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Paul Argyle

Publications and source records attributed to Paul Argyle.

2 recordsLinked to original sources

Non-Unitary Quantum Butterfly Effect: Theory and Experiment

We present a generalization of the out of time order correlator (OTOC) for non-unitary systems. For a Haar-random brickwork qudit-circuit, peppered with local, projective measurement, we calculate the infinite-temperature, ensemble-average OTOC. Utilizing transfer matrix machinery, we find that the OTOC asymptotically decays exponentially and that the decay depends on the rate of measurement. In the thermodynamic limit, with sparse measurement, the OTOC approximately exhibits a Gaussian shape. Phenomenologically, this corresponds to the development of a causal scrambling front where unlike the unitary case, the signal decays in the bulk of the causal cone. We develop a Monte Carlo framework to compute the OTOC. We then estimate the OTOC directly via quantum simulation and on Quantinuum's trapped-ion quantum computer. We resolve the unitary and non-unitary OTOC phenomenology despite the presence of hardware noise. Our analytic results characterize how local nonunitary channels affect OTOC decay, providing a quantitative benchmark for the cumulative effects of a broad class of hardware noise on many-body dynamics.

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Measurement-Induced Quantum Neural Network

We introduce a measurement-induced quantum neural network (MINN), an adaptive monitored-circuit architecture in which mid-circuit measurement outcomes determine the entangling gates in subsequent layers. In contrast to standard monitored circuits where sites and gates are sampled randomly, the gates are parametrized and variational, producing correlated history-dependent dynamics and injecting nonlinearity through measurement back-action. A generic MINN is not expected to be efficiently classically simulable. To demonstrate feasibility, we study a matchgate MINN that admits exact fermionic simulation and can be trained with gradient estimators. We apply the architecture to continuous optimization, image classification, and ground-state search in the Sherrington-Kirkpatrick spin glass, finding effective training and performance over a broad range of monitoring rates.

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