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Trapped Ion Quantum Computing Quantum Machine Learning Quantum Simulation

Grover Quantum Algorithm: Applications and Limits

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Authors: David R.C. Hill

Year

2026

Paper ID

48588

Status

Peer-reviewed

Abstract Read

~2 min

Abstract Words

104

Citations

0

Abstract

The Grover algorithm is a fundamental quantum algorithm that achieves a quadratic speedup for unstructured search problems, requiring O(√N) queries instead of O(N) classically. It works by repeatedly applying an oracle and a diffusion operator to amplify the probability of marked states. This advantage makes it relevant to cryptography, optimization, and constraint satisfaction and as a general primitive via amplitude amplification in areas like quantum machine learning and simulation. However, practical implementations are severely constrained by current noisy intermediate-scale quantum (NISQ) machines with limited coherence, deep oracle circuits, and lack of scalable Quantum RAM, restricting demonstrations to small-scale experiments with reproducibility challenges.

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  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2026 reference point for readers tracking recent quantum research.
  • The Grover algorithm is a fundamental quantum algorithm that achieves a quadratic speedup for unstructured search problems, requiring O(√N) queries instead of O(N) classically.

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References & Citation Signals

[1] DOI https://doi.org/10.3390/encyclopedia6040089 [2] Publisher https://doi.org/10.3390/encyclopedia6040089

Local Citation Graph (Related-Paper Links)

Current Paper #48588 #69038 Physically Constrained Ensemble... #69034 Hardware-aware Low-latency Quan... #69023 Scalable Quantum Algorithms for... #69003 QBugLM: An Agentic Benchmarking...

External citation index: OpenAlex citation signal • updated 2026-06-14 05:17:14

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