Sample-efficient quantum error mitigation via classical learning surrogates
This addresses the scalability problem in quantum error mitigation for researchers and engineers working on near-term quantum computing, offering a promising path toward practical quantum utility, though it is incremental as it builds on existing ZNE techniques.
The paper tackled the high measurement overhead in quantum error mitigation (QEM) for near-term quantum processors by introducing surrogate-enabled zero-noise extrapolation (S-ZNE), which uses classical learning surrogates to perform ZNE classically, reducing measurement overhead from linear to constant for circuit families and demonstrating effectiveness in numerical experiments on up to 100-qubit tasks.
The pursuit of practical quantum utility on near-term quantum processors is critically challenged by their inherent noise. Quantum error mitigation (QEM) techniques are leading solutions to improve computation fidelity with relatively low qubit-overhead, while full-scale quantum error correction remains a distant goal. However, QEM techniques incur substantial measurement overheads, especially when applied to families of quantum circuits parameterized by classical inputs. Focusing on zero-noise extrapolation (ZNE), a widely adopted QEM technique, here we devise the surrogate-enabled ZNE (S-ZNE), which leverages classical learning surrogates to perform ZNE entirely on the classical side. Unlike conventional ZNE, whose measurement cost scales linearly with the number of circuits, S-ZNE requires only constant measurement overhead for an entire family of quantum circuits, offering superior scalability. Theoretical analysis indicates that S-ZNE achieves accuracy comparable to conventional ZNE in many practical scenarios, and numerical experiments on up to 100-qubit ground-state energy and quantum metrology tasks confirm its effectiveness. Our approach provides a template that can be effectively extended to other quantum error mitigation protocols, opening a promising path toward scalable error mitigation.