Classiq and Sumitomo Corporation

In March 2025, Classiq Technologies, a leading quantum computing software company, partnered with Sumitomo Corporation and Mizuho-DL Financial Technology to achieve a significant breakthrough in quantum computing applications for financial risk management. The collaboration successfully demonstrated up to 95% compression of quantum circuits for Monte Carlo simulations, marking a major milestone in making quantum computing viable for practical financial applications.

The Financial Risk Management Challenge

Financial institutions routinely employ Monte Carlo simulations for derivative pricing and asset risk evaluation, essential processes for managing investment portfolios and complying with regulatory requirements. However, these simulations require generating vast numbers of random scenarios, leading to high computational costs and extended processing times on classical computers.

Traditional risk assessment methods, particularly for credit portfolio risk management, involve evaluating numerous complex variables and potential scenarios. For large financial institutions, running these simulations can take hours or even days, limiting the ability to respond quickly to changing market conditions or to perform comprehensive risk assessments with the desired frequency and accuracy.

Quantum computing offered potential solutions to these computational challenges, but practical implementation faced significant hurdles. Quantum circuits for financial simulations typically required either a large number of qubits or deep circuits with many operations, making them difficult to run efficiently on current quantum hardware with limited qubit counts and high error rates.

Quantum Solution

The partners developed an innovative approach to quantum-enhanced Monte Carlo simulations that leveraged Classiq’s quantum software platform and specialized algorithms provided by Mizuho-DL Financial Technology. The project focused on two distinct methods for implementing quantum Monte Carlo simulations.

• Traditional Quantum Monte Carlo Simulation: This approach required a dedicated qubit for each random number needed in the simulation, leading to high qubit usage but relatively shallow circuits.

• Pseudo-Random Number-Based Quantum Monte Carlo Simulation: This novel method, developed by Mizuho-DL FT, generated necessary random patterns in stages, significantly reducing the required qubit count at the cost of deeper, more complex circuits.

To optimize these quantum approaches, Classiq applied its quantum circuit compression technology. The company’s high-level quantum language, Qmod, enabled the team to generate optimized circuits for both simulation methods, focusing on reducing circuit depth while maintaining computational accuracy.

The solution was designed to address the practical limitations of current quantum hardware, which typically features limited qubit counts and is susceptible to errors, particularly in deep circuits. By optimizing circuit design, the partners aimed to enable financial institutions to run complex risk simulations more efficiently than possible with classical computing alone.

Implementation

The implementation process began with Sumitomo Corporation, which had established its Quantum Transformation (QX) project in 2021 to revolutionize business processes through quantum computing . Sumitomocorp As part of this initiative, Sumitomo had invested in Classiq, recognizing the Israeli company’s expertise in developing essential software for quantum computing.

For the Monte Carlo simulation project, the partners adopted a methodical approach:

• First, they defined the financial risk management use case, focusing on credit portfolio risk assessment, a computation-intensive process critical for financial institutions.

• Next, Mizuho-DL FT provided quantum algorithms for both traditional and pseudo-random number-based Monte Carlo simulations.
  Classiq then applied its quantum circuit design and optimization technology to generate efficient implementations of these algorithms, with a focus on reducing circuit depth and qubit requirements.

• Finally, the partners evaluated the performance of the optimized circuits, measuring both computational efficiency and accuracy compared to conventional approaches.

Throughout the implementation, the team focused on creating practical solutions that could be executed on current quantum hardware while positioning the partners to take advantage of more capable quantum systems as they become available.

The collaboration achieved remarkable results, demonstrating up to 95% compression of quantum circuits for both types of Monte Carlo simulations while maintaining computational accuracy . Quantumcomputingreport This dramatic reduction in circuit depth significantly improved the feasibility of running these algorithms on current quantum hardware, which is limited by noise and error rates that increase with circuit depth.

The pseudo-random number-based approach proved particularly effective, requiring fewer qubits than the traditional method while still delivering accurate results after optimization. This approach addressed one of the key limitations of current quantum hardware: the restricted number of available qubits.

From a business perspective, these achievements marked a significant step toward practical quantum computing applications in finance. By enabling high-precision calculations with fewer resources, the study demonstrated that large-scale probabilistic simulations for financial risk management may be feasible on near-term quantum hardware . Morningstar, Inc.Classiq This could potentially lead to faster, more accurate risk assessments, enabling financial institutions to make better-informed decisions and optimise their portfolios more effectively.

For Sumitomo Corporation, which had been exploring quantum computing applications since 2018, this success represented a significant milestone in its QX project . Sumitomocorp The demonstration validated the company’s investment in quantum technologies and positioned it at the forefront of quantum applications in finance.

Future Directions

Building on this success, the partners established plans to further advance quantum computing applications in financial risk management. Future work will focus on expanding the approach to handle larger, more complex financial portfolios and a wider range of risk assessment scenarios.

The partners also identified opportunities to apply similar quantum circuit optimization techniques to other computationally intensive financial applications, such as fraud detection, algorithmic trading, and portfolio optimization.

As quantum hardware continues to mature, with increases in qubit count and reductions in error rates, the optimized algorithms developed in this project are expected to deliver even greater performance improvements over classical methods. The groundwork laid by this collaboration positions all three companies to remain at the forefront of quantum finance applications as the technology evolves.

Sumitomo Corporation continues to expand its quantum computing initiatives through its QX project, collaborating with partners around the world to explore applications in various industries beyond that of finance, including mobility, manufacturing, and cybersecurity.