HSBC & Haiqu Solve Quantum Finance’s Data Block with IBM

Quantum computing has long promised to calculate market risks at lightning speed, but this vision has been blocked by the data bottleneck. 

Getting complex, real-world financial data into a quantum computer is notoriously difficult. 

Now, new joint research from HSBC and quantum software startup Haiqu suggests a breakthrough. 

Their findings prove that financial risk modelling applications are much closer to practical reality than previously thought. 

Solving the loading problem

The research shows that financial institutions can provide financial data to a quantum computer through a process called Quantum State Preparation. 

Normally, encoding “heavy-tailed” distributions – mathematical models used to predict extreme market crashes – requires complex circuits that today’s quantum hardware simply can’t handle. 

These circuits become overwhelmed, causing the quantum computer to crash before it finishes the calculation.

HSBC and Haiqu solved this by using a method called Matrix Product States.

This allowed them to create shallow circuits, which are essentially a more streamlined, efficient way to pack data. 

Instead of trying to store every single piece of data in the computer’s memory at once, they used a sampling-based workflow that “avoids storing the full discretised dataset in classical memory, enabling larger encoding circuits to be generated,” reads the firms’ press release.

Real-world testing on IBM hardware

IBM provides access to quantum computing processors like the Eagle and Osprey, which are designed to handle increasingly complex workloads. HSBC and Haiqu ran their tests on this hardware.

The study demonstrated the method’s efficacy across increasingly complex scales. 

At the 25-qubit level, a threshold where physical quantum processors begin to handle complex data, the team utilised IBM hardware to successfully reproduce probability distributions that satisfied all standard statistical benchmarks 

To test the system’s resilience against the errors common in larger processors, the team moved to 64 qubits.

There, they implemented a specialised workflow to run circuits under realistic noise conditions, confirming that the methodology remains robust even when the underlying hardware is imperfect. 

Finally, seeking to demonstrate the method’s ultimate potential, they turned to simulations involving 156 qubits.

This proved that their approach could scale to manage massive datasets, effectively pushing the boundaries far beyond what was previously considered possible in the field. 

“Preparing complex probability distributions efficiently is a key step in many quantum algorithms,” explains Dr 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.”

Why this matters for your wallet

In finance, risk modelling is everything. It helps banks understand how much money they might lose during a market downturn, ensuring they stay stable and protect their customers.

By making these models quantum-ready today, HSBC and Haiqu are moving the technology out of the lab and into the real world. 

Mykola Maksymenko, Co-founder and CTO of Haiqu, says: “One of the biggest practical barriers is getting realistic financial data onto today’s quantum hardware.

“This work shows a scalable path around that barrier and helps move quantum finance workflows from theory toward execution.”

While we aren’t yet at the point where a quantum computer manages your local bank branch, this research proves that using quantum technology in finance is now very close to becoming a reality.