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Quantum Physics

arXiv:2301.10196 (quant-ph)
[Submitted on 24 Jan 2023 (v1), last revised 8 Jun 2023 (this version, v3)]

Title:Overlap-ADAPT-VQE: Practical Quantum Chemistry on Quantum Computers via Overlap-Guided Compact Ansätze

Authors:César Feniou, Muhammad Hassan, Diata Traoré, Emmanuel Giner, Yvon Maday, Jean-Philip Piquemal
View a PDF of the paper titled Overlap-ADAPT-VQE: Practical Quantum Chemistry on Quantum Computers via Overlap-Guided Compact Ans\"atze, by C\'esar Feniou and 5 other authors
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Abstract:ADAPT-VQE is a robust algorithm for hybrid quantum-classical simulations of quantum chemical systems on near-term quantum computers. While its iterative process systematically reaches the ground state energy, ADAPT-VQE is sensitive to local energy minima, leading to over-parameterized ansätze. We introduce the Overlap-ADAPT-VQE to grow wave-functions by maximizing their overlap with any intermediate target wave-function that already captures some electronic correlation. By avoiding building the ansatz in the energy landscape strewn with local minima, the Overlap-ADAPT-VQE produces ultra-compact ansätze suitable for high-accuracy initializations of a new ADAPT procedure. Spectacular advantages over ADAPT-VQE are observed for strongly correlated systems including massive savings in circuit depth. Since this compression strategy can also be initialized with accurate Selected-Configuration Interaction (SCI) classical target wave-functions, it paves the way for chemically accurate simulations of larger systems, and strengthens the promise of decisively surpassing classical quantum chemistry through the power of quantum computing.
Subjects: Quantum Physics (quant-ph); Chemical Physics (physics.chem-ph)
Cite as: arXiv:2301.10196 [quant-ph]
  (or arXiv:2301.10196v3 [quant-ph] for this version)
  https://doi.org/10.48550/arXiv.2301.10196
arXiv-issued DOI via DataCite
Journal reference: Commun. Phys. 6 (2023) 192
Related DOI: https://doi.org/10.1038/s42005-023-01312-y
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Submission history

From: Jean-Philip Piquemal [view email]
[v1] Tue, 24 Jan 2023 18:10:58 UTC (2,658 KB)
[v2] Mon, 30 Jan 2023 17:58:01 UTC (2,707 KB)
[v3] Thu, 8 Jun 2023 06:34:24 UTC (5,837 KB)
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