A generalized cycle benchmarking algorithm for characterizing mid-circuit measurements

Abstract

Mid-circuit measurements (MCMs) are crucial ingredients in the development of fault-tolerant quantum computation. While there have been rapid experimental progresses in realizing MCMs, a systematic method for characterizing noisy MCMs is still under exploration. In this work we develop a cycle benchmarking (CB)-type algorithm to characterize noisy MCMs. The key idea is to use a joint Fourier transform on the classical and quantum registers and then estimate parameters in the Fourier space, analogous to Pauli fidelities used in CB-type algorithms for characterizing the Pauli noise channel of Clifford gates. Furthermore, we develop a theory of the noise learnability of MCMs, which determines what information can be learned about the noise model (in the presence of state preparation and terminating measurement (SPAM) noise) and what cannot, which shows that all learnable information can be learned using our algorithm. As an application, we show how to use the learned information to test the independence between measurement noise and state preparation noise in an MCM. Finally, we conduct numerical simulations to illustrate the practical applicability of the algorithm. Similar to other CB-type algorithms, we expect the algorithm to provide a useful toolkit that is of experimental interest.

30 pages, 9 figures, 1 table

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Cited by in corpus (7)

• A Practical Introduction to Benchmarking and Characterization of Quantum Computers
• Scalable noise characterization of syndrome-extraction circuits with averaged circuit eigenvalue sampling
• Pauli Noise Learning for Mid-Circuit Measurements
• Efficient self-consistent learning of gate set Pauli noise
• Characterizing physical and logical errors in a transversal CNOT via cycle error reconstruction
• Mitigating errors in state preparation and measurement with noncomputational states
• Disambiguating Pauli noise in quantum computers