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Noisy intermediate-scale quantum simulation of the one-dimensional wave equation

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arxiv 2402.19247 v3 pith:WGP76ZS2 submitted 2024-02-29 quant-ph math-phmath.MPphysics.app-phphysics.class-phphysics.comp-ph

Noisy intermediate-scale quantum simulation of the one-dimensional wave equation

classification quant-ph math-phmath.MPphysics.app-phphysics.class-phphysics.comp-ph
keywords quantumequationsimulationwaveapproachone-dimensionalacrossalgorithmic
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We design and implement quantum circuits for the simulation of the one-dimensional wave equation on the Quantinuum H1-1 quantum computer. The circuit depth of our approach scales as $O(n^{2})$ for $n$ qubits representing the solution on $2^{n}$ grid points, and leads to infidelities of $O(2^{-4n} t^{2})$ for simulation time $t$ assuming smooth initial conditions. By varying the qubit count we study the interplay between the algorithmic and physical gate errors to identify the optimal working point of minimum total error. Our approach to simulating the wave equation can be used with appropriate state preparation algorithms across different quantum processors and serve as an application-oriented benchmark.

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Cited by 3 Pith papers

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

  1. Structure-Preserving Quantum Simulation of Wave Equations on a Trapped-Ion Processor

    quant-ph 2026-07 conditional novelty 6.0

    On Quantinuum H2-2, Fourier-based structure-preserving circuits resolve subdomain kinetic-energy dynamics for structured 1D/2D acoustic and Dirac wave problems up to 4096 encoded degrees of freedom with MAE ~0.006–0.024.

  2. Approximate Hamiltonian Simulation Algorithm for Efficient Fluid Quantum Simulations

    quant-ph 2026-04 unverdicted novelty 4.0

    An approximate Hamiltonian simulation algorithm reduces circuit depth for quantum fluid dynamics from O(n²) to O(n) while preserving macroscopic density and momentum evolution in 10-qubit numerical tests.

  3. A Quantum Path to Partial Differential Equations

    quant-ph 2026-07 accept novelty 3.5

    Lecture notes that organize quantum PDE algorithms around block encodings of finite-difference and finite-element operators, tracking discretization, preparation, normalization, postselection, and measurement costs.