Extension of Clifford Data Regression Methods for Quantum Error Mitigation

• URL: http://arxiv.org/abs/2411.16653v1
• Date: Mon, 25 Nov 2024 18:38:06 GMT
• Title: Extension of Clifford Data Regression Methods for Quantum Error Mitigation
• Authors: Jordi Pérez-Guijarro, Alba Pagès-Zamora, Javier R. Fonollosa,
• Abstract summary: This work investigates two variants of Clifford Data Regression, which introduce a non-trivial set of gates into the original circuit. The first variant uses multiple copies of the original circuit, while the second adds a layer of single-qubit rotations. The performance of these methods is evaluated through numerical experiments, demonstrating a reduction in root mean square error.
• Score: 1.9662978733004601
• License:
• Abstract: To address the challenge posed by noise in real quantum devices, quantum error mitigation techniques play a crucial role. These techniques are resource-efficient, making them suitable for implementation in noisy intermediate-scale quantum devices, unlike the more resource-intensive quantum error correction codes. A notable example of such a technique is Clifford Data Regression, which employs a supervised learning approach. This work investigates two variants of this technique, both of which introduce a non-trivial set of gates into the original circuit. The first variant uses multiple copies of the original circuit, while the second adds a layer of single-qubit rotations. Different characteristics of these methods are analyzed theoretically, such as their complexity, or the scaling of the error with various parameters. Additionally, the performance of these methods is evaluated through numerical experiments, demonstrating a reduction in root mean square error.

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