Pith. sign in

REVIEW 4 cited by

Variational Quantum Linear Solver

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 1909.05820 v4 pith:FVYIOSJQ submitted 2019-09-12 quant-ph

classification quant-ph
keywords quantumvqlslinearepsilonkapparanglesizecondition
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
abstract

Previously proposed quantum algorithms for solving linear systems of equations cannot be implemented in the near term due to the required circuit depth. Here, we propose a hybrid quantum-classical algorithm, called Variational Quantum Linear Solver (VQLS), for solving linear systems on near-term quantum computers. VQLS seeks to variationally prepare $|x\rangle$ such that $A|x\rangle\propto|b\rangle$. We derive an operationally meaningful termination condition for VQLS that allows one to guarantee that a desired solution precision $\epsilon$ is achieved. Specifically, we prove that $C \geq \epsilon^2 / \kappa^2$, where $C$ is the VQLS cost function and $\kappa$ is the condition number of $A$. We present efficient quantum circuits to estimate $C$, while providing evidence for the classical hardness of its estimation. Using Rigetti's quantum computer, we successfully implement VQLS up to a problem size of $1024\times1024$. Finally, we numerically solve non-trivial problems of size up to $2^{50}\times2^{50}$. For the specific examples that we consider, we heuristically find that the time complexity of VQLS scales efficiently in $\epsilon$, $\kappa$, and the system size $N$.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

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

  1. Noise Resilience of Variational Quantum Compiling

    quant-ph 2019-08 conditional novelty 7.0 of 10

    Variational quantum compiling's optimal parameters are provably unchanged by a broad class of incoherent noise, so noisy devices can still train the correct short-depth circuit.

  2. Parametrized-circuit-free quantum regression with variance regularization

    quant-ph 2026-07 accept novelty 6.0 of 10

    Symmetry-inspired fixed observables plus classical linear regression with variance regularization predict quantum properties without parameterized circuits and with lower resource cost than VQAs.

  3. Quantum solvability of noisy linear problems by divide-and-conquer strategy

    quant-ph 2019-08 reject novelty 6.0 of 10

    The divide-and-conquer LWE algorithm claims a NISQ-friendly polynomial speedup, but its success probability bound fails because the transformed noise scales with the superposed coefficient.

  4. Solving 1D Poisson problem with a Variational Quantum Linear Solver

    cs.CE 2024-12 conditional novelty 5.0 of 10

    A unitary decomposition using SWAP and center-switch gates reduces the number of terms needed to encode tridiagonal linear systems in the variational quantum linear solver, with first simulator and hardware demonstrat...

Pith tools