Quantum contextual bandits and recommender systems for quantum data

TL;DR

This paper introduces a quantum contextual bandit framework for quantum data recommendation, focusing on selecting quantum states with minimal energy based on Hamiltonian contexts, and interprets the strategy as an online quantum phase classifier.

Contribution

It formulates a novel quantum state recommendation problem using linear contextual bandits and applies it to quantum phase classification tasks.

Findings

Effective quantum phase classification via bandit strategies

Application to Ising and cluster models

Framework for low energy quantum state recommendation

Abstract

We study a recommender system for quantum data using the linear contextual bandit framework. In each round, a learner receives an observable (the context) and has to recommend from a finite set of unknown quantum states (the actions) which one to measure. The learner has the goal of maximizing the reward in each round, that is the outcome of the measurement on the unknown state. Using this model we formulate the low energy quantum state recommendation problem where the context is a Hamiltonian and the goal is to recommend the state with the lowest energy. For this task, we study two families of contexts: the Ising model and a generalized cluster model. We observe that if we interpret the actions as different phases of the models then the recommendation is done by classifying the correct phase of the given Hamiltonian and the strategy can be interpreted as an online quantum phase classifier.