CAESER: Circuit Average Ensemble Series Expansion Rescaling

Achieving reliable quantum computation on noisy intermediate-scale devices requires robust quantum error mitigation (QEM) methods with controllable accuracy. However, most existing QEM techniques either rely on explicit noise characterization or leave residual bias that is difficult to quantify. We introduce CAESER (Circuit Average Ensemble Series Expansion Rescaling), a noise–model-free QEM protocol that provides bounded and systematically reducible bias for arbitrary quantum circuits. CAESER leverages the Clifford Perturbation Theory (CPT) representation of a target circuit to construct a classically simulatable ensemble of Clifford circuits whose expectation values can be combined with quantum measurements to estimate and correct residual bias. This hybrid quantum + HPC approach yields a provably asymptotically unbiased estimator without requiring noise learning or circuit-structure constraints. We experimentally demonstrate CAESER on large-scale random mirror circuits and Hamiltonian time-evolution benchmarks using IBM Heron quantum processors, showing significant accuracy improvements over both unmitigated and classically simulated results. CAESER exemplifies a general Boosted Error Mitigation framework that systematically improves the fidelity of error-mitigated observables, paving the way for an asymptotically bias-free quantum computation in the pre-fault-tolerant era.