HSBC, Haiqu Encode Financial Risk Data on 156-Qubit IBM Machines as Quantum Funding Tops $2 Billion
Updated
Updated · InformationWeek · Jun 5
HSBC, Haiqu Encode Financial Risk Data on 156-Qubit IBM Machines as Quantum Funding Tops $2 Billion
1 articles · Updated · InformationWeek · Jun 5
Summary
HSBC and Haiqu said their April research encoded heavy-tailed financial risk distributions into shallow quantum circuits run on IBM systems with up to 156 qubits, targeting a key bottleneck in quantum finance.
That bottleneck is state preparation: loading classical market data into quantum states can erase quantum gains, especially for Monte Carlo-based tasks such as value-at-risk, derivative pricing and credit portfolio risk.
The companies used matrix product state methods to model distributions such as Lévy and Gamma, aiming to preserve the quadratic speedup of quantum amplitude estimation without intractable gate counts or excessive noise.
The work lands as governments push quantum toward industrial use, with the U.S. planning $2 billion for nine companies and the U.K. launching a £2 billion effort, even as scaling, error correction and cooling remain major hurdles.
Industry executives and analysts say useful quantum systems are nearing niche commercial applications, but the longer-term model is still hybrid computing, with quantum processors augmenting CPUs and GPUs rather than replacing them.
Why does private capital favor AI while governments pour billions into the less certain future of quantum computing?
As the quantum decryption threat nears, is the industry's focus on offense rather than critical digital defense?
Could the energy needs of future quantum data centers undermine the very climate problems they are meant to solve?
From Theory to Practice: HSBC & Haiqu’s Quantum Finance Leap and the US $600M Quantum Computing Push
Overview
Recent joint research by HSBC and Haiqu marks a major step forward in quantum finance by solving the tough problem of efficiently loading real financial data onto quantum hardware. This breakthrough uses an improved Matrix Product State (MPS) method, which greatly reduces circuit depth and adapts well to current quantum devices. The new protocol makes it possible to encode complex data, like Lévy distributions that are hard for classical computers, directly into quantum states. As a result, quantum finance is moving from theory to practical use, opening the door to more accurate and scalable financial modeling.