Optimizing Unitary Coupled Cluster Wave Functions on Quantum Hardware: Error Bound and Resource-Efficient Optimizer

• URL: http://arxiv.org/abs/2410.15129v2
• Date: Fri, 25 Oct 2024 14:04:44 GMT
• Title: Optimizing Unitary Coupled Cluster Wave Functions on Quantum Hardware: Error Bound and Resource-Efficient Optimizer
• Authors: Martin Plazanet, Thomas Ayral,
• Abstract summary: We study the projective quantum eigensolver (PQE) approach to optimizing unitary coupled cluster wave functions on quantum hardware. The algorithm uses projections of the Schr"odinger equation to efficiently bring the trial state closer to an eigenstate of the Hamiltonian. We present numerical evidence of superiority over both the optimization introduced in arXiv:2102.00345 and VQE optimized using the Broyden Fletcher Goldfarb Shanno (BFGS) method.
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• Abstract: In this work, we study the projective quantum eigensolver (PQE) approach to optimizing unitary coupled cluster wave functions on quantum hardware, as introduced in arXiv:2102.00345. The projective quantum eigensolver is a hybrid quantum-classical algorithm which, by optimizing a unitary coupled cluster wave function, aims at computing the ground state of many-body systems. Instead of trying to minimize the energy of the system like in the variational quantum eigensolver, PQE uses projections of the Schr\"odinger equation to efficiently bring the trial state closer to an eigenstate of the Hamiltonian. In this work, we provide a mathematical study of the algorithm, which allows us to obtain a number of interesting results. We first show that one can derive a bound relating off-diagonal coefficients (residues) of the Hamiltonian to the error of the algorithm. This bound not only gives a formal motivation to the projective approach to optimizing unitary coupled cluster wavefunctions, but it also allows us to formulate a well-informed convergence criterion for residue-based optimizers. We then introduce a mathematical study of the classical optimization itself, and show that, using our results, one can derive a residue-based optimizer for which we present numerical evidence of superiority over both the optimization introduced in arXiv:2102.00345 and VQE optimized using the Broyden Fletcher Goldfarb Shanno (BFGS) method.

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