Papers
Topics
Authors
Recent
Search
2000 character limit reached

Arbitrary Boundary Conditions and Constraints in Quantum Algorithms for Differential Equations via Penalty Projections

Published 26 Jun 2025 in quant-ph | (2506.21751v1)

Abstract: Complicated boundary conditions are essential to accurately describe phenomena arising in nature and engineering. Recently, the investigation of a potential speedup through quantum algorithms in simulating the governing ordinary and partial differential equations of such phenomena has gained increasing attention. We design an efficient quantum algorithms for solving differential equations with arbitrary boundary conditions. Specifically, we propose an approach to enforce arbitrary boundary conditions and constraints through adding a penalty projection to the governing equations. Assuming a fast-forwardable representation of the projection to ensure an efficient interaction picture imulation, the cost of to enforce the constraints is at most O(logλ)O(\log\lambda) in the strength of the penalty λ\lambda in the gate complexity; in the worst case, this goes as O([v(0)<sup>2A0</sup>+bL<sup>1[0;t]<sup>2)]t<sup>2/ε)O([|v(0)|<sup>2|A_0|</sup> + |b|_{L<sup>1[0;t]}<sup>2)]t<sup>2/\varepsilon), for precision ε\varepsilon and a dynamical system ddtv(t)=A0(t)v(t)+b(t)\frac{\rm d}{{\rm d}t} v(t) = A_0(t) v(t) + b(t) with negative semidefinite A0(t)A_0(t) of size n<sup>d×</sup>n<sup>dn<sup>d\times</sup> n<sup>d. E.g., for the heat equation, this leads to a gate complexity overhead of O~(dlogn+logt)\widetilde O(d\log n + \log t). We show constraint error bounds for the penalty approach and provide validating numerical experiments, and estimate the circuit complexity using the Linear Combination of Hamiltonian Simulation.

Summary

No one has generated a summary of this paper yet.

Paper to Video (Beta)

No one has generated a video about this paper yet.

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Continue Learning

We haven't generated follow-up questions for this paper yet.