Unlocking new performance capability in quantum chemistry
The challenge
Quantum phase estimation (QPE) is a powerful tool for simulating quantum materials, but existing methods are too resource-intensive and error-prone to be useful on today’s quantum hardware.
Impact
90%
reduction in gate overheads for quantum chemistry simulations and 5X wider circuit.
The outcome
Mitsubishi Chemical Group used Fire Opal to dramatically reduce circuit complexity in QPE, achieving a 5X increase in computational capacity and 90% gate reduction. These results offer a path to large-scale quantum chemistry simulations on near-term devices.
Quantum computing is widely regarded as a game-changer for chemistry and materials science. Its ability to simulate molecular systems with quantum-level precision promises to unlock breakthroughs in drug discovery, energy materials, and industrial chemistry. While the potential for breakthroughs in drug discovery and energy materials is vast, achieving quantum advantage on today’s hardware remains a significant technical challenge.
Our collaboration with Mitsubishi Chemical Group marks a significant milestone in overcoming this challenge. Using Fire Opal, the team demonstrated a new approach to quantum phase estimation (QPE) that dramatically improves efficiency and scalability, enabling real simulations on today’s quantum hardware.
The limits of Quantum Phase Estimation (QPE)
Quantum phase estimation is one of the most important algorithms in quantum computing. It allows researchers to calculate the energies of molecules and materials, which is essential for understanding chemical reactivity, conductivity, and more.
However, QPE presents a range of practical challenges on today’s quantum hardware. It is computationally expensive due to its high gate overhead, making it difficult to execute within the constraints of current devices. The method is also highly sensitive to noise, with quantum errors degrading accuracy and limiting usefulness. As researchers aim to simulate larger systems, scalability becomes another major hurdle, and current quantum devices simply lack the capacity to support these workloads. Compounding these issues is the absence of large-scale fault tolerance, meaning there is no reliable way to correct errors at the scale required for complex simulations. Finally, mapping theoretically powerful algorithms like QPE onto noisy, intermediate-scale quantum (NISQ) hardware is a non-trivial task that requires careful engineering.
A new path: Quantum phase difference estimation (QPDE) with Fire Opal
To overcome the above challenge, Mitsubishi Chemical Group and co-authors developed a new algorithm based on quantum phase difference estimation (QPDE). The approach implements a tensor-based QPDE algorithm, which optimizes gate operations and reduces computational overhead. By leveraging tensor network-based unitary compression, the method improves noise resilience and scalability, enabling more efficient energy gap estimation for large systems on current quantum devices. This approach significantly reduces gate complexity, making it more feasible for noisy, intermediate-scale quantum (NISQ) hardware.
Using Fire Opal as an enabling layer, Mitsubishi Chemical Group and IBM's quantum device demonstrated a significant leap forward in computational efficiency with the successful execution of a novel QPDE algorithm. The number of CZ gates, a primary measure of circuit complexity, was reduced from needing 7,242 to just 794 in the final iteration of the 33-qubit demonstration. This represents a remarkable 90% reduction in gate overhead.
This improved efficiency led directly to a 5x increase in computational capacity over previous QPE methods, enabling the team to run wider and more complex quantum circuits. This represented a new world record in the largest QPE demonstration.
We achieved a significant leap forward in accurately calculating the physical properties of materials, demonstrating a five-fold increase in achievable circuit width over previous Quantum Phase Estimation studies. These results were enabled by Q-CTRL’s performance management, which made it possible to run deeper and wider circuits on IBM Quantum systems.Shu Kanno, Scientist, Mitsubishi Chemical Corporation
The impact of this work is substantial, as it enables larger-scale quantum simulations with improved efficiency. The proposed tensor-based QPDE algorithm reduces gate complexity by up to 5x and nearly ~90% improved efficiency compared to traditional QPE methods. This leads to better noise resilience and scalability, making it more feasible for near-term quantum hardware. The method allows for more accurate simulations of quantum materials and molecular systems, providing a significant step forward in quantum chemistry and material discovery, with potential applications in designing new materials and understanding complex quantum systems.
By lowering resource requirements and improving error tolerance, the solution significantly enhances the feasibility of running quantum chemistry workloads on today's quantum devices. This gives researchers a validated, scalable tool for exploring energy gaps in complex systems, accelerating foundational work in fields like drug discovery and energy materials.
The success of this project highlights how Fire Opal makes advanced quantum algorithms practical and productive today. Fire Opal handles optimization, noise-aware tuning, and hardware calibration automatically, empowering domain experts in chemistry and materials science to run high-fidelity simulations with minimal effort.
This work is part of a growing body of evidence that demonstrates how AI-powered infrastructure software is closing the gap between the promise of quantum computing and its actual performance.
Learn more about this work in the peer-reviewed published paper here [1].
This case study was co-presented by Q-CTRL and Mitsubishi Chemical Group at Q2B Tokyo 2025. Watch the recording here.
Reach out to our expert team today to explore how we can help you tackle your optimization challenges.
[1] S. Kanno,K. Sugisaki,H. Nakamura,H. Yamauchi,R. Sakuma,T. Kobayashi,Q. Gao, & N. Yamamoto, Tensor-based quantum phase difference estimation for large-scale demonstration, Proc. Natl. Acad. Sci. U.S.A. 122 (30) e2425026122, https://doi.org/10.1073/pnas.2425026122 (2025).
