Measurement disturbance tradeoffs in unsupervised quantum classification

Seminar author:Hector Spencer-Wood

Event date and time:2023-01-19 03:00

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We consider measurement disturbance tradeoffs in quantum machine learning protocols which seek to learn about quantum data. We study the simplest example of a binary classification task in the unsupervised regime. Primarily, we investigate how a classification of two qubits, that can each be in one of two unknown states, affects our ability to perform a subsequent classification on three qubits when a third is added. Surprisingly, we find a range of strategies in which a nontrivial first classification does not affect the success rate of the second classification. There is, however, a nontrivial measurement disturbance tradeoff between the success rate of the first and second classifications, and we fully characterize this tradeoff analytically. Following this, we briefly discuss strategies to generalise this to the scenario in which we begin with n qubits to classify and subsequently add an extra one.