Measurement disturbance tradeoffs in three-qubit unsupervised quantum classification

Spencer-Wood, Hector and Jeffers, John and Croke, Sarah (2022) Measurement disturbance tradeoffs in three-qubit unsupervised quantum classification. Physical Review A, 105 (6). 062447. ISSN 1050-2947 (

[thumbnail of Spencer-Wood-etal-PRA-2022-Measurement-disturbance-tradeoffs-in-three-qubit-unsupervised-quantum-classification]
Text. Filename: Spencer_Wood_etal_PRA_2022_Measurement_disturbance_tradeoffs_in_three_qubit_unsupervised_quantum_classification.pdf
Accepted Author Manuscript
License: Strathprints license 1.0

Download (665kB)| Preview


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. Specifically, 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.


Spencer-Wood, Hector, Jeffers, John ORCID logoORCID: and Croke, Sarah;