Scalable Noise Characterization of Syndrome-Extraction Circuits with Averaged Circuit Eigenvalue Sampling
Characterizing the performance of noisy quantum circuits is central to the production of prototype quantum computers and can enable improved quantum error correction that exploits noise biases identified in a quantum device. We develop a scalable noise-characterization protocol suited to characteriz...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Physical Society
2025-02-01
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| Series: | PRX Quantum |
| Online Access: | http://doi.org/10.1103/PRXQuantum.6.010334 |
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| Summary: | Characterizing the performance of noisy quantum circuits is central to the production of prototype quantum computers and can enable improved quantum error correction that exploits noise biases identified in a quantum device. We develop a scalable noise-characterization protocol suited to characterizing the syndrome-extraction circuits of quantum error-correcting codes, a key component of fault-tolerant architectures. Our protocol builds upon averaged circuit eigenvalue sampling (ACES), a framework for noise-characterization experiments that simultaneously estimates the Pauli-error probabilities of all gates in a Clifford circuit and captures averaged spatial correlations between gates implemented simultaneously in the layers of the circuit. By rigorously analyzing the performance of noise-characterization experiments in the ACES framework, we derive a figure of merit for their expected performance, allowing us to optimize their experimental design and improve the precision to which we estimate noise given fixed experimental resources. We demonstrate the scalability and performance of our protocol through circuit-level numerical simulations of the entire noise-characterization procedure for the syndrome-extraction circuit of a distance-25 surface code with over 1000 qubits. Our results indicate that detailed noise-characterization methods are scalable to near-term quantum devices. |
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| ISSN: | 2691-3399 |