Quantum Computing Disrupts Finance
The quantum computing industry is gaining pace, and as we’ve already discussed in our previous blog post, we expect to see practically useful applications emerge within the next 3-5 years. During this period, IQT believes the financial sector will be a leader in terms of adoption and integration of the new technology. The most important reasons we think this are shown below.
| Benefit of Quantum Computing | Revenue/Cost Savings |
| Huge potential for savings from quantum computing | When companies deal with multi-billion dollar revenues, even minor improvements in the efficiency of operations can save hundreds of millions of dollars. |
| Quantum computing is a natural extension of HPC | HPC is already entrenched at banks and other financial institutions and there is already a high degree of interest in quantum computing at financial institutions. Some big banks are already taking on dedicated quantum staff, who can take HPC to the next stage for strategic advantage |
| Quantum computing satisfies a real need to support big data at banks and other financial institutions | Financial data is constantly generated and needs to be structured and analyzed. If room-temperature quantum computers with adequate size and maintenance requirements appear, the financial sector will very likely look into purchasing many dedicated machines for real-time operations or become massive users of cloud services – but that time has not yet come |
| Financial problems inherently involve a lot of unpredictability that quantum computers can handle better than classical computers | Financial problems of the kind that big banks, insurance companies and hedge funds deal with are complex and take time to solve – quantum computers speed up the process |
Early Stage of Disruption: Coming Sooner than you Expect!
We have been told by several banks that it could be many years before quantum computers will have much impact on their operations. But we think they are being shy! They are already hiring big name quantum technologists – this is not consistent with their stated belief that it could be ten years before quantum will make a difference. We think that it could be as early as three years before we start to see some (albeit minor) results.
Also, the quantum revolution in the financial sector will not be confined to the fringes. Although a handful of banks are getting most of the publicity in “quantum finance,” we estimate that there must be at least 30 banks and other financial institutions that are dabbling in quantum right now. [Fair enough: small town banks have probably yet to hear about quantum yet, but the revolution has to begin somewhere]
Before dismissing our bullish prophesies, please consider that quantum computing can do the math orders of magnitude faster and do not require fault-tolerant computing. Noisy quantum computers with only a few hundred qubits will be enough, and soon other banks will be following Goldman Sachs, JP Morgan Chase, Barclays, Citigroup, BBVA and others who are already building their quantum assets. More banks will follow.
For the Banks the Quantum Revolution begins in Monte Carlo
At Inside Quantum Technology, we expect Quantum Monte Carlo (QMC) methods to be the first family of quantum algorithms to bring significant revenue generation/cost savings for the financial sector. Classical Monte Carlo methods are already widely used for modeling and pricing financial derivatives, running on parallel CPUs and GPUs. The quantum analogs will provide significant speedups. So it makes sense we believe for quantum to invade the classical realm first in Monte Carlo, as it were.
But (QMC) is just one example of how quantum computers can disrupt the financial sector. To give a picture of the likely future consider:
- Several Quantum Machine Learning subroutines offer significant speedups in comparison to their classical counterparts (usually a square-root or exponential speedup). These methods include: quantum Bayesian inference, online perceptron, least-fitting squares, quantum Boltzmann machine, quantum reinforcement learning.
- Optimization problems account for a large portion of financial problems and are well suited to quantum machines including annealers. These include calculating optimal trading trajectories (a version of this problem was run on D-Wave’s machine), optimal arbitrage opportunities, and optimal feature selection in credit scoring.
For more in-depth discussions on quantum computers and other quantum technologies be sure to participate in the Inside Quantum Technology Online Conference on October 26-30, 2020.
