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Generative Circuit Design for Quantum-Selected Configuration Interaction

arXiv Quantum Physics
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⚡ Quantum Brief
Researchers led by Ryota Kemmoku introduced a Transformer-powered framework to optimize quantum circuits for electronic ground-state calculations, achieving chemical precision with 98% fewer two-qubit gates than standard Trotterized methods. The team applied their Generative Quantum Eigensolver (GQE) approach to nitrogen (N₂) simulations in 32-qubit active spaces, demonstrating substantial hardware efficiency gains over qDRIFT and first-order Trotter approximations. Their method outperforms heat-bath configuration interaction (HCI) in strongly correlated regimes, reaching equivalent accuracy with 50% smaller subspaces—critical for noisy intermediate-scale quantum (NISQ) device practicality. The framework dynamically optimizes ansatz circuits by training on QSCI subspace energies, enabling adaptive state preparation tailored to molecular systems under hardware constraints. Validated in stretched-bond scenarios, the results suggest a scalable path for quantum chemistry simulations on near-term devices, addressing gate-count bottlenecks in variational quantum algorithms.
Generative Circuit Design for Quantum-Selected Configuration Interaction

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Quantum Physics arXiv:2604.09756 (quant-ph) [Submitted on 10 Apr 2026] Title:Generative Circuit Design for Quantum-Selected Configuration Interaction Authors:Ryota Kemmoku, Qi Gao, Shu Kanno, Kimberlee Keithley, Ikko Hamamura, Naoki Yamamoto, Kouhei Nakaji View a PDF of the paper titled Generative Circuit Design for Quantum-Selected Configuration Interaction, by Ryota Kemmoku and 6 other authors View PDF HTML (experimental) Abstract:Quantum-selected configuration interaction (QSCI) has emerged as a feasible approach for approximating electronic ground states on noisy quantum devices toward large-system demonstrations. In QSCI, Slater determinants are sampled from a quantum-prepared state, and the Hamiltonian is then diagonalized in the sampled subspace. To create a high-quality subspace under hardware constraints, the design of the state-preparation circuit is crucial. Here, we present a Generative Quantum Eigensolver (GQE)-based framework that optimizes ansatz structures using a Transformer policy trained on the QSCI subspace energy. We validate the framework on N2 in active spaces of up to 32 qubits. We found that the optimized circuits reach chemical precision with substantially lower gate counts than time-evolved circuits. Quantitatively, this corresponds to an average reduction of 98% in the required two-qubit gate count relative to the single-step first-order Trotterized approximation and 83% relative to the qDRIFT approximation. Furthermore, the resulting wavefunctions are competitive with heat-bath configuration interaction (HCI) in terms of compactness. In stretched-bond, strongly correlated regimes, they achieve chemical precision with subspaces that are 50% smaller than those required by HCI. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2604.09756 [quant-ph] (or arXiv:2604.09756v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2604.09756 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Ryota Kemmoku [view email] [v1] Fri, 10 Apr 2026 16:00:40 UTC (1,173 KB) Full-text links: Access Paper: View a PDF of the paper titled Generative Circuit Design for Quantum-Selected Configuration Interaction, by Ryota Kemmoku and 6 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-04 References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

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Source: arXiv Quantum Physics