HSBC collaborates on noisy qubit real-world application

HSBC has collaborated with quantum middleware developer Haiqu and a team of academic researchers on an efficient way to run financial models on commercially available quantum computing hardware. The publication of joint research in Physical Review Research discussed an approach to encoding real-world probability distributions into quantum circuits.

HSBC is among the financial institutes looking at how to secure financial transactions as quantum computing evolves into commercially viable products, such as protecting financial systems using post quantum cryptography. Quantum computers also offer banks such as HSBC a way to run more powerful financial market simulations.

The research team – which included experts from HSBC, Haiku, Czech Technical University, University of Zurich, the Akhiezer Institute for Theoretical Physics and Karazin Kharkiv National University in Ukraine, and Greece’s Athena Research Center – looked at Lévy distributions, which are used when modelling extreme variations of stock markets indexes worldwide.

“By developing methods to efficiently work with Lévy distributions on a quantum computer, we pave the way for more precise modeling of market behaviours, particularly in capturing heavy tails, skewness and volatility clustering,” the researchers stated in the paper.

According to Haiqu, while quantum computing can be used in derivative pricing, portfolio optimisation, fraud detection and machine learning, these applications need realistic financial distributions. This means data must first be loaded into a quantum computer. The process of encoding classical data into quantum states is widely recognised as a major bottleneck when implementing many quantum algorithms on hardware. The challenge is particularly relevant for applications such as financial risk modelling and simulation, where complex probability distributions must be loaded onto quantum devices.

Haiqu said that the number of required quantum operations in conventional algorithms can scale exponentially with the number of qubits, making it a significant bottleneck on today’s noisy, depth-limited hardware. To address this problem, the company has developed compact quantum circuits with linear, rather than exponential, scaling.

“One of the biggest practical barriers is getting realistic financial data onto today’s quantum hardware,” said Mykola Maksymenko, co-founder and CTO of Haiqu. “This work shows a scalable path around that barrier and helps move quantum finance workflows from theory toward execution.”

The researcher uses matrix product state (MPS) methods to construct shallow quantum circuits that encode smooth functions, including probability distributions, directly into quantum states.

Using a 25 qubit IBM quantum computer, the research paper stated that accuracy of the machine was sufficient to pass quantitative statistical tests, even on current noisy quantum computing devices. “The results on distribution loading may have importance to various domains of financial risk analysis, risk management and decision-making that include series of financial data,” the researchers noted.

They used a sampling-based workflow, running on 64-qubit hardware, which they said demonstrates the feasibility of their approach when running at larger scales. In the paper, they stated that similar behaviour was observed in simulations up to 156 qubits, which they claimed means the approach could extend to substantially larger problem sizes.

“Preparing complex probability distributions efficiently is a key step in many quantum algorithms,” said Philip Intallura, group head of quantum technologies at HSBC. “This work shows how they can be implemented with much shallower quantum circuits, bringing practical applications such as financial risk modelling closer.”