QunaSys validates ~319M‑gate, 28‑qubit QPE circuit on 1,024 H100 GPUs, achieves chemical accuracy

【Article Title】 QunaSys validates ~319M‑gate, 28‑qubit QPE circuit on 1,024 H100 GPUs, achieves chemical accuracy 【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-qpe-simulation-1024-gpus/) QunaSys used 1,024 NVIDIA H100 GPUs to perform an end-to-end classical simulation of a quantum phase estimation (QPE) circuit for quantum chemistry, covering circuit construction through result analysis. For the four‑hydrogen model (H₄), the team generated roughly one million measurement samples from a 28‑qubitqubitQubit / Quantum Bit / QubitThe basic unit of information in a quantum computer. It can represent not only 0 or 1 but also a quantum state that is a superposition of them.QI NoteHaving more qubits does not necessarily mean higher performance. Error rates, connectivity, coherence time, and the number of logical qubits are also important., approximately 319‑million‑gate circuit and achieved chemical accuracy. ✍️ Quantum Index Analysis We explain the technical and business significance behind the announcement and the evaluation points that numbers and headlines alone may not reveal. Read our analysis ↓ Overview The verification used QunaSys’s quantum software development platform QURI SDK Enterprise, NVIDIA cuQuantum, and AIST’s quantum–classical hybrid computing system ABCI‑Q. The target was qubitization‑based QPE, and the team processed circuit construction, compilation, GPU execution, and measurement‑distribution analysis end‑to‑end. The H₄ circuit was optimized from an initial configuration of 99 qubits and about 1.78 billion gates down to 28 qubits and about 319 million gates. Using 1,024 H100 GPUs, it took roughly 1.5 hours to generate about one million measurement samples; the difference between the estimated energy and the exact solution was 0.53 mHa, within the chemical‑accuracy benchmark of 1.6 mHa. Under looser tolerance conditions, the team also executed a circuit for a ten‑hydrogen model (H₁₀) consisting of 42 qubits and about 490,000 gates. In a performance evaluation using a 32‑qubit H₂ circuit, runtime on a single GPU was about 4,155 seconds, which dropped to about 24 seconds on 512 GPUs. Beyond 512 GPUs, further speedup plateaued because of increased inter‑GPU communication overhead. QunaSys says this verification represents, among publicly reported cases they reviewed as of 2026, the largest end-to-end classical simulation of qubitization‑type QPE in terms of both qubit count and gate count. The results are scheduled to be presented in the Research Posters track at the international conference SC26 in November 2026. Key points • The H₄ QPE circuit was reduced from an initial 99 qubits and about 1.78 billion gates to 28 qubits and about 319 million gates. • Using 1,024 H100 GPUs, it took roughly 1.5 hours to generate about one million measurement samples. • The difference between the estimated energy and the exact solution was 0.53 mHa, within the chemical‑accuracy benchmark of 1.6 mHa. • With relaxed error tolerance, the team also executed an H₁₀ circuit of 42 qubits and about 490,000 gates. • Parallel performance flattened beyond 512 GPUs due to inter‑GPU communication overhead, highlighting challenges for further scaling. Technical and business implications Technically, the significance lies in validating FTQCFTQCFault-Tolerant Quantum Computing / FTQCA method for future large-scale quantum computing that uses quantum error correction to allow correct computation to continue even when physical errors occur.QI NoteA demonstration of quantum error correction is not the same as realizing FTQC. Logical error rates, the number of physical qubits required, logical gate performance, and so on are important.‑targeted quantum chemistry algorithms—specifically QPE—end‑to‑end in a large‑scale GPU environment, covering not just parts of the circuit but the full workflow from construction and compilation to execution and measurement‑distribution analysis. This suggests the possibility of preparing baseline data for comparison with future quantum hardware results prior to access to actual machines. However, the work is a classical simulation and does not demonstrate performance on FTQC hardware. QunaSys has not disclosed commercial deployment plans for QURI SDK Enterprise or the impact of these results on customer acquisition or revenue. What to watch next The next focus is whether this approach can be extended to larger molecules and higher qubit counts while maintaining chemical accuracy. Key technical challenges include how much the inter‑GPU communication bottleneck can be mitigated as scale increases, and whether future quantum hardware will produce results consistent with these baseline classical simulations. On the business side, it will be important to see external adoption or concrete deployment examples of QURI SDK Enterprise as a development and verification platform for FTQC‑oriented algorithms. ✍️ Quantum Index Analysis The key evaluation point in this result is less the headline figure of a circuit exceeding 300 million gates and more the fact that a QPE algorithm intended for fault‑tolerant quantum computers (FTQCFTQCFault-Tolerant Quantum Computing / FTQCA method for future large-scale quantum computing that uses quantum error correction to allow correct computation to continue even when physical errors occur.QI NoteA demonstration of quantum error correction is not the same as realizing FTQC. Logical error rates, the number of physical qubits required, logical gate performance, and so on are important.) was validated consistently from circuit construction through measurement analysis. In particular, reducing the initial configuration from 99 qubitsqubitsQubit / Quantum Bit / QubitThe basic unit of information in a quantum computer. It can represent not only 0 or 1 but also a quantum state that is a superposition of them.QI NoteHaving more qubits does not necessarily mean higher performance. Error rates, connectivity, coherence time, and the number of logical qubits are also important. to 28 qubits and from about 1.78 billion gates to about 319 million gates is an important achievement that also reduces the computational resources that would be required on actual hardware. At the same time, the H₄ target is a small model that can be solved exactly on classical computers, so achieving chemical accuracy here is not itself of practical significance. This verification confirms that the quantum‑algorithm implementation runs correctly rather than demonstrating quantum speedup or practical quantum‑chemical computation. Also, the roughly 1.5 hours on 1,024 GPUs should be viewed as a representation of current classical simulation cost. Note that in March 2026, Osaka University and Fixstars reported running iterative QPE (IQPE) classical simulations using the same ABCI‑Q platform with up to 1,024 H100 GPUs, reporting circuit execution up to 41 qubits (https://qiqb.osaka-u.ac.jp/newstopics/pr20260312). ComparisonOsaka University & FixstarsQunaSysAnnouncement timingMarch 2026October 2026QPE variantIterative QPE (IQPE)Qubitization‑based QPECompute platform usedABCI‑Q, up to 1,024 H100 GPUsABCI‑Q, 1,024 H100 GPUsMain target moleculesH₂O, Fe₂S₂, etc.H₄, H₁₀Main achievementsExecuted circuits up to 41 qubits; calculations for a 42 spin‑orbital systemH₄ with 28 qubits and ~319M gates, achieving chemical accuracyTechnical focusParallel simulation of large circuitsLarge‑scale, high‑accuracy end‑to‑end validationPositioning of resultsScaling up molecules and quantum‑circuit sizesEstablishing an implementation and verification environment for FTQC‑oriented QPE Sources: Osaka University & Fixstars announcement (https://qiqb.osaka-u.ac.jp/newstopics/pr20260312), QunaSys announcement (https://qiqb.osaka-u.ac.jp/newstopics/pr20260312). Evaluation conditions differ, so qubit or gate counts alone cannot determine superiority. Because the two efforts use different QPE variants and evaluation conditions, simple comparison is not meaningful; QunaSys’s result should be seen as a large‑scale end‑to‑end validation of qubitization‑type QPE rather than merely a record for qubit count. The future focus will be how far target molecules can be expanded while maintaining chemical accuracy, and whether QURI SDK Enterprise will be adopted by external users as a development and verification platform for FTQC‑oriented algorithms. NETWORKSee the industry network around QunaSys → (/qi-network/) Related articles • QunaSys and Mazda to jointly research quantum CAE and CFD — exploring next steps after quantum‑inspired optimization (https://en.quantumindex.jp/qunasys-mazda-quantum-cae-cfd-research/) • Diraq argues quantum computers’ commercial value should be measured by “cost per logical operation” (https://en.quantumindex.jp/diraq-logical-operation-cost-metric/) • IonQ applies quantum generative models to SAR change detection, demonstrating conditions and limits of effectiveness (https://en.quantumindex.jp/ionq-quantum-generative-model-sar-change-detection/) Source Read the original announcement (https://qunasys.com/news/posts/20261020-project-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 used 1,024 NVIDIA H100 GPUs to perform an end-to-end classical simulation of a quantum phase estimation (QPE) circuit for quantum chemistry, covering circuit construction through result analysis. For the four‑hydrogen model (H₄), the team generated roughly one million measurement samples from a 28‑qubitqubitQubit / Quantum Bit / QubitThe basic unit of information in a quantum computer. It can represent not only 0 or 1 but also a quantum state that is a superposition of them.QI NoteHaving more qubits does not necessarily mean higher performance. Error rates, connectivity, coherence time, and the number of logical qubits are also important., approximately 319‑million‑gate circuit and achieved chemical accuracy.

Overview

The verification used QunaSys’s quantum software development platform QURI SDK Enterprise, NVIDIA cuQuantum, and AIST’s quantum–classical hybrid computing system ABCI‑Q. The target was qubitization‑based QPE, and the team processed circuit construction, compilation, GPU execution, and measurement‑distribution analysis end‑to‑end. The H₄ circuit was optimized from an initial configuration of 99 qubits and about 1.78 billion gates down to 28 qubits and about 319 million gates. Using 1,024 H100 GPUs, it took roughly 1.5 hours to generate about one million measurement samples; the difference between the estimated energy and the exact solution was 0.53 mHa, within the chemical‑accuracy benchmark of 1.6 mHa. Under looser tolerance conditions, the team also executed a circuit for a ten‑hydrogen model (H₁₀) consisting of 42 qubits and about 490,000 gates. In a performance evaluation using a 32‑qubit H₂ circuit, runtime on a single GPU was about 4,155 seconds, which dropped to about 24 seconds on 512 GPUs. Beyond 512 GPUs, further speedup plateaued because of increased inter‑GPU communication overhead. QunaSys says this verification represents, among publicly reported cases they reviewed as of 2026, the largest end-to-end classical simulation of qubitization‑type QPE in terms of both qubit count and gate count. The results are scheduled to be presented in the Research Posters track at the international conference SC26 in November 2026.

Key points

• The H₄ QPE circuit was reduced from an initial 99 qubits and about 1.78 billion gates to 28 qubits and about 319 million gates.
• Using 1,024 H100 GPUs, it took roughly 1.5 hours to generate about one million measurement samples.
• The difference between the estimated energy and the exact solution was 0.53 mHa, within the chemical‑accuracy benchmark of 1.6 mHa.
• With relaxed error tolerance, the team also executed an H₁₀ circuit of 42 qubits and about 490,000 gates.
• Parallel performance flattened beyond 512 GPUs due to inter‑GPU communication overhead, highlighting challenges for further scaling.

Technical and business implications

Technically, the significance lies in validating FTQCFTQCFault-Tolerant Quantum Computing / FTQCA method for future large-scale quantum computing that uses quantum error correction to allow correct computation to continue even when physical errors occur.QI NoteA demonstration of quantum error correction is not the same as realizing FTQC. Logical error rates, the number of physical qubits required, logical gate performance, and so on are important.‑targeted quantum chemistry algorithms—specifically QPE—end‑to‑end in a large‑scale GPU environment, covering not just parts of the circuit but the full workflow from construction and compilation to execution and measurement‑distribution analysis. This suggests the possibility of preparing baseline data for comparison with future quantum hardware results prior to access to actual machines. However, the work is a classical simulation and does not demonstrate performance on FTQC hardware. QunaSys has not disclosed commercial deployment plans for QURI SDK Enterprise or the impact of these results on customer acquisition or revenue.

What to watch next

The next focus is whether this approach can be extended to larger molecules and higher qubit counts while maintaining chemical accuracy. Key technical challenges include how much the inter‑GPU communication bottleneck can be mitigated as scale increases, and whether future quantum hardware will produce results consistent with these baseline classical simulations. On the business side, it will be important to see external adoption or concrete deployment examples of QURI SDK Enterprise as a development and verification platform for FTQC‑oriented algorithms.

✍️ Quantum Index Analysis

The key evaluation point in this result is less the headline figure of a circuit exceeding 300 million gates and more the fact that a QPE algorithm intended for fault‑tolerant quantum computers (FTQCFTQCFault-Tolerant Quantum Computing / FTQCA method for future large-scale quantum computing that uses quantum error correction to allow correct computation to continue even when physical errors occur.QI NoteA demonstration of quantum error correction is not the same as realizing FTQC. Logical error rates, the number of physical qubits required, logical gate performance, and so on are important.) was validated consistently from circuit construction through measurement analysis. In particular, reducing the initial configuration from 99 qubitsqubitsQubit / Quantum Bit / QubitThe basic unit of information in a quantum computer. It can represent not only 0 or 1 but also a quantum state that is a superposition of them.QI NoteHaving more qubits does not necessarily mean higher performance. Error rates, connectivity, coherence time, and the number of logical qubits are also important. to 28 qubits and from about 1.78 billion gates to about 319 million gates is an important achievement that also reduces the computational resources that would be required on actual hardware.

At the same time, the H₄ target is a small model that can be solved exactly on classical computers, so achieving chemical accuracy here is not itself of practical significance. This verification confirms that the quantum‑algorithm implementation runs correctly rather than demonstrating quantum speedup or practical quantum‑chemical computation. Also, the roughly 1.5 hours on 1,024 GPUs should be viewed as a representation of current classical simulation cost.

Note that in March 2026, Osaka University and Fixstars reported running iterative QPE (IQPE) classical simulations using the same ABCI‑Q platform with up to 1,024 H100 GPUs, reporting circuit execution up to 41 qubits.

Comparison	Osaka University & Fixstars	QunaSys
Announcement timing	March 2026	October 2026
QPE variant	Iterative QPE (IQPE)	Qubitization‑based QPE
Compute platform used	ABCI‑Q, up to 1,024 H100 GPUs	ABCI‑Q, 1,024 H100 GPUs
Main target molecules	H₂O, Fe₂S₂, etc.	H₄, H₁₀
Main achievements	Executed circuits up to 41 qubits; calculations for a 42 spin‑orbital system	H₄ with 28 qubits and ~319M gates, achieving chemical accuracy
Technical focus	Parallel simulation of large circuits	Large‑scale, high‑accuracy end‑to‑end validation
Positioning of results	Scaling up molecules and quantum‑circuit sizes	Establishing an implementation and verification environment for FTQC‑oriented QPE

Sources: Osaka University & Fixstars announcement, QunaSys announcement. Evaluation conditions differ, so qubit or gate counts alone cannot determine superiority.

Because the two efforts use different QPE variants and evaluation conditions, simple comparison is not meaningful; QunaSys’s result should be seen as a large‑scale end‑to‑end validation of qubitization‑type QPE rather than merely a record for qubit count.

The future focus will be how far target molecules can be expanded while maintaining chemical accuracy, and whether QURI SDK Enterprise will be adopted by external users as a development and verification platform for FTQC‑oriented algorithms.

NETWORKSee the industry network around QunaSys →

Related articles

• QunaSys and Mazda to jointly research quantum CAE and CFD — exploring next steps after quantum‑inspired optimization
• Diraq argues quantum computers’ commercial value should be measured by “cost per logical operation”
• IonQ applies quantum generative models to SAR change detection, demonstrating conditions and limits of effectiveness

Source

Read the original announcement