Pith. sign in

REVIEW 2 cited by

Quantum Computation of Fluid Dynamics

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2007.09147 v1 pith:4DLXTLQA submitted 2020-07-17 quant-ph physics.comp-phphysics.flu-dyn

classification quant-phphysics.comp-phphysics.flu-dyn
keywords quantumcomputingalgorithmscomputationaldynamicsfluidadventapproaches
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

Studies of strongly nonlinear dynamical systems such as turbulent flows call for superior computational prowess. With the advent of quantum computing, a plethora of quantum algorithms have demonstrated, both theoretically and experimentally, more powerful computational possibilities than their classical counterparts. Starting with a brief introduction to quantum computing, we will distill a few key tools and algorithms from the huge spectrum of methods available, and evaluate possible approaches of quantum computing in fluid dynamics.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

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

  1. Adaptive Lattice Gas Algorithm: Classical and Quantum implementations

    quant-ph 2025-04 conditional novelty 6.0 of 10

    A 1D adaptive integer lattice gas with velocity-dependent collision fractions reproduces lattice Boltzmann equilibrium and cosine-wave dynamics, and can be encoded in log(N)+3 qubits with a linear collision operator.

  2. A multi-ansatz variational quantum solver for compressible flows

    physics.flu-dyn 2025-08 conditional novelty 5.0 of 10

    A multi-ansatz variational quantum linear solver, tested on a quantum simulator, reproduces classical 1D shock-tube solutions and benefits from additional ansatz branches and domain decomposition.

Pith tools