Harper Lane tackles quantum computing and its current use in banking and finance. Harper has written several articles recently for the 21st Century Tech Blog, with many readers commenting on her contributions here as well as on LinkedIn.
For those who see the arrival of quantum computing as a coming Q-apocalypse for every system and application protected by passwords and encryption algorithms, it is interesting to see how banking and finance are forging ahead with qubits added to bits and bytes.
It is also interesting to read about how quantum devices may travel that last mile to close the gap to achieve widespread adoption, not just in the financial sector, but rather, globally.
I will let Harper tell you more.
Quantum computing has captured the imagination of the financial sector, promising breakthroughs in portfolio optimization, risk modelling, derivative pricing, and fraud detection. Yet behind the headlines lies a more nuanced reality.
Today’s quantum systems are fragile, noisy, and constrained. The gap between theoretical advantage and practical deployment remains significant. Understanding error correction, the techniques used to detect and fix mistakes in quantum computations, and noise, the unwanted disturbances that cause quantum bits or qubits to lose coherence and produce incorrect results, is essential. Anyone evaluating how quantum technologies need to be well-armed with knowledge about quantum strengths and weaknesses.
Quantum Promise for Financial Modelling
Many financial problems are mathematically complex and computationally intensive. Classical computers often struggle with tasks such as simulating large correlated systems, solving optimization problems with many variables, or pricing complex derivatives under uncertain conditions. Quantum computing, supposedly, can offer faster results for these problems using fundamentally different computing mechanisms than conventional systems.
For example, Monte Carlo simulations, widely used in options pricing and risk assessment, could benefit from quantum algorithms that reduce the number of samples required to achieve a given level of accuracy. Similarly, portfolio optimization problems—where firms aim to allocate capital efficiently across thousands of assets—can be framed as combinatorial challenges that quantum computers may solve more efficiently using techniques like quantum annealing.
So, which banks are dipping their toes into quantum computing waters? A number have launched pilot projects. These include: JPMorgan Chase, which is using quantum algorithms for derivatives pricing and optimization; Goldman Sachs, using options pricing and Monte Carlo acceleration research; Barclays, HSBC, and BBVA, doing exploratory work in risk modelling and portfolio optimization
Noise: The Central Limitation of Current Quantum Systems
Unlike classical bits, which are stable and deterministic, qubits are highly sensitive to the environment. The quantum folk call these noise. They include small interactions with external factors, such as temperature fluctuations, electromagnetic interference, or imperfections in control signals. All can disturb a qubit’s state, and it is one of the primary barriers to widespread quantum adoption.
Noise in financial transactions and calculations introduces unacceptable uncertainty, leading to computational errors that cause significant financial losses. This can happen when calculating derivative values or when modelling probability distributions. Both would produce disastrous outcomes.
The current state of quantum computing devices is classified as NISQ, meaning Noisy Intermediate-Scale Quantum systems. They can perform limited calculations but lack the stability required for large-scale, fault-tolerant computation. For financial firms, this means that while experimentation is possible, mission-critical workloads remain out of reach.
Overcoming Quantum Deficiencies
To deal with noise, quantum systems researchers have developed quantum error correction (QEC) techniques. Unlike classical error correction, which typically involves duplicating bits, QEC encodes a single logical qubit into many physical qubits. This allows the system to detect and correct errors without directly measuring the quantum state, which would otherwise collapse it.
The overhead needed is substantial. Maintaining one reliable logical qubit can require hundreds or even thousands of physical qubits. The current quantum processor limitations, often only ten to a few hundred qubits, pose a scalability problem.
For financial applications, it means that the algorithms needed to deliver real results are not yet feasible. Even relatively modest tasks in portfolio optimization or risk analysis could demand error-corrected systems far beyond current capabilities.
Despite these challenges, progress is ongoing. Advances in hardware design, improved qubit coherence times, and more efficient error correction codes are steadily reducing the gap. Still, the timeline for fully fault-tolerant quantum systems remains uncertain, and financial institutions must plan accordingly.
How and Who are Quantum Partnering with the Financial Sector
Banks and financial institutions are not installing these devices on site because quantum systems require super-cold millikelvin temperatures, vibration isolation, and shielding from other external noise. The result is that quantum devices are being accessed through the Cloud using software and hardware-as-a-service models.
Right now, the world of finance is using quantum computing solutions from a limited number of vendors, including:
- IBM Quantum, with its cloud-accessible superconducting systems.
- Google Quantum AI, with its advanced research hardware.
- IonQ, Quantinuum (Honeywell) with trapped-ion systems.
- D-Wave with quantum annealing (these are used more directly for optimization experiments).
- And software layer players like Classiq, Zapata, and QC Ware.
Short-Cut Hybrid Quantum Solutions
Current qubit limitations and environmental concerns are leading to hybrid quantum-classical computing solutions, with the latter handling the bulk of computation, while the quantum processors are used selectively to handle specific subroutines where they can provide an advantage.
For example, a risk management platform might use classical methods to generate baseline scenarios, then apply quantum techniques to refine certain optimization steps. Similarly, in fraud detection, quantum algorithms could assist with pattern recognition in high-dimensional datasets, while classical systems manage data ingestion and preprocessing.
This is where quantum computing software plays a crucial role. These platforms act as bridges between hardware and application layers, enabling developers to design, simulate, and test quantum algorithms in real-world contexts. By abstracting hardware complexities, they allow financial engineers to experiment without needing deep expertise in quantum physics.
Real-world trials have already demonstrated incremental gains. Some firms have reported improvements in sampling efficiency for Monte Carlo simulations, while others have explored enhanced optimization techniques for asset allocation. However, these gains are often modest and situational rather than transformative.
Managing Quantum Finance Expectations
The financial industry has a history of adopting advanced technologies quickly, but also of overestimating short-term impact. Quantum computing is no exception. While the long-term potential is significant, current systems are not ready to replace classical infrastructure.
Executives and technologists must balance enthusiasm with realism. Investments in quantum research and partnerships can provide valuable insights and strategic positioning, but expecting immediate returns is risky. Instead, organizations should focus on building internal expertise, identifying niche applications, and monitoring technological progress.
A practical example is stress testing. Regulators require banks to simulate extreme economic scenarios, which can involve massive computational workloads. While quantum computing could eventually accelerate these simulations, current systems cannot yet deliver the required scale or reliability. Firms that understand this limitation can invest wisely rather than chasing premature deployment.
The Future Includes Photonic Quantum Devices
When will quantum computing go mainstream? For the financial sector, it depends on breakthroughs in hardware. Fault-tolerant quantum computers that run in ambient room-temperature conditions are the desired solution, and it may not be that far away.
A new quantum computer system, Aurora, developed by Xanadu, a Canadian company, is the first scalable photonic quantum device capable of operating at room temperature. Overcoming that severe environmental precondition to widespread adoption is a game-changer.
Meanwhile, using smaller photonic quantum devices interconnected by fibre optics represents another significant advance. Christian Weedbrook, the founder and CEO of Xanadu, states, “Photonics really is the best and most natural way to both compute and network. We now could, in principle, scale up to thousands of server racks and millions of qubits.”
Aurora means more near-absolute-zero temperatures and noise-cancelling hardware restrictions. Aurora uses networked photonic chips that can be distributed across large area networks connected through a fibre-optic mesh. It means, instead of a single large quantum computer, you have a network of smaller and simpler quantum devices that are easier to error correct individually while being fully interconnected and operating at light speed.
