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Solving nonlinear differential equations on Quantum Computers: A Fokker-Planck approach

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arxiv 2401.13500 v1 pith:XHTLOTRQ submitted 2024-01-24 quant-ph nlin.CDphysics.comp-ph

classification quant-phnlin.CDphysics.comp-ph
keywords quantumnonlineardifferentialalgorithmsequationssolvingclassicalcomputers
verification ladder T0 review T1 audit T2 compute T3 formal
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For quantum computers to become useful tools to physicists, engineers and computational scientists, quantum algorithms for solving nonlinear differential equations need to be developed. Despite recent advances, the quest for a solver that can integrate nonlinear dynamical systems with a quantum advantage, whilst being realisable on available (or near-term) quantum hardware, is an open challenge. In this paper, we propose to transform a nonlinear dynamical system into a linear system, which we integrate with quantum algorithms. Key to the method is the Fokker-Planck equation, which is a non-normal partial differential equation. Three integration strategies are proposed: (i) Forward-Euler stepping by unitary block encoding; (ii) Schroedingerisation, and (iii) Forward-Euler stepping by linear addition of unitaries. We emulate the integration of prototypical nonlinear systems with the proposed quantum solvers, and compare the output with the benchmark solutions of classical integrators. We find that classical and quantum outputs are in good agreement. This paper opens opportunities for solving nonlinear differential equations with quantum algorithms.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Provable Oracle-Free Quantum Algorithm for Nonlinear Dynamics on Hybrid Oscillator-Qubit Processors

    quant-ph 2026-07 conditional novelty 7.0 of 10

    Oracle-free hybrid qubit–qumode circuits simulate polynomial-drift nonlinear ODEs via Fokker–Planck Schrödingerisation with O(d^{L+1} n^{L+2}) gates per Trotter step from an exact bipartite Pauli factorisation.

  2. Data-driven quantum Koopman method for simulating nonlinear dynamics

    quant-ph 2025-07 conditional novelty 6.0 of 10

    The paper introduces a data-driven Koopman method that represents nonlinear dynamics as unitary phase rotations in a learned embedding, with numerical tests on reaction-diffusion, shear flow, and 2D turbulence.

  3. Quantum Computing Technology Roadmaps and Capability Assessment for Scientific Computing -- An analysis of use cases from the NERSC workload

    quant-ph 2025-09 conditional novelty 2.0 of 10

    A NERSC analysis finds that more than 50% of its workload could ultimately benefit from quantum computing and that vendor roadmaps and quantum application requirements are projected to overlap in the next 5 to 10 years.

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