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Quantum Information and Measurement (QIM)

Characterizing large-scale quantum devices

Joel J. Wallman, Joseph Emerson

Published Jan 1, 2019• 0 citations
Publisher

Abstract

Characterizing large-scale quantum devices is challenging because quantum noise processes are complex. We present a method for efficiently characterizing general operations in quantum devices with signal-to-noise ratios independent of the system size.
DOI
10.1364/qim.2019.s3b.2

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Efficiently characterizing large quantum states and processes is a central yet notoriously challenging task in quantum information science, as conventional tomography methods typically require resources that grow exponentially with system size. Here, we introduce a structure-agnostic learning framework for noisy nnn-qubit quantum circuits under~i.i.d.~single-qubit noise. We first prove that quantum states with unital noise channels admit an efficient learnable representation in the logarithmic-depth regime. We then extend this framework to quantum process tomography under constant noise, deriving a unified protocol that applies to both unital and non-unital noisy channels and retains efficient guarantees for logarithmic-depth circuits. This process-learning formulation is input-agnostic and imposes no distributional assumptions on the input quantum states. We further study a more general regime with arbitrary noise strength. In this setting, low-weight Pauli propagation induces a terminal truncation whose threshold depends logarithmically on the inverse accuracy, leading to quasi-polynomial complexity and near-unit success probability in the average case. In contrast to the preceding two results, this arbitrary-noise guarantee does not impose any restriction on the circuit depth, and therefore covers arbitrary-depth circuits, including both the noiseless limit (γ=0γ= 0γ=0) and the strong-decoherence regime (γ=Θ(1)γ= Θ(1)γ=Θ(1)). Numerical simulations of two-dimensional Hamiltonian dynamics further demonstrate the accuracy and robustness of the approach, including for structured circuits beyond the random-circuit setting assumed in the theoretical analysis. These results provide a scalable and practically relevant route toward characterizing large-scale noisy quantum devices, addressing a key bottleneck in the development of quantum technologies.
Characterizing quantum processes is essential for unlocking the potential of quantum devices. However, standard quantum process tomography is resource-intensive and becomes infeasible on large-scale systems. Despite alternative approaches have been successfully developed for specific scenarios, they typically rely on multi-qubit gates or extensive prior knowledge, limiting their practicability and scalability. To address these challenges and complement existing approaches, we introduce influence sampling\textit{influence sampling}influence sampling, an efficient and scalable protocol that quantifies the influence\textit{influence}influence of a quantum process on all qubit subsets using only single-qubit test gates, with sample complexity independent of system size. Using a photonic platform, we demonstrate influence sampling to identify high-influence qubits, reduce the full process to a smaller effective process, i.e., a junta approximation, and then learn it. We further confirm scalability by applying the protocol to a 24-qubit system and validate the junta approximation on a two-qubit process. These results establish influence sampling as a critical characterization technique, facilitating process learning and device assessment.