Curator's Take
AI Commentary
This article delivers the first systematic, hardware‑aware benchmark of error‑mitigation techniques on a real IBM superconducting processor when the number of available shots is limited, showing exactly where mitigation pays off and where shot noise erodes any gain. By combining calibration‑driven qubit selection, depth scaling, zero‑noise extrapolation, dynamical decoupling and readout correction within Qiskit Runtime, the authors provide a practical recipe that can be adopted by developers to squeeze more accuracy out of today’s noisy devices. The study also clarifies how measurement budgeting—splitting shots across repeated runs versus deeper circuits—affects expectation‑value errors, offering concrete guidance for near‑term applications in simulation and sensing. Readers should care because these insights bridge the gap between theoretical mitigation promises and the realities of limited hardware resources, helping to accelerate useful quantum computations now rather than waiting for fault tolerance.
— Mark Eatherly
Summary
We experimentally study error mitigation and finite-shot sampling on superconducting quantum hardware under a constrained execution budget. The study combines calibration-aware qubit selection, circuit-depth scaling, zero-noise extrapolation, dynamical decoupling, readout-error mitigation, and repeated-shot estimation on an IBM Quantum processor. Experiments are organized across ideal simulation, noise-model simulation, and physical-device execution to separate sampling uncertainty from device-induced error. We investigate how mitigation performance changes with circuit depth, effective noise scale, qubit connectivity, and measurement budget, and quantify accuracy using expectation-value error, mean-squared error, statistical uncertainty, and mitigation gain. A fixed hardware-execution budget is used to evaluate shot allocation strategies and repeated measurements without relying on unlimited sampling. The resulting analysis provides a hardware-aware characterization of when mitigation improves expectation-value estimation and when finite-shot fluctuations offset the benefit of additional mitigation overhead. The implementation uses contemporary Qiskit and IBM Quantum Runtime workflows and is designed to provide reproducible experimental evidence for error-mitigation studies on current quantum processors.