In the rapidly evolving world of finance, the quest for more efficient computational methods and advanced algorithms is unending. Enter quantum computing—a revolutionary technology with the potential to redefine the landscape of financial analysis, modeling, and optimization. At its core, quantum computing harnesses the principles of quantum mechanics, a fundamental theory in physics that describes the nature of matter and energy on the most minor scales. But how is Quantum Computing and Quantum Technology being used in pursuing finance? What are those quantum applications being deployed in finance around the planet? Here, we look at some critical applications that institutions, from Hedge Funds to Banks, are experimenting with to gain a Quantum Edge.
Quantum optimization techniques have been applied to practical financial problems like determining the optimal trading trajectory and identifying optimal arbitrage opportunities. These quantum algorithms can potentially process vast amounts of financial data, allowing for more efficient and profitable trading strategies. There are a lot of kinks to iron out, but as you might expect, the financial industry is one of the first industries to explore how quantum computing can give them a profitable edge over their rivals.
Cyber Security
In partnership with Quantinuum, HSBC is delving into the potential advantages of quantum computing in financial services. A significant focus of their exploration is on enhancing cybersecurity measures. Given the sensitive nature of financial data, bolstering security protocols using quantum techniques could revolutionize data protection in banking.
Current cryptographic protocols depend on the inability of conventional computers to factor large numbers into their prime factors. Using Shor’s algorithm, Quantum computers could potentially factorize these large numbers exponentially faster, challenging the security of current encryption methods. However, quantum encryption could offer a solution, providing robust encryption to withstand intrusion attempts by even the most potent classical or quantum computers. All institutions relying on cryptographic security could be affected.
Fraud Detection
In a collaborative venture, Crédit Mutuel and IBM harness quantum computing to enhance fraud detection mechanisms. After a promising initial phase, the organizations have pinpointed specific financial services use cases that can benefit from their joint quantum endeavors.
Financial fraud is a persistent challenge. With the increasing volume of online transactions, detecting fraudulent activities in real time becomes imperative. Quantum machine learning algorithms can potentially process transaction data at remarkable speeds, identifying patterns and anomalies that might indicate fraudulent activities. This rapid detection can help financial institutions respond quickly, safeguarding assets and maintaining customer trust. Firms like Standard Chartered are interested in leveraging quantum computing for enhanced security and fraud detection.
While these are just a few examples, the potential applications of quantum computing in finance are vast. As the technology matures, it’s expected that more financial institutions will explore and adopt quantum solutions to address complex challenges in the sector.
Portfolio Management
IonQ, in collaboration with Fidelity, has developed a quantum technique to enhance portfolio management. Demonstrated on IonQ’s hardware, this technique can assist financial institutions in optimizing their investment strategies, ensuring better returns.
One of the primary use cases of quantum computing in finance is portfolio optimization. Financial institutions constantly seek to maximize returns while minimizing risks. This involves analyzing various investment combinations to find the optimal portfolio mix. Classical algorithms can take considerable time, especially when dealing with many assets. Quantum algorithms, such as the Quantum Approximate Optimization Algorithm (QAOA), can potentially find optimal solutions faster, enabling real-time portfolio adjustments in response to market changes. Companies like Barclays and JPMorgan Chase have shown interest in exploring quantum solutions.
Portfolio Optimization is the art of selecting the best portfolios of stocks from all the given portfolios that maximize expected returns and minimize financial risk. Quantum computers can consider various factors and provide the best solution in minimum time. Quantum annealing is one of the quantum optimization algorithms that can solve such problems. D-Wave is extensively using quantum annealing to solve optimization and sampling problems. They recently launched a plugin allowing developers to map quadratic optimization inputs in IBM’s Qiskit format onto D-Wave’s. Companies like IBM, Zapata, Rigetti, and Microsoft are developing and using this method. Quantum software startups Multiverse and Chicago Quantum specialize in financial applications and have published portfolio optimization results using D-Wave’s quantum computer. 1QBit is another company working on quantum solutions for portfolio optimization. They have developed algorithms that leverage quantum annealers to find optimal asset allocations.
Quantum Monte Carlo
The Monte Carlo Method is a mathematical technique used to estimate possible outcomes of an uncertain event. It is used in finance for stock market simulation, portfolio evaluation, and derivatives pricing. Quantum computing can provide a speedup in the Monte Carlo Method using the quantum amplitude estimation algorithm. IBM has published a paper showcasing a quantum-accelerated version of Monte Carlo simulations. Cambridge Quantum Computing (CQC) made a breakthrough in quantum Monte Carlo integration using quantum amplitude estimation. JPMorgan Chase and Barclays have used IBM’s quantum-computing software for Monte Carlo simulations since 2017. Goldman Sachs achieved a speedup in derivatives pricing using this method.
Cambridge Quantum Computing (CQC) recently made a breakthrough in Quantum Monte Carlo integration using quantum amplitude estimation. JPMorgan Chase and Barclays have been using IBM’s quantum-computing software to test Monte Carlo simulations for portfolio optimization since 2017. Goldman Sachs was able to speed up derivatives pricing by a thousand times for a million potential paths compared to the Monte Carlo method. BMO Financial Group and Scotiabank collaborated with Xanadu to benchmark a quantum Monte Carlo algorithm for various trading products.
Quantum Machine Learning (QML)
Machine learning focuses on finding relations in data. This is implemented using hybrid methods that involve both classical and quantum processing. Xanadu, Google, IBM, and Zapata are some of the companies implementing quantum machine learning algorithms. Xanadu has launched its open-source software Pennylane for quantum machine learning applications. Nomura Asset Management (NAM) works with Tohoku University on stock return prediction using quantum machine learning algorithms. JP Morgan is actively developing quantum algorithms for artificial intelligence and optimization.
High-dimensional Optimization Problems
Financial institutions, especially banks and asset managers, often grapple with high-dimensional optimization problems. These involve processing large sets of variables to make informed decisions. Quantum computing can expedite this process, allowing for faster and more precise decision-making, such as determining the best investment portfolio mix.
Credit Scoring
Credit scoring is used by financial institutions to assess the creditworthiness of individuals or entities. Classical models use statistical methods to predict the likelihood of a borrower defaulting on a loan. Multiverse Computing is a company that delved into quantum computing to enhance credit scoring models. They believe that quantum algorithms can provide more accurate and faster predictions. Multiverse Computing’s approach to quantum computing processes vast amounts of data in parallel, allowing for a more comprehensive analysis of a borrower’s financial history. This quantum approach differs from classical models, which might be unable to process and analyze large datasets as efficiently, potentially missing out on crucial information.
Option Pricing and Derivatives
Option pricing is a fundamental concept in finance, determining the fair market value of an option. Classical models, like the Black-Scholes model, have limitations and assumptions that might not always hold in real-world scenarios. JPMorgan Chase, in collaboration with academic institutions, has been researching the potential of quantum computing in improving option pricing models. JPMorgan’s research in quantum computing focuses on developing algorithms that can model complex financial scenarios more accurately, considering various factors that classical models might overlook. The quantum approach aims to simulate financial systems more holistically, capturing the intricacies and interdependencies of market factors.
How to get Started with Quantum Computing
Often, there is no better way than to start, but how? Many online resources help you understand quantum technologies, ranging from courses on elementary quantum mechanics and mathematics to how to program a quantum computer using one of the popular quantum languages such as Qiskit, Q#, or Cirq. There are also books that you can read to get a jump start on quantum technologies. For a great academic review paper on Quantum in Finance, the researchers behind Multiverse have produced an excellent article.
