TY - JOUR

T1 - Hybrid quantum algorithms for flow problems

AU - Bharadwaj, Sachin S.

AU - Sreenivasan, Katepalli R.

N1 - Publisher Copyright:
© 2023 the Author(s).

PY - 2023

Y1 - 2023

N2 - For quantum computing (QC) to emerge as a practically indispensable computational tool, there is a need for quantum protocols with end-to-end practical applications-in this instance, fluid dynamics. We debut here a high-performance quantum simulator which we term QFlowS (Quantum Flow Simulator), designed for fluid flow simulations using QC. Solving nonlinear flows by QC generally proceeds by solving an equivalent infinite dimensional linear system as a result of linear embedding. Thus, we first choose to simulate two well-known flows using QFlowS and demonstrate a previously unseen, full gate-level implementation of a hybrid and high precision Quantum Linear Systems Algorithms (QLSA) for simulating such flows at low Reynolds numbers. The utility of this simulator is demonstrated by extracting error estimates and power law scaling that relates T0 (a parameter crucial to Hamiltonian simulations) to the condition number _ of the simulation matrix and allows the prediction of an optimal scaling parameter for accurate eigenvalue estimation. Further, we include two speedup preserving algorithms for a) the functional form or sparse quantum state preparation and b) in situ quantum postprocessing tool for computing nonlinear functions of the velocity field. We choose the viscous dissipation rate as an example, for which the end-to-end complexity is shown to be O(polylog(N/ϵ)κ/ ϵQPP), where N is the size of the linear system of equations, ϵ is the solution error, and ϵQPP is the error in postprocessing. This work suggests a path toward quantum simulation of fluid flows and highlights the special considerations needed at the gate-level implementation of QC.

AB - For quantum computing (QC) to emerge as a practically indispensable computational tool, there is a need for quantum protocols with end-to-end practical applications-in this instance, fluid dynamics. We debut here a high-performance quantum simulator which we term QFlowS (Quantum Flow Simulator), designed for fluid flow simulations using QC. Solving nonlinear flows by QC generally proceeds by solving an equivalent infinite dimensional linear system as a result of linear embedding. Thus, we first choose to simulate two well-known flows using QFlowS and demonstrate a previously unseen, full gate-level implementation of a hybrid and high precision Quantum Linear Systems Algorithms (QLSA) for simulating such flows at low Reynolds numbers. The utility of this simulator is demonstrated by extracting error estimates and power law scaling that relates T0 (a parameter crucial to Hamiltonian simulations) to the condition number _ of the simulation matrix and allows the prediction of an optimal scaling parameter for accurate eigenvalue estimation. Further, we include two speedup preserving algorithms for a) the functional form or sparse quantum state preparation and b) in situ quantum postprocessing tool for computing nonlinear functions of the velocity field. We choose the viscous dissipation rate as an example, for which the end-to-end complexity is shown to be O(polylog(N/ϵ)κ/ ϵQPP), where N is the size of the linear system of equations, ϵ is the solution error, and ϵQPP is the error in postprocessing. This work suggests a path toward quantum simulation of fluid flows and highlights the special considerations needed at the gate-level implementation of QC.

KW - computational fluid mechanics (CFD)

KW - fluid dynamics

KW - quantum computation

KW - quantum linear systems algorithms (QLSA)

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U2 - 10.1073/pnas.2311014120

DO - 10.1073/pnas.2311014120

M3 - Article

C2 - 38039273

AN - SCOPUS:85178379931

SN - 0027-8424

VL - 120

JO - Proceedings of the National Academy of Sciences of the United States of America

JF - Proceedings of the National Academy of Sciences of the United States of America

M1 - e2311014120

ER -