Measurement disturbance tradeoffs in three-qubit unsupervised quantum classification

TL;DR

This paper analyzes measurement disturbance tradeoffs in a three-qubit quantum classification task, revealing conditions where initial classification does not impair subsequent classification success, and providing a full analytical characterization of the tradeoff.

Contribution

It introduces a detailed analysis of measurement disturbance tradeoffs in unsupervised quantum classification involving three qubits, with an analytical characterization of the tradeoff.

Findings

Certain strategies allow sequential classifications without success rate degradation

A non-trivial measurement disturbance tradeoff exists between first and second classifications

The tradeoff is fully characterized analytically

Abstract

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 non-trivial first classification does not affect the success rate of the second classification. There is, however, a non-trivial measurement disturbance tradeoff between the success rate of the first and second classifications, and we fully characterise this tradeoff analytically.