A probabilistic quantum algorithm for Lyapunov equations and matrix inversion

• URL: http://arxiv.org/abs/2508.04689v1
• Date: Wed, 06 Aug 2025 17:52:06 GMT
• Title: A probabilistic quantum algorithm for Lyapunov equations and matrix inversion
• Authors: Marcello Benedetti, Ansis Rosmanis, Matthias Rosenkranz,
• Abstract summary: We present a probabilistic quantum algorithm for preparing mixed states proportional to the solutions of Lyapunov equations.<n>At each step the algorithm either returns the current state, applies a trace non-increasing completely positive map, or restarts depending on the outcomes of a biased coin flip and an ancilla measurement.<n>In its most general form, the algorithm generates mixed states which approximate matrix-valued weighted sums and integrals.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a probabilistic quantum algorithm for preparing mixed states which, in expectation, are proportional to the solutions of Lyapunov equations -- linear matrix equations ubiquitous in the analysis of classical and quantum dynamical systems. Building on previous results by Zhang et al., arXiv:2304.04526, at each step the algorithm either returns the current state, applies a trace non-increasing completely positive map, or restarts depending on the outcomes of a biased coin flip and an ancilla measurement. We introduce a deterministic stopping rule which leads to an efficient algorithm with a bounded expected number of calls to a block-encoding and a state preparation circuit representing the two input matrices of the Lyapunov equations. We also consider approximating the normalized inverse of a positive definite matrix $A$ with condition number $\kappa$ up to trace distance error $\epsilon$. For this special case the algorithm requires, in expectation, at most $\lceil \kappa\ln(1/\epsilon) \rceil+1$ calls to a block-encoding of $\sqrt{A/\|A\|}$. This matches the optimal query complexity in $\kappa$ and $\epsilon$ of the related, but distinct, quantum linear system solvers. In its most general form, the algorithm generates mixed states which approximate matrix-valued weighted sums and integrals.

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