Quant Squared: Quantum Computing in Quant Finance

What is Quantum Computing?

Starting with a formal definition, quantum computers take certain atomic or subatomic particle behaviors found in quantum mechanics, including superposition, entanglement, and quantum interference and apply them in a computational context. These systems take inputs and produce outputs, as do other forms of computing, but this is a “nonclassical” area of computer science and is actively driving alternative concepts that contrast with traditional programming methods.
 

A few other essential terms include:
 

• Qubits: This is the basic unit of information in quantum computing. The role of qubits is like the role of bits in a classical computer, but their behavior is different. Bits are binary, taking values of 0 or 1 only, whereas qubits can maintain a superposition of all possible states.
   
• Superposition: The term superposition pertains to the concept that quantum particles exist in combinations of all possible states, which vary until they are observed and measured. A simple analogy is to consider a coin toss. In the classical case, the bits will be seen as coming up heads or tails. In the quantum case, we might see the coin as being heads and tails simultaneously or featuring every state in between the two extremes.
   
• Entanglement: Quantum particles interact with and influence each other at close range and even at great distances. In the quantum computing context, when qubits are entangled, it is possible to use measurements from one qubit to develop conclusions about the other qubits in the system. This process enables quantum computers to calculate a vast amount of information and solve complicated problems quickly.
   
• Quantum interference: This refers to the qubit’s intrinsic behavior and influences on the probability of it collapsing one way or another. Quantum computers are designed to reduce outside interference and to produce the most accurate internal results. 
   

The field of quantum mechanics is filled with puzzles and mysteries, but naturally the literature in the field has been developing steadily since the early 20th century. However, applying the principles of quantum mechanics to high performance computing started in earnest in the 1980s and has only gained momentum in the business world over the past few years. As an emerging technology, quantum computing appears to hold promise for quantitative finance, having special capacity for probability, vast amounts of data, and complex calculations. 
 

How does quantum computing fit into quant finance?

In a recent poll by the CQF Institute, when people were asked what areas they were most interested in, Quantum Finance and Quantum Computing were the most popular, after Machine Learning. Of those interested in QC, 57% said the most exciting aspects were speed, efficiency, and security and 26% said that quantum computing will stimulate new ways of thinking about finance.
 

As a practical matter, even experts admit that quantum computing is likely 5-10 years away, with a number of technical challenges, such as stabilizing the qubits, yet to be resolved. However, the existing machines are much faster and more powerful than classical computers and can handle voluminous information and extremely complex problems quickly. While there is much excitement about their potential, quantum computers are not intended to replace classical computers. Instead, they will provide additional, unique capabilities for solving complicated, data-intense problems, including the use of machine learning techniques and performing calculations that classical computers would not be able to handle in a reasonable (human scale) amount of time. This challenge is known as “quantum supremacy” and in fact, Google announced that it had attained this goal in October 2019, but that claim has been subject to debate.
 

As for applications in finance, Goldman Sachs is one of the firms leading the change in quantum computing. Earlier this year, an article in the Financial Times* described how Goldman was exploring the use of quantum machines to price complex derivatives, a computationally intensive task that represents a substantial cost for investment banks. As the FT noted, “In … research last year with IBM, Goldman calculated that it would need a quantum computer with about 7,500 quantum bits, or qubits, to run a full Monte Carlo simulation.” The firm is also collaborating with the start-up QC Ware but offers cautionary words on the timeframe for peak performance: “Rather than a 1,000-fold improvement expected of a fully error-corrected quantum computer, running such a calculation using today’s imperfect quantum hardware could yield a 10-fold gain within five years, according to the researchers.” However, that projection still seems promising for quants and in the meantime, there are plenty of tools and techniques to master.
 

Other areas that lend themselves well to quantum computing include portfolio optimization and cryptography, which JP Morgan, for example, has been discussing in the financial press. As with the work at Goldman, the focus is on the speed of the calculations, honing an edge in client services, and the potential costs savings over time.
 

A word from experts on the future of quantum computing

In a recent QuantSpeak podcast, David Orrell, author of Quantum Economics: The New Science of Money" commented, "It's getting a bit easier now [to communicate quantum ideas] and part of this has been the rise of interest in quantum computers - there is kind of this feedback loop between the tools that we use and the ideas…we have been thinking in a classical way for a long time… but once people start getting their heads around quantum computers, then that also starts to change about the way you think about other things as well." 
 

He also offered an observation on technology development, “Right now quantum computers are at this basic state and very expensive and so on, but I think there is scope for quantum devices….For something like a quantum walk, you don't need a full quantum computer, you just need a quantum device that can do a quantum walk, which is much simpler… and something like that is definitely within reach in a few years."
 

The idea of a quantum device is enticing and may lead to further developments in the industry. As technologists often point out, the smart phones of today are more powerful than the computers that helped the Apollo 11 mission to the Moon in 1969. Imagine a quantum micro-device for your pet machine learning project! 
 

Looking at the current initiatives, we can expect to see more action around the pricing of complex instruments, portfolio optimization and asset allocation, and also sustained work around cryptography to ensure the protection of financial technology in systems at banks, funds, and the exchanges themselves. As quantum computing evolves, it may well stimulate new forms of alpha generation and deeper insights on the interactions between assets, within markets, and across the national economies around the world. Like high frequency trading, quantum computing in finance may be a niche play for the financial elite at first, but it could grow to shape the market dynamics more broadly as it catches on. 
 

For quants and students of quantitative finance, it is a good time to listen and learn, experiment with programming languages and computing architectures, and keep their eyes on the horizon for quantum computing in the years to come.
 

Quantum Computing in Finance and the CQF

The CQF syllabus explores quantum computing in module 5 of the qualification, where it covers the definition of quantum computing, the three key ingredients of quantum computing, and applications in various fields. Delegates are also taught to construct a simple quantum circuit online using the IBM Quantum Experience and to write their own quantum program using the Python module Qiskit. This is taught by Dr. Alonso Peña. 

Quantum computing is also explored again in more detail in the advanced electives at the end of the qualification.

*See “Goldman Sachs predicts quantum computing 5 years away from use in markets,” Financial Times, April 19, 2021.

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