Year: 2026
Type: article
Abstract: Learning unknown noise processes in quantum systems reveals their physical origin and informs error suppression, mitigation, and correction. Characterizing a general quantum process requires exponentially many parameters inferred from noncommuting measurements. Because these measurements cannot be performed simultaneously, the sample complexity grows exponentially. For Pauli channels, quantum memory and entangling operations can transform this task into measurements of commuting observables, reducing complexity exponentially. However, noise in these resources increases the overhead, leaving open whether any advantage remains in realistic devices. Here, we introduce error-mitigated entanglement-enhanced learning, analyze it theoretically, and demonstrate it experimentally. We quantify the noise-induced overhead, perform hypothesis testing with up to 64 qubits, and learn intrinsic noise in parallel-gate layers using up to 16 qubits of a superconducting processor. We show that noisy quantum memory provides a learning advantage, with a current experimental overhead of 1.33 ± 0.05 per qubit, below the no-entanglement lower bound of 2. The authors show that entanglement with noisy quantum memory can significantly speed up learning of quantum processes. With error mitigation, their method characterizes quantum noise at scale and outperforms entanglement-free approaches.
Source: Nature Communications
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $6,990
Awards: G1404049065, G1432373144, QuSeC-TAQS: Nanoscale Covariance Magnetometry with Diamond Quantum Sensors, G1523888516, G2746316034, G2882288540, G3162985271, G3398237449, G3722242190, G431725244, G4516736450, QuIC - TaQS: Interconnects for a Superconducting-Atomic Hybrid Quantum Network, G4689434848, G4848018484, G4876042962, G5659690140, G6167497367, G6518383159, G6526646931, G6547089213, Kavli Institute for Theoretical Physics, G7420521838, G7452299184, NSF Engineering Research Center for Quantum Networks (CQN), Collaborative Research: FET: Medium: Design and Implementation of Quantum Databases, QII-TAQS: Quantum Metrological Platform for Single-Molecule Bio-Sensing
OpenAlex ID: W4403965012