Pith. sign in

REVIEW 1 cited by

Reconsider HHL algorithm and its related quantum machine learning algorithms

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 1803.01486 v1 pith:GELXNQJT submitted 2018-03-05 quant-ph

classification quant-ph
keywords algorithmquantumalgorithmscaveatslearningmachinerelatedseveral
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

HHL quantum algorithm to solve linear systems is one of the most important subroutines in many quantum machine learning algorithms. In this work, we present and analyze several other caveats in HHL algorithm, which have been ignored in the past. Their influences on the efficiency, accuracy and practicability of HHL algorithm and several related quantum machine learning algorithms will be discussed. We also found that these caveats affect HHL algorithm much deeper than the already noticed caveats. In order to obtain more practical quantum machine learning algorithms with less assumptions based on HHL algorithm, we should pay more attention to these caveats.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

  1. Q-RESTORE: Quantum-Driven Framework for Resilient and Equitable Transportation Network Restoration

    cs.MA 2025-01 reject novelty 3.0 of 10

    A D-Wave hybrid solver is applied to post-disaster road restoration on a 24-zone Sioux Falls network, with claimed speed and equity advantages that the paper's own equations do not support.

Pith tools