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.

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Nature

A research paper introducing error-mitigated entanglement-enhanced learning for characterizing quantum noise processes at scale. The authors demonstrate that noisy quantum memory can still provide a learning advantage over entanglement-free approaches, with an experimental overhead of 1.33 ± 0.05 per qubit — below the no-entanglement lower bound of 2. Experiments were conducted on a superconducting processor using up to 64 qubits for hypothesis testing and up to 16 qubits for learning intrinsic noise in parallel-gate layers.

Entanglement-enhanced learning of quantum processes at scale