Parameterized quantum circuits as machine learning models
Hybrid quantum-classical systems make it possible to utilize existing quantum computers to their fullest extent. Within this framework, parameterized quantum circuits can be regarded as machine learning models with remarkable expressive power. This Review presents the components of these models and discusses their application to a variety of data-driven tasks, such as supervised learning and generative modeling. With an increasing number of experimental demonstrations carried out on actual quantum hardware and with software being actively developed, this rapidly growing field is poised to have a broad spectrum of real-world applications.
Code (4)
Tasks
BIG-bench Machine LearningSimilar Papers 제목 키워드 기반
Evaluation of Parameterized Quantum Circuits: on the relation between classification accuracy, expressibility and entangling capability
An active area of investigation in the search for quantum advantage is Quantum Machine Learning. Quantum Machine Learning, and Parameterized Quantum Circuits in a hybrid quantum-classical setup in particular, could bring…
BIG-bench Machine LearningGeneral ClassificationQuantum Machine LearningRelationVariational quantum Gibbs state preparation with a truncated Taylor series
The preparation of quantum Gibbs state is an essential part of quantum computation and has wide-ranging applications in various areas, including quantum simulation, quantum optimization, and quantum machine learning. In …
Quantum Machine LearningParameterized Quantum Circuits with Quantum Kernels for Machine Learning: A Hybrid Quantum-Classical Approach
Quantum machine learning (QML) is the use of quantum computing for the computation of machine learning algorithms. With the prevalence and importance of classical data, a hybrid quantum-classical approach to QML is calle…
Dimensionality ReductionQuantum Machine LearningCautious optimism for deep parameterized quantum circuits
A central challenge in quantum machine learning is understanding the scaling behavior of parameterized quantum circuits (PQCs). In particular, it remains unclear how their performance on unseen data changes as the number…
Quantum Machine LearningThe Expressive Power of Parameterized Quantum Circuits
Parameterized quantum circuits (PQCs) have been broadly used as a hybrid quantum-classical machine learning scheme to accomplish generative tasks. However, whether PQCs have better expressive power than classical generat…
Tensor Networks