Parity of repeated measurements realizes an amplified readout-error channel, enabling drift-resilient, characterization-free mitigation of mid-circuit and terminating measurement and preparation errors.
Mitigating errors in state preparation and measurement with noncomputational states
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abstract
Error mitigation has enabled quantum computing applications with over one hundred qubits and deep circuits. Many error mitigation methods are noise-aware, relying on a faithful characterization of the noise channels of the hardware. However, fundamental limitations lead to unlearnable degrees of freedom of the underlying noise models when considering qubits. Here, we show how to leverage non-computational states as an additional resource to learn state-preparation errors in superconducting qubits. This allows one to fully constrain the noise models. We can thus independently and accurately mitigate state-preparation errors, gate errors and measurement errors. Our proposed method is also applicable to dynamic circuits with mid-circuit measurements. This work opens the door to improved error mitigation for measurements, both at the end of the circuit and mid-circuit.
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Drift-resilient mid-circuit measurement and state preparation error mitigation for dynamic circuits
Parity of repeated measurements realizes an amplified readout-error channel, enabling drift-resilient, characterization-free mitigation of mid-circuit and terminating measurement and preparation errors.