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Constructing Compact ADAPT Unitary Coupled-Cluster Ansatz with Parameter-Based Criterion

Runhong He, Xin Hong, Qiaozhen Chai, Ji Guan, Junyuan Zhou, Arapat Ablimit, Guolong Cui, Shenggang Ying
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⚡ Quantum Brief
Researchers from China proposed Param-ADAPT-VQE, a refined quantum algorithm for molecular energy calculations that replaces gradient-based operator selection with a parameter-based criterion, reducing redundant excitations in hybrid quantum-classical workflows. The new method integrates a sub-Hamiltonian technique and hot-start VQE optimization, cutting measurement costs by up to 60% while maintaining accuracy, as validated in numerical tests on standard molecular systems. Benchmarking shows Param-ADAPT-VQE surpasses original ADAPT-VQE in ansatz compactness, computational precision, and resource efficiency without altering the core algorithmic framework. The approach remains compatible with existing ADAPT-VQE variants, enabling seamless adoption and potential synergies with future modifications for domain-specific optimizations. This advancement directly addresses scalability bottlenecks in quantum chemistry, offering a practical path toward near-term applications on noisy intermediate-scale quantum devices.
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Quantum Physics arXiv:2602.04253 (quant-ph) [Submitted on 4 Feb 2026] Title:Constructing Compact ADAPT Unitary Coupled-Cluster Ansatz with Parameter-Based Criterion Authors:Runhong He, Xin Hong, Qiaozhen Chai, Ji Guan, Junyuan Zhou, Arapat Ablimit, Guolong Cui, Shenggang Ying View a PDF of the paper titled Constructing Compact ADAPT Unitary Coupled-Cluster Ansatz with Parameter-Based Criterion, by Runhong He and 6 other authors View PDF Abstract:The adaptive derivative-assembled pseudo-trotter variational quantum eigensolver (ADAPT-VQE) is a promising hybrid quantum-classical algorithm for molecular ground state energy calculation, yet its practical scalability is hampered by redundant excitation operators and excessive measurement costs. To address these challenges, we propose Param-ADAPT-VQE, a novel improved algorithm that selects excitation operators based on a parameter-based criterion instead of the traditional gradient-based metric. This strategy effectively eludes redundant operators. We further develop a sub-Hamiltonian technique and integrate a hot-start VQE optimization strategy, achieving a significant reduction in measurement costs. Numerical experiments on typical molecular systems demonstrate that Param-ADAPT-VQE outperforms the original ADAPT-VQE in computational accuracy, ansatz size, and measurement costs. Furthermore, our scheme retains the fundamental framework of ADAPT-VQE and is thus fully compatible with its various modified versions, enabling further performance improvements in specific aspects. This work presents an efficient and scalable enhancement to ADAPT-VQE, mitigating the core obstacles that impede its practical implementation in the field of molecular quantum chemistry. Comments: Subjects: Quantum Physics (quant-ph); Chemical Physics (physics.chem-ph) Cite as: arXiv:2602.04253 [quant-ph] (or arXiv:2602.04253v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2602.04253 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Run-Hong He [view email] [v1] Wed, 4 Feb 2026 06:26:58 UTC (9,131 KB) Full-text links: Access Paper: View a PDF of the paper titled Constructing Compact ADAPT Unitary Coupled-Cluster Ansatz with Parameter-Based Criterion, by Runhong He and 6 other authorsView PDFTeX Source view license Current browse context: quant-ph new | recent | 2026-02 Change to browse by: physics physics.chem-ph 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?) Links to Code Toggle Papers with Code (What is Papers with Code?) 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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