Variational quantum algorithm for measurement extraction from the Navier-Stokes, Einstein, Maxwell, B-type, Lin-Tsien, Camassa-Holm, DSW, H-S, KdV-B, non-homogeneous KdV, generalized KdV, KdV, translational KdV, sKdV, B-L and Airy equations
Pete Rigas

TL;DR
This paper explores a variational quantum algorithm (VQA) for solving a wide range of nonlinear PDEs, demonstrating its potential to produce classical-quality solutions for complex physical equations using noiseless quantum simulations.
Contribution
It extends the application of VQA to multiple nonlinear PDEs beyond previously studied equations, showcasing its versatility and potential in physics-related computational problems.
Findings
VQA can reliably solve Navier-Stokes and other complex PDEs.
Quantum simulations produce solutions comparable to classical methods.
The approach is demonstrated across diverse physical equations.
Abstract
Classical-quantum hybrid algorithms have recently garnered significant attention, which are characterized by combining quantum and classical computing protocols to obtain readout from quantum circuits of interest. Recent progress due to Lubasch et al in a 2019 paper provides readout for solutions to the Schrodinger and Inviscid Burgers equations, by making use of a new variational quantum algorithm (VQA) which determines the ground state of a cost function expressed with a superposition of expectation values and variational parameters. In the following, we analyze additional computational prospects in which the VQA can reliably produce solutions to other PDEs that are comparable to solutions that have been previously realized classically, which are characterized with noiseless quantum simulations. To determine the range of nonlinearities that the algorithm can process for other IVPs, we…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum Information and Cryptography · Computational Physics and Python Applications
