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Quantum Error Correction Fault Tolerance Quantum Machine Learning

QDB: From Quantum Algorithms Towards Correct Quantum Programs

arXiv
Authors: Yipeng Huang, Margaret Martonosi

Year

2018

Paper ID

23378

Status

Preprint

Abstract Read

~2 min

Abstract Words

141

Citations

N/A

Abstract

With the advent of small-scale prototype quantum computers, researchers can now code and run quantum algorithms that were previously proposed but not fully implemented. In support of this growing interest in quantum computing experimentation, programmers need new tools and techniques to write and debug QC code. In this work, we implement a range of QC algorithms and programs in order to discover what types of bugs occur and what defenses against those bugs are possible in QC programs. We conduct our study by running small-sized QC programs in QC simulators in order to replicate published results in QC implementations. Where possible, we cross-validate results from programs written in different QC languages for the same problems and inputs. Drawing on this experience, we provide a taxonomy for QC bugs, and we propose QC language features that would aid in writing correct code.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2018 reference point for readers tracking recent quantum research.
  • With the advent of small-scale prototype quantum computers, researchers can now code and run quantum algorithms that were previously proposed but not fully implemented.

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Current Paper #23378 #69034 Hardware-aware Low-latency Quan... #69036 CARVE-Q: Quantum-Proposed, Clas... #69025 Machine-Learning Optimization a... #69003 QBugLM: An Agentic Benchmarking...

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