An iterative Bayesian variational quantum eigensolver with the von Mises-Fisher distribution

Abstract:We develop an iterative Bayesian variational quantum eigensolver in which the unknown ground state, parametrised as a real unit vector, is inferred through a von Mises--Fisher distribution sequentially updated from Hadamard-test measurements on a single ancilla qubit. Building on a companion paper that treated a single success outcome via exact moment matching, we generalise the scheme to condition on $m$ success outcomes for the likelihood function per iteration. Since exact moment matching becomes impractical for $m>1$, we exploit the large-concentration regime, in which a first-order expansion of the log-likelihood yields a closed-form von Mises--Fisher posterior with $m$ entering only through a multiplicative weight. We prove that the two convergence properties of the single-outcome scheme---non-decreasing overlap and non-decreasing concentration---are preserved provided $m$ lies within an explicitly characterised range. Based on the theory, we propose a measurement-based algorithm and illustrate the convergence on randomly generated three- and four-qubit Hamiltonian matrices, on $\mathrm{HeH}^+$, and on $\mathrm{H}_2$.

arXiv:2610.11466 [quant-ph]

(or arXiv:2610.11466v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2610.11466 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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