The cost of solving linear differential equations on a quantum computer: fast-forwarding to explicit resource counts

• URL: http://arxiv.org/abs/2309.07881v3
• Date: Tue, 05 Nov 2024 13:51:28 GMT
• Title: The cost of solving linear differential equations on a quantum computer: fast-forwarding to explicit resource counts
• Authors: David Jennings, Matteo Lostaglio, Robert B. Lowrie, Sam Pallister, Andrew T. Sornborger,
• Abstract summary: We give the first non-asymptotic computation of the cost of encoding the solution to general linear ordinary differential equations into quantum states. We show that the stability properties of a large class of classical dynamics allow their fast-forwarding. We find that the history state can always be output with complexity $O(T1/2)$ for any stable linear system.
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• Abstract: How well can quantum computers simulate classical dynamical systems? There is increasing effort in developing quantum algorithms to efficiently simulate dynamics beyond Hamiltonian simulation, but so far exact resource estimates are not known. In this work, we provide two significant contributions. First, we give the first non-asymptotic computation of the cost of encoding the solution to general linear ordinary differential equations into quantum states -- either the solution at a final time, or an encoding of the whole history within a time interval. Second, we show that the stability properties of a large class of classical dynamics allow their fast-forwarding, making their quantum simulation much more time-efficient. From this point of view, quantum Hamiltonian dynamics is a boundary case that does not allow this form of stability-induced fast-forwarding. In particular, we find that the history state can always be output with complexity $O(T^{1/2})$ for any stable linear system. We present a range of asymptotic improvements over state-of-the-art in various regimes. We illustrate our results with a family of dynamics including linearized collisional plasma problems, coupled, damped, forced harmonic oscillators and dissipative nonlinear problems. In this case the scaling is quadratically improved, and leads to significant reductions in the query counts after inclusion of all relevant constant prefactors.

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