Improved parameter initialization for the (local) unitary cluster Jastrow ansatz

• URL: http://arxiv.org/abs/2511.22476v1
• Date: Thu, 27 Nov 2025 14:05:18 GMT
• Title: Improved parameter initialization for the (local) unitary cluster Jastrow ansatz
• Authors: Wan-Hsuan Lin, Fangchun Liang, Mario Motta, Haimeng Zhang, Kenneth M. Merz, Kevin J. Sung,
• Abstract summary: We propose two methods to improve the parameters of quantum algorithms for chemistry.<n>The first method, which is applicable to both expectation value- and sample-based algorithms, uses compressed double factorization of the CCSD amplitudes.<n>The second method, which is applicable to sample-based algorithms, uses approximate tensor network simulation to improve the quality of samples produced by the ansatz circuit.
• Score: 0.3017117582063482
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The unitary cluster Jastrow (UCJ) ansatz and its variant known as local UCJ (LUCJ) are promising choices for variational quantum algorithms for chemistry due to their combination of physical motivation and hardware efficiency. The parameters of these ansatzes can be initialized from the output of a coupled cluster, singles and doubles (CCSD) calculation performed on a classical computer. However, truncating the number of repetitions of the ansatz, as well as discarding interactions to accommodate the connectivity constraints of near-term quantum processors, degrade the approximation to CCSD and the resulting energy accuracy. In this work, we propose two methods to improve the parameter initialization. The first method, which is applicable to both expectation value- and sample-based algorithms, uses compressed double factorization of the CCSD amplitudes to improve or recover the CCSD approximation. The second method, which is applicable to sample-based algorithms, uses approximate tensor network simulation to improve the quality of samples produced by the ansatz circuit. We validate our methods using exact state vector simulation on systems of up to 52 qubits, as well as experiments on superconducting quantum processors using up to 65 qubits. Our results indicate that our methods can significantly improve the output of both expectation value- and sample-based quantum algorithms.

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