Dimension-adaptive machine-learning-based quantum state reconstruction

Lohani, Sanjaya; Regmi, Sangita; Lukens, Joseph M.; Glasser, Ryan T.; Searles, Thomas A.; Kirby, Brian T.

doi:10.48550/arxiv.2205.05804

Cited by 1 publication

(1 citation statement)

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“…This approach, which is also standard in MLE [68], ensures that our predicted matrices are always physical. Finally, while the approach described here requires prior knowledge of the dimension of the system it will be applied to, recent work has demonstrated an approach for performing any n qubit reconstruction using a reconstruction method designed explicitly for m ⩾ n qubits [69]. While we do not pursue that extension in this manuscript, the techniques described in this paper are compatible with such an approach.…”

Section: A1 Quantum State Outputmentioning

confidence: 99%

Data-centric machine learning in quantum information science

Lohani

Lukens

Glasser

et al. 2022

Mach. Learn.: Sci. Technol.

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We propose a series of data-centric heuristics for improving the performance of machine learning systems when applied to problems in quantum information science. In particular, we consider how systematic engineering of training sets can significantly enhance the accuracy of pre-trained neural networks used for quantum state reconstruction without altering the underlying architecture. We find that it is not always optimal to engineer training sets to exactly match the expected distribution of a target scenario, and instead, performance can be further improved by biasing the training set to be slightly more mixed than the target. This is due to the heterogeneity in the number of free variables required to describe states of different purity, and as a result, overall accuracy of the network improves when training sets of a fixed size focus on states with the least constrained free variables. For further clarity, we also include a ``toy model'' demonstration of how spurious correlations can inadvertently enter synthetic data sets used for training, how the performance of systems trained with these correlations can degrade dramatically, and how the inclusion of even relatively few counterexamples can effectively remedy such problems.

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Section: A1 Quantum State Outputmentioning

confidence: 99%