How does quantum computing impact the finance industry?

QCs are only in the developmental stage; experiments are already showing their great potential in the finance industry.

Based on the World Economic Forum’s estimate from 2022, national governments have invested more than $25 billion in quantum computing research, and over $1 billion in venture capital deals were closed in the previous year. Quantum computers (QCs) are in the early stages of development, and there are many technical challenges that need to be overcome before they can become practical tools for everyday use.

Nevertheless, they have already demonstrated great potential for applications in a wide range of fields. QCs have the ability to solve complex mathematical problems exponentially faster than classical computers, making them ideal for several complex tasks. The finance industry is one of the first runners in testing the technology. However, from the military to pharmaceuticals, logistics and manufacturing companies, several industries are experimenting with QC.

The mentioned features of QCs can have an enormous impact on the future of financial services. There are several tasks where financial forecasting and financial modeling can be supported by QCs for faster and more accurate processes. Notably, portfolio optimization, risk management and asset pricing are some of the most mentioned examples. However, their potential advantages and threats to cryptography make it important for financial service providers to monitor the technology.

Collaboration is crucial in the area of QCs due to the fact that technology and software development enable the revolution. Accelerating programs are initiated by the largest tech companies for experimentation with their hardware, software or cloud solutions, such as IBM, Microsoft, Google or Amazon.

Goldman Sachs has partnered with Microsoft Azure Quantum to explore the use of QCs for pricing. JPMorgan is experimenting with quantum solutions for optimization and risk management. HSBC announced its collaboration with IBM in 2022 to explore the use of QCs for pricing, portfolio optimization and risk mitigation.

QCs are new machines that can perform calculations much faster than classical computers, based on the principles of quantum mechanics.

The expression of QCs refers to a new type of machine based on the principles of quantum mechanics. Quantum mechanics is a division of physics that deals with the behavior of matter and light on the atomic and subatomic scales. The most valued property of QCs is that they perform certain types of calculations much faster than classical computers.

Classical computers store and process information in the unit of bits while QCs use quantum bits (or qubits). Bits represent information in a binary format and can have only two possible values: zero or one. Every piece of information going through a classical computer is essentially a long string of zeros and ones.

Qubits can exist in multiple states at once, a property known as superposition. This means that a single qubit can represent numerous possible combinations of zeros and ones; therefore, it can process a much larger amount of information than a classical bit.

Another exciting feature of qubits is the potential of “entanglement,” where qubit pairs are created. Modifying the state of one in the pair will change the state of the other qubit in a predictable way. This property gives extra power to QCs. Increasing the number of bits in a classical computer has a linear effect on the processing power, while adding an extra qubit to a quantum machine causes an exponential increase in the processing power.

Despite the great potential of QC, the technology and its applications need to overcome several challenging barriers.

Working with qubits is an enormously challenging scientific task because they need to be isolated in a controlled quantum state, which is extremely fragile. The smallest change in the physical environment (vibration or temperature) can cause an imbalance, which is the collapse of the superposition. Complex preventive actions are required, such as supercooled refrigerators, insulation or vacuum chambers to protect the system from losing its equilibrium.

Another aspect of the challenge is that as a different paradigm, QCs require not only completely new hardware and software but also algorithmic solutions. Numerous articles discuss the potential of QCs in machine learning, artificial intelligence or cryptography. Less often emphasized that it does not only mean using QCs to run algorithms designed for classical computers (quantum-enhanced) but building completely new algorithms, which are leveraging the features of QCs.

QCs in banking can be a game changer due to the potential of multiplying the speed and volume of calculations and transactions. However, different financial institutions only started to experiment with their own quantum algorithms and the limits of those potentials are not clear yet. Quantum algorithms are algorithms that take advantage of the unique properties of quantum systems, such as superposition and entanglement.

One example of quantum algorithms is Grover’s algorithm, which can be used to search large, unstructured databases of financial data more quickly than classical algorithms. For example, it could be used to search for specific financial transactions or to identify patterns in financial data. Another example is Shor’s algorithm, which enables one to factor in large numbers more quickly than classical algorithms.

The finance industry is optimistic about quantum computing. Tasks such as portfolio optimization, risk management and asset pricing have a great chance to be beneficiaries.

Grover’s and Shor’s algorithms can be applied to portfolio optimization. Portfolio optimization involves finding the optimal combination of investments to maximize returns while minimizing risk. Besides providing faster and more accurate calculations the technology can enable more flexible optimization strategies that take into account a wider range of factors, such as environmental, social and governance factors.

Another example could be asset pricing. Asset pricing is the process of estimating the value of financial assets such as stocks, bonds and derivatives. Traditional methods for pricing financial assets rely on complex mathematical models, such as Monte Carlo simulations, which involve simulating a large number of possible outcomes for a given financial asset and then using these simulations to estimate its value. Quantum Monte Carlo (QMC) can handle, for example, complex financial instruments, such as options, that have non-linear payoffs.