QunaSys and Mitsubishi Electric create foundational algorithms for quantum CAE, cutting matrix workloads and stabilizing circuits

【Article Title】 QunaSys and Mitsubishi Electric create foundational algorithms for quantum CAE, cutting matrix workloads and stabilizing circuits 【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-mitsubishi-electric-quantum-cae-algorithms/) QunaSys and Mitsubishi Electric have jointly developed two foundational algorithms aimed at applying quantum computers to CAE used in product design and performance evaluation. One algorithm focuses on reducing the processing volumeprocessing volumeThroughput / ThroughputA metric indicating the amount of work that can be processed per unit time. In quantum computing, it represents how quickly circuits or jobs can be repeatedly executed, among other things.QI NoteThe definition and units of throughput vary between companies and systems. When comparing, check not only the raw number of executions but also conditions such as circuit size, accuracy, and wait times. of large matrix data, and the other aims to stably determine the parameters required for 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. design. ✍️ Quantum Index Analysis The background technical and business significance of this announcement, plus evaluation points that aren’t obvious from the numbers or headlines alone. Read our independent analysis ↓ Summary of the announcement CAE reproduces physical phenomena such as fluid flow, heat transfer, structural mechanics, and electromagnetic fields on a computer and is used for product design and performance evaluation. The joint development reported here focuses on reducing processing volume and improving computational accuracy when applying quantum computers to the large-matrix calculations common in CAE. The first algorithm reorganizes large matrix data into a form that can be processed efficiently on a quantum computer, reducing the quantum computational workload. The second algorithm stably determines the parameters needed for quantum circuit design, which leads to improved calculation accuracy. The companies cite potential future uses such as comparing many conditions in early-stage product design and performing high-precision performance evaluations that account for complex physical phenomena. Part of the results were produced under the Japan Science and Technology Agency (JST) Moonshot-type Research and Development Program (Goal 6). Key points • QunaSys and Mitsubishi Electric have jointly developed foundational algorithms for applying quantum computing to CAE • One algorithm reorganizes large matrix data for quantum computation, reducing required processing volume • The other algorithm stably determines parameters for quantum circuit design, improving calculation accuracy • Targeted simulations include fluid, thermal, structural, and electromagnetic analyses • Part of the work was supported by JST’s Moonshot-type R&D Program (Goal 6) Technical and business implications On the technical side, the announcement is significant because it presents foundational approaches addressing two core challenges for quantum computing in CAE: quantum processing volume and accuracy for large-scale matrix computations. On the business side, it concretizes potential applications of quantum computers in manufacturing, such as comparing many design conditions and performance evaluation. However, the companies did not disclose performance on actual quantum hardware, any demonstrated advantage over classical computingclassical computingClassical Computing / Classical Computation / Classical Computing / Classical ComputationA computation method that uses bits of 0 and 1; the form of computation performed by the computers commonly used today.QI NoteUsed as a point of comparison with quantum computing, but it can encompass CPUs, GPUs, supercomputers, and specialized algorithms, so care should be taken about the conditions of comparison., or a timeline for adoption. At this stage, the results should be viewed as foundational technology development toward future practical use. What to watch next Going forward, attention will focus on whether quantitative results for processing volume and computational accuracy on real quantum hardware are published. Beyond comparisons with conventional CAE methods, it will be necessary to determine the problem sizes and conditions for which the approach is practical in areas such as fluid and structural analysis. Demonstrations and deployment examples that assume manufacturing environments will also be important factors in judging commercialization prospects. ✍️ Quantum Index Analysis Mitsubishi Electric does not appear to be treating 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. as a transient research theme. In June 2026 it signed an MOU with Quantinuum to explore the use of quantum computing for CAE, including CFD (https://www.mitsubishielectric.co.jp/ja/pr/2026/0602_rd/?utm_source=chatgpt.com) (see the Quantinuum press release (https://www.quantinuum.com/press-releases/quantinuum-signs-mou-with-mitsubishi-electric-to-launch-strategic-quantum-computing-partnership?utm_source=chatgpt.com)). Taken together with the results announced with QunaSys, this suggests a sustained effort to secure technologies that could allow future gate-model quantum computers to be used in product design. That said, the practicality of quantum CAE—especially for fluid dynamics—requires careful assessment. Fluid analysis isn’t solved by a single fast solution of linear equations; it typically involves iterative computations with nonlinearity and time evolution. When considering the end-to-end workflow that includes loading classical data into quantum states and reading out results, speedups inside a quantum algorithm do not necessarily translate into overall CAE acceleration. Moreover, accelerating product development isn’t only about making a single CAE run faster. In design optimization, CAE is run repeatedly while searching design conditions, so the number of CAE runs also heavily influences total computation time. In a public case study with Mazda and Fixstars Amplify, multiple-vehicle body design trials that previously required around 10,000–30,000 evaluations were reduced to about 1,000 runs while achieving equivalent or better results. That approach kept CAE itself unchanged and improved “which designs to try next.” This is an important axis for comparing quantum CAE. By the time quantum hardware reaches a practical scale, GPUs, HPC, numerical solvers, and AI techniques such as surrogate models and optimization will also have advanced. If the goal is to speed up overall product development, one must compare “making each run faster” versus “reducing the number of runs” on the same table. The value of the current foundational algorithms should likewise not be measured solely by reductions within quantum circuits. The question is whether end-to-end computation time—including state preparation, time evolution, and measurement—can outperform contemporary GPU/HPC. That is the real hurdle for quantum CAE to become a practical technology. Related articles • Xanadu and Mitsubishi Chemical move EUV materials research into phase two using quantum computing (https://en.quantumindex.jp/xanadu-mitsubishi-chemical-euv-quantum-research/) • QuEra and Zapata Quantum partner to jointly validate applications for fault-tolerant quantum computing (https://en.quantumindex.jp/quera-zapata-quantum-application-partnership/) • JIJ releases quantum programming language “Qamomile” v0.14.0 with resource estimation and multi-SDK conversion (https://en.quantumindex.jp/jij-qamomile-v0-14-0-quantum-programming-language/) Source Read the original announcement (https://qunasys.com/news/posts/20260901-project-ai) 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**. 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QunaSys and Mitsubishi Electric have jointly developed two foundational algorithms aimed at applying quantum computers to CAE used in product design and performance evaluation. One algorithm focuses on reducing the processing volumeprocessing volumeThroughput / ThroughputA metric indicating the amount of work that can be processed per unit time. In quantum computing, it represents how quickly circuits or jobs can be repeatedly executed, among other things.QI NoteThe definition and units of throughput vary between companies and systems. When comparing, check not only the raw number of executions but also conditions such as circuit size, accuracy, and wait times. of large matrix data, and the other aims to stably determine the parameters required for 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. design.

Summary of the announcement

CAE reproduces physical phenomena such as fluid flow, heat transfer, structural mechanics, and electromagnetic fields on a computer and is used for product design and performance evaluation. The joint development reported here focuses on reducing processing volume and improving computational accuracy when applying quantum computers to the large-matrix calculations common in CAE. The first algorithm reorganizes large matrix data into a form that can be processed efficiently on a quantum computer, reducing the quantum computational workload. The second algorithm stably determines the parameters needed for quantum circuit design, which leads to improved calculation accuracy. The companies cite potential future uses such as comparing many conditions in early-stage product design and performing high-precision performance evaluations that account for complex physical phenomena. Part of the results were produced under the Japan Science and Technology Agency (JST) Moonshot-type Research and Development Program (Goal 6).

Key points

• QunaSys and Mitsubishi Electric have jointly developed foundational algorithms for applying quantum computing to CAE
• One algorithm reorganizes large matrix data for quantum computation, reducing required processing volume
• The other algorithm stably determines parameters for quantum circuit design, improving calculation accuracy
• Targeted simulations include fluid, thermal, structural, and electromagnetic analyses
• Part of the work was supported by JST’s Moonshot-type R&D Program (Goal 6)

Technical and business implications

On the technical side, the announcement is significant because it presents foundational approaches addressing two core challenges for quantum computing in CAE: quantum processing volume and accuracy for large-scale matrix computations. On the business side, it concretizes potential applications of quantum computers in manufacturing, such as comparing many design conditions and performance evaluation. However, the companies did not disclose performance on actual quantum hardware, any demonstrated advantage over classical computingclassical computingClassical Computing / Classical Computation / Classical Computing / Classical ComputationA computation method that uses bits of 0 and 1; the form of computation performed by the computers commonly used today.QI NoteUsed as a point of comparison with quantum computing, but it can encompass CPUs, GPUs, supercomputers, and specialized algorithms, so care should be taken about the conditions of comparison., or a timeline for adoption. At this stage, the results should be viewed as foundational technology development toward future practical use.

What to watch next

Going forward, attention will focus on whether quantitative results for processing volume and computational accuracy on real quantum hardware are published. Beyond comparisons with conventional CAE methods, it will be necessary to determine the problem sizes and conditions for which the approach is practical in areas such as fluid and structural analysis. Demonstrations and deployment examples that assume manufacturing environments will also be important factors in judging commercialization prospects.

✍️ Quantum Index Analysis

Mitsubishi Electric does not appear to be treating 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. as a transient research theme. In June 2026 it signed an MOU with Quantinuum to explore the use of quantum computing for CAE, including CFD (see the Quantinuum press release). Taken together with the results announced with QunaSys, this suggests a sustained effort to secure technologies that could allow future gate-model quantum computers to be used in product design.

That said, the practicality of quantum CAE—especially for fluid dynamics—requires careful assessment. Fluid analysis isn’t solved by a single fast solution of linear equations; it typically involves iterative computations with nonlinearity and time evolution. When considering the end-to-end workflow that includes loading classical data into quantum states and reading out results, speedups inside a quantum algorithm do not necessarily translate into overall CAE acceleration.

Moreover, accelerating product development isn’t only about making a single CAE run faster. In design optimization, CAE is run repeatedly while searching design conditions, so the number of CAE runs also heavily influences total computation time. In a public case study with Mazda and Fixstars Amplify, multiple-vehicle body design trials that previously required around 10,000–30,000 evaluations were reduced to about 1,000 runs while achieving equivalent or better results. That approach kept CAE itself unchanged and improved “which designs to try next.”

This is an important axis for comparing quantum CAE. By the time quantum hardware reaches a practical scale, GPUs, HPC, numerical solvers, and AI techniques such as surrogate models and optimization will also have advanced. If the goal is to speed up overall product development, one must compare “making each run faster” versus “reducing the number of runs” on the same table.

The value of the current foundational algorithms should likewise not be measured solely by reductions within quantum circuits. The question is whether end-to-end computation time—including state preparation, time evolution, and measurement—can outperform contemporary GPU/HPC. That is the real hurdle for quantum CAE to become a practical technology.

Related articles

• Xanadu and Mitsubishi Chemical move EUV materials research into phase two using quantum computing
• QuEra and Zapata Quantum partner to jointly validate applications for fault-tolerant quantum computing
• JIJ releases quantum programming language “Qamomile” v0.14.0 with resource estimation and multi-SDK conversion

Source

Read the original announcement