QunaSys and Mazda start joint research on quantum CAE/CFD, exploring gate‑model methods after quantum‑inspired optimization

【Article Title】 QunaSys and Mazda start joint research on quantum CAE/CFD, exploring gate‑model methods after quantum‑inspired optimization 【Article Body】 AI TRANSLATION This article is an AI-generated translation of the original Japanese article. For the authoritative version, please refer to the original. Read the original Japanese article → (https://quantumindex.jp/qunasys-mazda-quantum-cae-cfd-research/) QunaSys on September 28, 2026 published details and future outlook for its joint research with Mazda Technical Research Center on quantum CAEquantum CAE量子CAE / Quantum CAE / Quantum Computer-Aided EngineeringThe concept of utilizing quantum computers and quantum algorithms for computations performed in CAE, such as simulations and optimizations.QI NoteAt present, rather than referring to a single established computational method, it is a framework that broadly covers the application of quantum computation to areas such as structural analysis and design optimization. When reviewing presentations or publications, check which parts use quantum computation and the scope of verification of actual speedup and effectiveness. and quantum CFD. They are examining algorithms and implementation methods for gate‑model quantum computers and accumulating knowledge needed for application to automotive development. ✍️ Quantum Index Analysis The technical and business significance behind this announcement, and evaluation points not obvious from numbers or headlines alone. Read our analysis ↓ Summary of the announcement In automotive development, CAE and CFD are used for fluid analysis, crash analysis, press‑forming analysis, and more. At the same time, increasing complexity and scale of products and analysis models have created challenges from growing computational scale and simulation time. In the joint research, the partners are investigating quantum algorithms and implementation methods to apply gate‑model quantum computers to CAE and CFD. In addition to a quantum‑computing education program, QunaSys provided theoretical material and implementation examples that include source code. Specifically, they are using the one‑dimensional advection equation as a test case. To combine Mazda’s in‑house algorithms and to enable subsequent research stages, researchers and engineers from both organizations are sharing knowledge while building the technical foundation. Both parties say that if quantum computers can accelerate CAE, it could enable simulations across more conditions and larger, higher‑accuracy analyses. However, the announcement does not include concrete performance improvements, the quantum hardware to be used, timelines for practical use, or business plans. Key points • QunaSys and Mazda Technical Research Center are conducting joint research on applying quantum computers to CAE and CFD • The focus is on algorithms and implementation methods for gate‑model quantum computers • QunaSys provided an education program, theory, and implementation examples including source code • They are using the one‑dimensional advection equation as a test case and are considering combinations with Mazda’s proprietary algorithms • No specific performance figures, hardware choices, timelines for practical deployment, or commercialization plans were disclosed Technical and business implications Technically, it is significant that the project is advancing not only algorithm exploration but also education, implementation, and knowledge sharing in parallel. Introducing quantum technologies to automotive CAE and CFD requires not only individual computational methods but also accumulated implementation capability, so this research can be seen as building that foundational capability. From a business perspective, this is an example of exploring applications that connect quantum computing to design and analysis processes in manufacturing. While future reductions in computation time and expansion of analysis scale are expected, the announcement alone does not allow judgment of superiority over conventional methods or effectiveness in practical environments. What to watch next The next focus will be whether insights gained from the one‑dimensional advection equation can be extended to more complex analyses closer to automotive development. Comparative data on speed and accuracy versus conventional methods, and execution results on quantum hardware, would be important evidence. It will also be necessary to watch for validation in realistic design workflows and whether software delivery and commercialization plans are concretized. ✍️ Quantum Index Analysis This quantum CAEquantum CAE量子CAE / Quantum CAE / Quantum Computer-Aided EngineeringThe concept of utilizing quantum computers and quantum algorithms for computations performed in CAE, such as simulations and optimizations.QI NoteAt present, rather than referring to a single established computational method, it is a framework that broadly covers the application of quantum computation to areas such as structural analysis and design optimization. When reviewing presentations or publications, check which parts use quantum computation and the scope of verification of actual speedup and effectiveness. research is easiest to interpret as the next stage of Mazda Technical Research Center’s efforts to utilize quantum technologies. In an interview (https://qunasys.com/case/posts/2026-mazda-cae/), Mr. Kodaira of QunaSys explained that Mazda started investigating quantum technologies around 2019 and intensified annealing‑based technology development in 2022–2023. Around the same time, Mazda used Fixstars Amplify (https://en.quantumindex.jp/tag/fixstars-amplify/) to pursue quantum annealingquantum annealingQuantum Annealing / Quantum AnnealingA computational method that uses quantum fluctuations to search for good solutions to combinatorial optimization problems and the like. Problems are solved by mapping them into a form where one searches for low-energy states.QI NoteQuantum annealing, unlike general-purpose gate-model quantum computers, is a scheme specialized for solving optimization problems. It was theoretically proposed in the 1990s by Hidetoshi Nishimori and others, and today D-Wave develops representative commercial systems. When evaluating performance, one should check not only the number of qubits but also how the problem is embedded and the comparison conditions with classical methods./Ising‑machine black‑box optimization, applying it to problems such as simultaneous body‑structure design for multiple vehicle models (https://amplify.fixstars.com/ja/customers/interview/vehicle-co-optimization), and presented results at academic venues. That optimization research has already progressed to problems close to real vehicle design. In the published case, for a multi‑objective constrained problem designing plate thicknesses at 222 locations across three vehicle models, it was reported that solutions found by NSGA‑II after 30,000 trials were outperformed in just 1,000 trials. Research continued afterward, and at the August 2026 Design Engineering and Systems Division conference (https://pub.confit.atlas.jp/ja/event/dsdconf26), Mazda and Fixstars Amplify jointly presented further advances in multi‑objective, multi‑constraint black‑box optimization. Compared with those efforts, gate‑model quantum CAE/CFD is at a much earlier stage. The current test case is the one‑dimensional advection equation, which is far from the three‑dimensional geometries, turbulence, and complex boundary conditions of real automotive CFD. In other words, Mazda is not treating “quantum” as a single, monolithic pursuit: quantum‑inspired optimization is being advanced toward real problems, while gate‑model quantum computing is being pursued in parallel as foundational research looking to the future. This distinction matters. To evaluate the value of quantum CAE, you need to look not at whether a one‑dimensional equation can be solved by a quantum circuitquantum circuit量子回路 / Quantum CircuitA representation of the computation procedure executed on a quantum computer, listing in order operations such as qubit initialization, quantum gate operations, and measurements.QI NoteEven for the same algorithm, the number of qubits used, circuit depth, and number of gates vary depending on the implementation. When comparing real-device performance, also check the circuit scale and the conditions after compilation., but at whether the circuit scales as problem size increases, total computation time including state preparation and measurement, and whether an advantage over classical CAE can be demonstrated. At the same time, because Mazda already has practical experience applying annealing to real design problems, its strategy to accumulate in‑house knowledge on gate‑model approaches now — rather than wait for practical devices — is consistent. NETWORKSee QunaSys’s industry network → (/qi-network/) Related articles • QunaSys and Mitsubishi Electric advance quantum CAE foundations: reducing large‑matrix processing and stabilizing circuit design (https://en.quantumindex.jp/qunasys-mitsubishi-electric-quantum-cae-algorithms/) • Classiq and INGL optimize natural‑gas transport with QAOA — device tests highlight challenges in QUBO conversion (https://en.quantumindex.jp/classiq-ingl-quantum-optimization-gas-network/) • PsiQuantum and Brookhaven National Laboratory collaborate on FTQC‑targeted algorithm research using “Construct” (https://en.quantumindex.jp/psiquantum-brookhaven-construct-partnership/) Source Read the original announcement (https://qunasys.com/news/posts/20260928-casestudy-ai-jp) If you found this article useful, please consider sharing it. ──────────────────── 【AI Evaluation Request】 Evaluate the following article critically, not based on the quality of its writing, but on its **original informational value as a specialist publication**. Do not give a high rating to summaries, paraphrases, explanations of technical terms, or generic observations such as "this will be worth watching" or "the timeline remains uncertain" if they could easily be produced by a general-purpose generative AI, even if they are well written. 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QunaSys on September 28, 2026 published details and future outlook for its joint research with Mazda Technical Research Center on quantum CAEquantum CAE量子CAE / Quantum CAE / Quantum Computer-Aided EngineeringThe concept of utilizing quantum computers and quantum algorithms for computations performed in CAE, such as simulations and optimizations.QI NoteAt present, rather than referring to a single established computational method, it is a framework that broadly covers the application of quantum computation to areas such as structural analysis and design optimization. When reviewing presentations or publications, check which parts use quantum computation and the scope of verification of actual speedup and effectiveness. and quantum CFD. They are examining algorithms and implementation methods for gate‑model quantum computers and accumulating knowledge needed for application to automotive development.

Summary of the announcement

In automotive development, CAE and CFD are used for fluid analysis, crash analysis, press‑forming analysis, and more. At the same time, increasing complexity and scale of products and analysis models have created challenges from growing computational scale and simulation time.

In the joint research, the partners are investigating quantum algorithms and implementation methods to apply gate‑model quantum computers to CAE and CFD. In addition to a quantum‑computing education program, QunaSys provided theoretical material and implementation examples that include source code.

Specifically, they are using the one‑dimensional advection equation as a test case. To combine Mazda’s in‑house algorithms and to enable subsequent research stages, researchers and engineers from both organizations are sharing knowledge while building the technical foundation.

Both parties say that if quantum computers can accelerate CAE, it could enable simulations across more conditions and larger, higher‑accuracy analyses. However, the announcement does not include concrete performance improvements, the quantum hardware to be used, timelines for practical use, or business plans.

Key points

• QunaSys and Mazda Technical Research Center are conducting joint research on applying quantum computers to CAE and CFD
• The focus is on algorithms and implementation methods for gate‑model quantum computers
• QunaSys provided an education program, theory, and implementation examples including source code
• They are using the one‑dimensional advection equation as a test case and are considering combinations with Mazda’s proprietary algorithms
• No specific performance figures, hardware choices, timelines for practical deployment, or commercialization plans were disclosed

Technical and business implications

Technically, it is significant that the project is advancing not only algorithm exploration but also education, implementation, and knowledge sharing in parallel. Introducing quantum technologies to automotive CAE and CFD requires not only individual computational methods but also accumulated implementation capability, so this research can be seen as building that foundational capability.

From a business perspective, this is an example of exploring applications that connect quantum computing to design and analysis processes in manufacturing. While future reductions in computation time and expansion of analysis scale are expected, the announcement alone does not allow judgment of superiority over conventional methods or effectiveness in practical environments.

What to watch next

The next focus will be whether insights gained from the one‑dimensional advection equation can be extended to more complex analyses closer to automotive development. Comparative data on speed and accuracy versus conventional methods, and execution results on quantum hardware, would be important evidence. It will also be necessary to watch for validation in realistic design workflows and whether software delivery and commercialization plans are concretized.

✍️ Quantum Index Analysis

This quantum CAEquantum CAE量子CAE / Quantum CAE / Quantum Computer-Aided EngineeringThe concept of utilizing quantum computers and quantum algorithms for computations performed in CAE, such as simulations and optimizations.QI NoteAt present, rather than referring to a single established computational method, it is a framework that broadly covers the application of quantum computation to areas such as structural analysis and design optimization. When reviewing presentations or publications, check which parts use quantum computation and the scope of verification of actual speedup and effectiveness. research is easiest to interpret as the next stage of Mazda Technical Research Center’s efforts to utilize quantum technologies. In an interview, Mr. Kodaira of QunaSys explained that Mazda started investigating quantum technologies around 2019 and intensified annealing‑based technology development in 2022–2023. Around the same time, Mazda used Fixstars Amplify to pursue quantum annealingquantum annealingQuantum Annealing / Quantum AnnealingA computational method that uses quantum fluctuations to search for good solutions to combinatorial optimization problems and the like. Problems are solved by mapping them into a form where one searches for low-energy states.QI NoteQuantum annealing, unlike general-purpose gate-model quantum computers, is a scheme specialized for solving optimization problems. It was theoretically proposed in the 1990s by Hidetoshi Nishimori and others, and today D-Wave develops representative commercial systems. When evaluating performance, one should check not only the number of qubits but also how the problem is embedded and the comparison conditions with classical methods./Ising‑machine black‑box optimization, applying it to problems such as simultaneous body‑structure design for multiple vehicle models, and presented results at academic venues.

That optimization research has already progressed to problems close to real vehicle design. In the published case, for a multi‑objective constrained problem designing plate thicknesses at 222 locations across three vehicle models, it was reported that solutions found by NSGA‑II after 30,000 trials were outperformed in just 1,000 trials. Research continued afterward, and at the August 2026 Design Engineering and Systems Division conference, Mazda and Fixstars Amplify jointly presented further advances in multi‑objective, multi‑constraint black‑box optimization.

Compared with those efforts, gate‑model quantum CAE/CFD is at a much earlier stage. The current test case is the one‑dimensional advection equation, which is far from the three‑dimensional geometries, turbulence, and complex boundary conditions of real automotive CFD. In other words, Mazda is not treating “quantum” as a single, monolithic pursuit: quantum‑inspired optimization is being advanced toward real problems, while gate‑model quantum computing is being pursued in parallel as foundational research looking to the future.

This distinction matters. To evaluate the value of quantum CAE, you need to look not at whether a one‑dimensional equation can be solved by a quantum circuitquantum circuit量子回路 / Quantum CircuitA representation of the computation procedure executed on a quantum computer, listing in order operations such as qubit initialization, quantum gate operations, and measurements.QI NoteEven for the same algorithm, the number of qubits used, circuit depth, and number of gates vary depending on the implementation. When comparing real-device performance, also check the circuit scale and the conditions after compilation., but at whether the circuit scales as problem size increases, total computation time including state preparation and measurement, and whether an advantage over classical CAE can be demonstrated. At the same time, because Mazda already has practical experience applying annealing to real design problems, its strategy to accumulate in‑house knowledge on gate‑model approaches now — rather than wait for practical devices — is consistent.

NETWORKSee QunaSys’s industry network →

Related articles

• QunaSys and Mitsubishi Electric advance quantum CAE foundations: reducing large‑matrix processing and stabilizing circuit design
• Classiq and INGL optimize natural‑gas transport with QAOA — device tests highlight challenges in QUBO conversion
• PsiQuantum and Brookhaven National Laboratory collaborate on FTQC‑targeted algorithm research using “Construct”

Source

Read the original announcement