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Parameterized quantum circuits as machine learning models
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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.
Forward citations
Cited by 5 Pith papers
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Expressivity of Quantum Reservoir Computers
A quantum reservoir computer's expressivity is limited by its input encoding (a Fourier series of degree r, giving 2r+1 independent functions), not by its Hilbert space size.
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Quantum-enhanced least-square support vector machine: simplified quantum algorithm and sparse solutions
The paper proposes quantum LS-SVM algorithms based on continuous-variable matrix inversion and sparse hybrid solutions, but the core equations contain a sign error and an invalid unitary factorization.
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The Capacity of Quantum Neural Networks
The memory capacity of any quantum neural network is at most the information content of its trainable parameters, so classically-parameterized QNNs lack capacity advantage over classical NNs.
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Continuous-variable photonic quantum extreme learning machines for fast collider-data selection
A Gaussian photonic QELM with displacement encoding and quadrature/photon-number readout produces polynomial features that, under a linear readout, match or beat small MLPs on top-jet and Higgs classification.
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Experimental investigation of single qubit quantum classifier with small number of samples
A silicon photonic single-qubit classifier achieves about 86 percent accuracy when trained with an average of roughly two photons per sample, matching simulation.
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