Parameterized quantum circuits as machine learning models

By Marcello Benedetti, Erika Lloyd, Stefan H. Sack, Mattia Fiorentini. Quantum Science and Technology, 2019

1,332 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/638e41912f314c74436205aa8d332dca963ab1dc

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In Artificial Intelligence

In Atomic and Molecular Physics, and Optics

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Maria Schuld

Citations in the corpus

Effect of data encoding on the expressive power of variational quantum-machine-learning models (2020, background): “This approach is known by different names such as variational circuits [1, 2], quantum circuit learning [3], quantum neural networks [4, 5], or parametrised quantum circuits [6].”
QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuits (2021, methodology): “The variational method has shown huge potentials in applications such as quantum machine learning [4], [7], [56], [63], numerical analysis [37], [39], quantum simulation [30], [31], [52], and optimizations [18], [47].”
General parameter-shift rules for quantum gradients (2021, background): “As a result, a new class of quantum algorithms — variational quantum algorithms (VQAs) [5, 6] — have come to shape the noisy intermediate-scale quantum (NISQ) era.”
Estimating the gradient and higher-order derivatives on quantum hardware (2020, background): “Some of the most important examples are the variational quantum eigensolver (VQE) [1, 7], the quantum approximate optimization algorithm (QAOA) [8], and all variational implementations of quantum machine learning algorithms [2, 3, 6, 9].”
Quantum machine learning models are kernel methods (2021, background): “, [15]) as well as for more sophisticated fault-tolerant algorithms trained by a quantum computer (e.”
Robust data encodings for quantum classifiers (2020, background): “In this context, VQAs can be considered quantum neural networks (QNNs) [25, 26], and a multitude of applica-”