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Quantum Machine Learning

The Quantum House Of Cards

arXiv
Authors: Xavier Waintal

Year

2023

Paper ID

52961

Status

Preprint

Abstract Read

~2 min

Abstract Words

120

Citations

N/A

Abstract

Quantum computers have been proposed to solve a number of important problems such as discovering new drugs, new catalysts for fertilizer production, breaking encryption protocols, optimizing financial portfolios, or implementing new artificial intelligence applications. Yet, to date, a simple task such as multiplying 3 by 5 is beyond existing quantum hardware. This article examines the difficulties that would need to be solved for quantum computers to live up to their promises. I discuss the whole stack of technologies that has been envisioned to build a quantum computer from the top layers (the actual algorithms and associated applications) down to the very bottom ones (the quantum hardware, its control electronics, cryogeny, etc.) while not forgetting the crucial intermediate layer of quantum error correction.

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 computers have been proposed to solve a number of important problems such as discovering new drugs, new catalysts for fertilizer production, breaking encryption...

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Current Paper #52961 #69034 Hardware-aware Low-latency Quan... #69025 Machine-Learning Optimization a... #69003 QBugLM: An Agentic Benchmarking... #68993 Tomography of quantum states wi...

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