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State preparation and measurement errors are commonly regarded asindistinguishable. The problem of distinguishing state preparation (SPAM)errors from measurement errors is important to the field of characterizingquantum processors. In this work, we propose a method to separatelycharacterize SPAM errors using a novel type of algorithmic cooling protocolcalled measurement-based algorithmic cooling (MBAC). MBAC assumes the abilityto perform (potentially imperfect) projective measurements on individualqubits, which is available on many modern quantum processors. We demonstratethat MBAC can significantly reduce state preparation error under realisticassumptions, with a small overhead that can be upper bounded by measurablequantities. Thus, MBAC can be a valuable tool not only for benchmarkingnear-term quantum processors, but also for improving the performance of quantumprocessors in an algorithmic manner.

Algorithmic cooling for resolving state preparation and measurement errors in quantum computing