Pith. sign in

REVIEW 1 cited by

Optimizing Quantum Search Using a Generalized Version of Grover's Algorithm

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 2005.06468 v2 pith:HS4VWP2G submitted 2020-05-13 quant-ph cs.ET

classification quant-phcs.ET
keywords statealgorithmquantumsearchstepsuperpositionall-zerosamplitude
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Grover's Search algorithm was a breakthrough at the time it was introduced, and its underlying procedure of amplitude amplification has been a building block of many other algorithms and patterns for extracting information encoded in quantum states. In this paper, we introduce an optimization of the inversion-by-the-mean step of the algorithm. This optimization serves two purposes: from a practical perspective, it can lead to a performance improvement; from a theoretical one, it leads to a novel interpretation of the actual nature of this step. This step is a reflection, which is realized by (a) cancelling the superposition of a general state to revert to the original all-zeros state, (b) flipping the sign of the amplitude of the all-zeros state, and finally (c) reverting back to the superposition state. Rather than canceling the superposition, our approach allows for going forward to another state that makes the reflection easier. We validate our approach on set and array search, and confirm our results experimentally on real quantum hardware.

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. Discovering Algorithms with Computational Language Processing

    cs.AI 2025-07 conditional novelty 5.0 of 10

    A machine learning framework called CLP discovers, improves, and tailors algorithms by chaining computational tokens with MCTS and RL, with strong results on the Quadratic Assignment Problem and quantum search.

Pith tools