The ‘Quantum Computing in Financial Services’ report is an in-depth analysis of Quantum Computing and its applicability and impact on financial services. The report highlights key players in the ecosystem across hardware, software, and services, discusses the adoption of Quantum Computing by the financial services industry, and analyzes collaborative efforts exploring its early use cases in financial services.

1. REPORT
Quantum Computing in
Financial Services
July 2021

2. Introduction
2
Drawbacks of Traditional Computers and How Quantum Computing Can Fill the Gaps
Machines were created to ease the life of humans. From the invention of the wheel to the development of computers, humans have found ingenious
ways to make life easier. Charles Babbage, considered the ‘father of the computer,’ conceptualized and invented the first mechanical computer in the
early 19th century to facilitate navigation calculations. The principle of the modern computer was proposed by Alan Turing in his 1936 paper, On
Computable Numbers. Since then, computers have permeated all walks of life; a world without computers is now unimaginable. Supercomputers are
now used in computing-intensive tasks in numerous fields, including weather forecasting, climate research, and oil exploration. Artificial Intelligence
(AI), Machine Learning (ML), and cloud computing are some technologies that have helped us leverage the existing computing prowess of these
machines.
In classic computing, uncertainty is unacceptable. With quantum computers, however, it’s an asset as they have the unique ability to learn about the
world using probability; they explore multiple answers to arrive at complex decisions. Traditional computer science involves the flow and
manipulation of bits—the basic units of information in a computer—that can hold the value of 1 or 0 but not simultaneously. With quantum
computers, however, 1s and 0s give way to qubits or quantum bits as the fundamental building block of quantum information, experienced as a two-
state quantum mechanical system. The power of these qubits is their inherent ability to scale exponentially; a two-qubit machine allows for four
calculations simultaneously, a three-qubit machine allows for eight calculations, and a four-qubit machine performs 16 simultaneous calculations. As
the technology develops, Quantum Computing could lead to significant advances in numerous fields, from chemistry and materials science to nuclear
physics and financial services.
All this leads to the question of what is the future of computing? Although Quantum Computing can no longer be called the future of computing, as
we are already developing these computers, its applications and full potential will only be realized after several years. The often-discussed Moore's
Law, which predicted the development of robust computer systems with more transistors, is coming to an end because engineers are unable to
develop chips with smaller and more transistors. The creation of more powerful computers is regarded as the most crucial aspect of a computer
system. However, energy efficiency, device lifetime, and economic viability are just as important, requiring numerous transistors, especially when it
comes to large cloud data centers that power large portions of online web applications. These are among the many reasons that have forced
computer science engineers and corporations to look beyond traditional systems and toward areas such as Quantum Computing.

3. We have barely scratched the surface of Quantum Computing. New areas in the field such as
cloud-based Quantum Computing, allowing users to use these quantum-powered computers
through the internet, are focusing on making Quantum Computing more accessible. With the
limitations of traditional computing getting amplified and vast amounts of data being generated
every second, the rise of Quantum Computing systems to fill the gap is inevitable. Both
corporations and governments are focusing on developing Quantum Computing. India announced
a $1 billion fund for the National Mission on Quantum Technologies and Applications. In 2020, the
White House Office of Science and Technology Policy, the National Science Foundation, and the
Department of Energy in the US announced a $1 billion fund to establish 12 AI and quantum
information science research centers nationwide. Companies and governments are working
together to lead the next wave of innovations in computing systems. Innovations and higher
processing power need to ensure that these systems can solve real-world problems.
Outlook

4. Over the past fifty years, computers have become faster, smaller, and more powerful. They have been
transforming and impacting our society in innumerable ways. But like any exponential explosion of resources,
this growth, as described by Moore’s Law, must soon come to an end. Gordon Moore, the co-founder of Intel
Corporation, found that the number of transistors on a silicon chip doubled every year. In his paper in Electronics,
he proposed that this rate of growth would continue, later revising this to a more conservative doubling every 2
years in 1975. While not a law in the mathematical sense, Moore’s Law bore out—about every 18 months, a
transistor would be half the size of the current transistor.
This meant more transistors could be packed into a chip, driving the exponential growth of computing power for
the subsequent 40 years. However, in the middle of the past decade, the laws of physics finally had their say,
denying transistors the ability to be shrunken any more than they already were. It has been predicted that it
would be difficult to shrink the transistor density beyond 7nm or 5nm.
What Led to the Exploration of Quantum
Computing?
Quantum Computing began in 1980 when physicist Paul Benioff proposed a quantum mechanical model of the
Turing machine. Richard Feynman and Yuri Manin later suggested that a quantum computer could simulate
things a classical computer cannot.

5. This trend is evident in the
field of supercomputers as well. For
the past 25 years, the growth rate in
supercomputer performance has
been consistent at about 80%
annually. Though there were
changes from year to year, the
growth rate has stayed firm in three-
year increments. From 2002 to
2013, performance growth
multiplied by 1,000 times. Though
there has been exponential growth
since 2013, the growth rate has
declined significantly, approximately
40% each year.
Source: Explainthatstuff.com
50 Years of Moore’s Law
1970
1972
1974
1979
1982
1985
1989
1993
1995
2000
2007
2007
2013
2015
2016
2017
2018
2019
2,300 Intel 4004
3,500 Intel 8008
6,000 Intel 8080
40,000 Motorola 68000
1,34,000 | Intel 80286
2,75,000 | Intel 80386
10,00,000 | Intel 80486
31,00,000 | Intel Pentium P5
75,00,000 | Intel Pentium P6
4,20,00,000 | Intel Pentium 4
46,30,00,000 | AMD K10
78,90,00,000 | IBM Power 6
1,00,00,00,000 | Apple A7
1,90,00,00,000 | Intel Broadwell
3,00,00,00,000 | Qualcomm Snapdragon
20,00,00,00,000 | AMD Epyc
39,54,00,00,000 | AMD Epyc Rome
30,00,00,00,000 | AWS Graviton 2
Number of Transistors
Until now, we’ve relied on supercomputers—massive classical computers, often with thousands of classical CPU and GPU cores—to
resolve most problems. However, supercomputers aren’t adept at solving certain types of problems that seem easy at first glance, a key
reason why we need quantum computers. Supercomputers don't have the working memory to hold the myriad combinations of real-
world problems. Instead, they analyze each combination one after the other, making the process time consuming. Unlike classical
supercomputers, quantum computers can create vast multidimensional spaces to represent these large problems.

6. Quantum Supremacy

7. Prominent Hardware Players
Canadian Quantum Computing company D-Wave Systems is
considered the world's first company to sell computers to
exploit quantum effects in its operation. On May 11, 2011, it
announced D-Wave One, described as "the world's first
commercially available quantum computer,” operating on a 128-
qubit chipset using quantum annealing. D-Wave does not
implement a generic quantum computer; its computers
implement specialized quantum annealing, a general method
for finding the global minimum of a function by a process using
quantum fluctuations.
In 2015, D-Wave's 2X quantum computer with more than 1,000
qubits was installed at the Quantum Artificial Intelligence Lab at
NASA Ames Research Center. Since then, it has shipped 2,048-
qubits systems. In 2019, D-Wave announced its next-generation
Pegasus quantum processor chip with 15 connections per qubit
instead of 6; the company said that the chip would have more
than 5,000 qubits and produce less noise.
Honeywell is another major tech company making significant
strides in the quantum computing sphere. In June 2020, it
announced its first quantum computer, Model HO machine, with a
record quantum volume of 64. In September 2020, Honeywell
announced a new H1 Model with a quantum volume of 128. IBM
developed quantum volume in 2017 as a hardware-agnostic
method to measure gate-based quantum computers' performance
and assist in the ongoing development of quantum computers.
Honeywell claims that the H1 Model’s quantum volume of 128 is
the highest in the industry as the model has an architecture
consisting of 10 fully connected qubits.
Enterprises can directly access H1 Model via a cloud API &
Microsoft Azure Quantum and channel partners such as Zapata
Computing and Cambridge Quantum Computing. DHL and Merck
are among the companies that have partnered with Honeywell to
use its quantum systems.

8. Google rose to prominence in the quantum
computing world in 2019 when it announced that
Sycamore, its state-of-the-art quantum computer,
achieved "quantum supremacy.” The quantum
computer carried out a specific calculation beyond
the practical capabilities of regular, ‘classical’
machines. As per Google estimates, even the best
classical supercomputer would have taken 10,000
years to complete the calculation.
IBM has been exploring superconducting qubits since the mid-2000s, increasing coherence
times and reducing errors to enable multi-qubit devices from 2010. It claims to have built
the first quantum computer on the cloud in 2016. In 2017, IBM announced that it would
add two 20-qubit machines to its quantum cloud. The same year, IBM declared that it had
constructed a 50-qubit quantum processor. As of 2020, the company made 28 quantum
computers available.
In 2017, IBM was the first company to offer universal quantum computing systems via the
IBM Q Network. The network now includes more than 125 organizations, including
Fortune 500s, startups, research labs, and educational institutions. Partners include
Daimler AG, JPMorgan Chase, and ExxonMobil. Some use cases of IBM's quantum
computers are the simulation of new materials for batteries, model portfolios and
financial risks, and the simulation of chemistry for new energy technologies.
IBM has launched IBM Quantum—an initiative that uses IBM’s full-stack approach,
including Quantum computing systems, together with software tools and cloud services to
build quantum systems for business and science applications. In 2020, the company
achieved a new milestone on its quantum computing roadmap, achieving its highest
quantum volume to date. Combining a series of new software and hardware techniques to
improve overall performance, IBM has upgraded one of its newest 27-qubit client-deployed
systems to achieve a Quantum Volume 64. Currently, it’s working on IBM Quantum Condor,
a 1,000+ qubit device, likely to be launched by the end of 2023.
Eyeing the future, Google has announced that it would
build a “useful, error-corrected quantum computer” by
the end of the decade. While current quantum
computers are made up of less than 100 qubits, Google
is targeting building a machine with 1,000,000 qubits.
Like many other companies investing in quantum
computing, Google plans to offer its commercial-grade
quantum computing services over the cloud. Google
Cloud has announced its collaboration with Quantum
Computing startup IonQ to make its quantum
hardware accessible through its cloud computing
platform.

9. A Monte Carlo Simulation (MCS) is a model used to predict the probability of different outcomes when the intervention of random variables is present. This
technique is used to understand the impact of risk and uncertainty in quantitative analysis and decision-making. MCS provides a decision-maker with a range of
possible outcomes and the probabilities resulting from an action. It shows the extreme possibilities—the outcomes of going for broke and for the most
conservative decision—and all possible consequences of middle-of-the-road decisions. The technique, first used by scientists who worked on the atom bomb
during the Second World War, was named after Monte Carlo, the Monaco resort town renowned for its casinos.
Monte Carlo Simulations
Goldman Sachs tried overcoming this challenge through its partnership with QC Ware, a quantum software provider. Taking a significant
step toward quantum advantage for financial applications, Goldman Sachs and QC Ware researchers have designed quantum algorithms
that outperform classical algorithms for Monte Carlo simulations and can be used on near-term quantum hardware. The research
community is aware of quantum algorithms that can perform Monte Carlo simulations 1,000x faster than classical methods. However,
these algorithms require error-corrected quantum hardware projected to be available in 10–20 years. Current quantum devices have
very high error rates, and they can perform only a few calculation steps accurately before returning incorrect results. The newly
developed algorithm offered ways to speed up Monte Carlo Simulations with Quantum Computing on near-term hardware that is
expected to be available in the next 5–10 years.
Monte Carlo methods are used to evaluate risk and simulate prices for financial instruments that involve complex calculations and consume significant time and
computational resources. Typically, these calculations are executed once overnight. Thus, traders operating in volatile markets are forced to use outdated
results. Providing traders with a high-speed Quantum Computing approach to perform these risk assessments means that simulations could be executed
throughout the day and could transform the way financial markets worldwide operate.
In finance, a fair amount of uncertainty and risk is involved in estimating the future value of figures or amounts because of the wide variety of potential
outcomes. MCS helps reduce the uncertainty in estimating future outcomes and has multiple applications in finance. For example, in the development of
trading systems, MCS refers to the process of using randomized simulated trade sequences to evaluate the statistical properties of a trading system.

10. Quantum Computing is currently confined to the laboratory experiments by a select few technology companies and niche startups.
Commercial adoption of the technology is at least a decade away.
Adoption
Current and Future States
However, this does not essentially imply a long wait before we put Quantum Computing to use. For the next five years, the most likely
approach would be to combine the power of both classical and Quantum Computing into a hybrid computing framework. Hybrid
computing uses the exponential computing power of Quantum Computing to solve parts of a complex problem and combines them with
classical computing methods to solve other parts. In early 2020, CaixaBank developed a machine learning algorithm leveraging hybrid
computing to do better credit risk profiling based on a limited set of 1,000 user profiles. Google’s TensorFlow Quantum combines state-of-
the-art machine learning and Quantum Computing algorithms in a hybrid model.
Quantum computers need to amp up their processing power before their game-changing effects can be brought mainstream. The good
news is that while the processing power in classical computing increases at an exponential scale (measured by Moore’s Law), quantum
computers increase in power at a logarithmic scale (measured by Rose’s Law). A McKinsey study estimates that globally, only 2,000–5,000
quantum computers will be in operation by the end of this decade.
Although the availability of a competent quantum computer is many years away, Amazon Web Services, Google, and Microsoft provide
cloud-based quantum simulation capabilities for researchers and developers to try out potential applications. Amazon Braket is a
managed Quantum Computing service run in partnership with D-Wave, IonQ, and Rigetti. The availability of cloud-based computing
infrastructure and classical computing is expected to fuel adoption and accelerate learning in this decade. The industry expects that
during this period, quantum computational power will grow fast enough to start implementing specific algorithms and simulations to
solve some of the most complex problems in financial services.

11. Quantum Computing is expected to impact multiple areas in the
financial services industry. D-Wave and Accenture have together
identified over 150 use cases across industries, most of which belong to
financial services. Top sectors in financial services where Quantum
Computing can make a critical contribution are Investments, Insurance,
and Lending.
Impact Areas & Early Use Cases
Quantum Computing enables better machine learning models for
credit risk profiling with a smaller set of variables, without the need
for large amounts of training data. Classical algorithms can slow
down with too many variables in a dataset, adversely impacting
performance. Quantum-based models, however, can process millions
of risk scenarios in a fraction of the time and yet deliver highly
accurate assessments.
Multi-period portfolio optimization accounting for transaction costs and
changing market conditions is computationally complex and involves
processing numerous variables. Quantum-based algorithms can process
faster and help make more accurate decisions on the optimal asset mix for
efficient portfolios.
Lending
Investments & Capital Markets
In early 2020, CaixaBank developed the first quantum-based machine
learning algorithm that classifies risks in Spanish banking. Supported
by the Monetary Authority of Singapore (MAS), Tradeteq and
Singapore Management University are exploring the application of
quantum-based neural networks for better credit scoring of
businesses, helping small businesses gain better access to trade
finance.
The sheer processing power of quantum computers can transform High-
Frequency Trading (HFT) by making it more information-driven.
Combinatorial optimization can improve the trading algorithm by reducing
the number of possible solutions. Designing the optimal trading trajectory,
speeding up risk scenario analysis during stress testing of bank balance
sheets, and better asset-liability management are other use cases that
Quantum Computing is expected to transform.
Multiverse Computing, a Spanish startup, has a fairly mature quantum-
inspired portfolio optimization tool that has shown to generate twice the
average ROI compared to classical computational methods, risk and
volatility remaining constant. Multiverse has worked with BBVA and Credit
Agricole Corporate & Investment Bank in this area to achieve quantifiable
results. As adoption improves and costs become manageable, one can
imagine digital-centric fund platforms and robo-advisors becoming active
users of quantum in the realm of investments.

12. Banks Experimenting with Quantum
Computing
Hardware Players
Note: This is not an exhaustive list.
Software Players

13. Table of
Contents
Introduction
What Led to the Exploration of Quantum Computing?
Complementary Fields of Computing Being Explored
Data Representation
Quantum Supremacy and Key Players
Impact on Financial Services
Adoption & Early Use Cases in Financial Services
Limitations
Conclusion
Research Methodology

14. The ‘Quantum Computing in Financial Services’ report is a comprehensive study based on MEDICI’s
proprietary FinTech data on startups, deep market intelligence derived from years of tracking this
segment, and secondary research refined through brainstorming sessions. We reinforced our
inferences for this report through in-depth interviews with segment experts to extract valuable
market signals from the noise, identify market trends, and develop viewpoints.
Research Methodology
MEDICI has ample information in quantitative and qualitative forms, further curated by our industry
analysts. In the secondary research process, we conducted an in-depth study of the global Quantum
Computing landscape by identifying and understanding the key stakeholders, drivers, trends,
challenges, and opportunities. Key sources referred to for secondary research include company and
industry reports, press releases, government and other official sources, and our partners.
Primary research is the foundation of this study. It complements secondary research with insights
from veterans in the computing industry, founders of FinTech companies, venture capitalists, and
other industry influencers. Over a two month-period, multiple interviews were conducted with
industry experts to draw valuable insights for the report.
Research-based qualitative and quantitative findings and insights were curated by MEDICI analysts
to present a comprehensive view of the Quantum Computing landscape. These were further refined
through MEDICI’s years of experience in deeply tracking industry developments and bringing
together the ecosystem.

15. About
MEDICI is the world’s leading FinTech Research and Innovation Platform. MEDICI is a partner to banks, tech companies, and FIs globally with over 13,000 FinTechs on the
platform, enabling FinTechs to scale and create a global economic impact. MEDICI is committed to supporting the complex financial services ecosystem and enabling
stakeholders benefit from the industry’s accelerated growth and global impact.
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Principal, Research
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Sulesh Kumar
FinTech Strategy and Research
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