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Quantum Machine Learning

Designing Hash and Encryption Engines using Quantum Computing

arXiv
Authors: Suryansh Upadhyay, Rupshali Roy, Swaroop Ghosh

Year

2023

Paper ID

57174

Status

Preprint

Abstract Read

~2 min

Abstract Words

138

Citations

N/A

Abstract

Quantum computing (QC) holds the promise of revolutionizing problem-solving by exploiting quantum phenomena like superposition and entanglement. It offers exponential speed-ups across various domains, from machine learning and security to drug discovery and optimization. In parallel, quantum encryption and key distribution have garnered substantial interest, leveraging quantum engines to enhance cryptographic techniques. Classical cryptography faces imminent threats from quantum computing, exemplified by Shors algorithms capacity to breach established encryption schemes. However, quantum circuits and algorithms, capitalizing on superposition and entanglement, offer innovative avenues for enhancing security. In this paper we explore quantum-based hash functions and encryption to fortify data security. Quantum hash functions and encryption can have numerous potential application cases, such as password storage, digital signatures, cryptography, anti-tampering etc. The integration of quantum and classical methods demonstrates potential in securing data in the era of quantum computing.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2023 reference point for readers tracking recent quantum research.
  • Quantum computing (QC) holds the promise of revolutionizing problem-solving by exploiting quantum phenomena like superposition and entanglement.

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