Q-Embroidery: A Study on Weaving Quantum Error Correction into the Fabric of Quantum Classifiers
CoRR(2024)
摘要
Quantum computing holds transformative potential for various fields, yet its
practical application is hindered by the susceptibility to errors. This study
makes a pioneering contribution by applying quantum error correction codes
(QECCs) for complex, multi-qubit classification tasks. We implement 1-qubit and
2-qubit quantum classifiers with QECCs, specifically the Steane code, and the
distance 3 5 surface codes to analyze 2-dimensional and 4-dimensional
datasets. This research uniquely evaluates the performance of these QECCs in
enhancing the robustness and accuracy of quantum classifiers against various
physical errors, including bit-flip, phase-flip, and depolarizing errors. The
results emphasize that the effectiveness of a QECC in practical scenarios
depends on various factors, including qubit availability, desired accuracy, and
the specific types and levels of physical errors, rather than solely on
theoretical superiority.
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