Q-Cluster: Quantum Error Mitigation Through Noise-Aware Unsupervised Learning

• URL: http://arxiv.org/abs/2504.10801v1
• Date: Tue, 15 Apr 2025 01:53:39 GMT
• Title: Q-Cluster: Quantum Error Mitigation Through Noise-Aware Unsupervised Learning
• Authors: Hrushikesh Pramod Patil, Dror Baron, Huiyang Zhou,
• Abstract summary: Quantum error mitigation (QEM) is critical in reducing the impact of noise in quantum computing.<n>We propose a novel QEM approach, Q-Cluster, that uses unsupervised learning (clustering) to reshape the measured bit-string distribution.<n>We show that our proposed Q-Cluster scheme improves the fidelity by a factor of 1.46x, on average, compared to the unmitigated output distribution.
• Score: 4.984018914962973
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum error mitigation (QEM) is critical in reducing the impact of noise in the pre-fault-tolerant era, and is expected to complement error correction in fault-tolerant quantum computing (FTQC). In this paper, we propose a novel QEM approach, Q-Cluster, that uses unsupervised learning (clustering) to reshape the measured bit-string distribution. Our approach starts with a simplified bit-flip noise model. It first performs clustering on noisy measurement results, i.e., bit-strings, based on the Hamming distance. The centroid of each cluster is calculated using a qubit-wise majority vote. Next, the noisy distribution is adjusted with the clustering outcomes and the bit-flip error rates using Bayesian inference. Our simulation results show that Q-Cluster can mitigate high noise rates (up to 40% per qubit) with the simple bit-flip noise model. However, real quantum computers do not fit such a simple noise model. To address the problem, we (a) apply Pauli twirling to tailor the complex noise channels to Pauli errors, and (b) employ a machine learning model, ExtraTrees regressor, to estimate an effective bit-flip error rate using a feature vector consisting of machine calibration data (gate & measurement error rates), circuit features (number of qubits, numbers of different types of gates, etc.) and the shape of the noisy distribution (entropy). Our experimental results show that our proposed Q-Cluster scheme improves the fidelity by a factor of 1.46x, on average, compared to the unmitigated output distribution, for a set of low-entropy benchmarks on five different IBM quantum machines. Our approach outperforms the state-of-art QEM approaches M3 [24], Hammer [35], and QBEEP [33] by 1.29x, 1.47x, and 2.65x, respectively.

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