrid mode

Timing Real commercial applications probably far in time

Potential Applications

Short/Mid run •Computing / Simulation applications : in pharmaceutical research, material / chemistry science, energy, agrifood

Long run – Theoretically demonstrated •Cryptography (Shor) •Search problems (Grover)

Long run – Speculative •Machine learning •Faster training of neural networks

•Optimization problems •Solving linear systems with exponential speedup

Finance use cases

MACHINE LEARNING

SAMPLING MONTE CARLO

OPTIMIZATION

Algorithm

Application

• • •

Quantum Annealing Quantum Semi Definite Programming (QSDP) Quantum Approximate Optimization Algorithm (QAOA)

• • • • •

Portfolio optimization Arbitration Logistics and resource allocation Collateral management Market instability detection / crash prediction

• • •

Quantum Gibbs Sampling Quantum Hamiltonian simulation Quantum Phase estimation (QPE)

• • • •

Monte Carlo Methods Derivative pricing VaR/CVaR (Value at Risk) Risk analysis

Quantum linear Systems Algorithms (QLSA, HHL) Quantum Fourier Transform Quantum Feature Detection (QFD) Quantum Reinforcement Learning (QRL)

• • • • • •

Data modeling Market making Fraud / anomaly detection Customer behavior / sentiment analysis Prediction Credit risk estimation

• • • •

Finance use cases

Figures from: Sarah LAMOUDI, Experte IA & Quantique, #LamoudiS

Societe Generale use case The goal is to see what can be the limits and timeframe for using the Quantum Computer to do pricing (evaluation of a Quantum Monte Carlo algorithm).

There are 3 major parameters where Quantum Computers could bring value: • Randomness • Thinness of the log normal distribution • Speed

The outcomes: • Better quality of randomness by using dedicated QRNG device. Quantum randomness can be certified and is auditable.

• Greater Log Normal distribution: this is explained by the superposition property of quantum computing. We benefit here from its exponential power. • Speed: based on the actual number of qubits, the results are not significant

We have identified and materialize the real potential of the quantum computer in this domain of finance, but from our point of view we need more powerful machines, with thousand of qubits before having an advantage over classical solution. And using such solution is not obvious.

T a k e a w a y

• There are 2 kinds of Quantum Computer - Digital / Universal : IBM, Google, Microsoft, Rigetti, IonQ, Honeywell, simulators (Atos QML), … - Analogic / specialized : D-Wave, simulators (Fujitsu Digital Annealer, Toshiba), … • The first usable noisy quantum computer (NISQ) could be available in 3 to 5 years, where the error free quantum computer could be available in 10 to 20 years from now. • It is a very, very, very complex subject and a lot of engineering problems to solve • It is possible to take advantage from Quantum computing already today with Quantum Inspired Solutions • Solution taking advantage of quantum physics effects are available - Quantum Key Distribution (QKD) - Quantum Random Number Generator (QRNG) • Not all cryptography protocol are vulnerable, and cryptographic algorithm resistant to Quantum Supremacy are under standardization (post-quantum cryptography), it’s time to include quantum threats in the cybersecurity risk assessment

â&#x20AC;&#x153;I think I can safely say that nobody understands quantum mechanicsâ&#x20AC;?

Richard Feynman 1918-1988 Nobel Prize in Physics (quantum electrodynamics) in 1965

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