Circuit-centric quantum classifiers

By Maria Schuld, Alex Bocharov, Krysta Marie Svore, Nathan Wiebe. Physical Review A, 2018

1,137 citations recorded by the source corpus.

A machine learning design is developed to train a quantum circuit specialized in solving a classification problem. In addition to discussing the training method and effect of noise, it is shown that the circuits perform reasonably well on classical benchmarks.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/804f822f9a6db8f559801f1c618b7d6c766741b4

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Citations in the corpus

Quantum Natural Gradient (2019, methodology): “Applications include the Variational Quantum Eigensolver (VQE) [20], Quantum Approximate Optimization Algorithm (QAOA) [5] and Quantum Neural Networks (QNNs) [6, 10, 23].”
Transfer learning in hybrid classical-quantum neural networks (2019, background): “One of the possible quantum generalizations of feedforward neural networks can be given in terms of variational quantum circuits [17, 24, 30, 30, 33, 34, 39, 41, 43].”
PennyLane: Automatic differentiation of hybrid quantum-classical computations (2018, background): “Recent advances in the quantum machine learning literature [8, 10, 11, 41] have suggested ways to estimate analytic derivatives by computing linear combinations of different quantum circuits.”
Continuous-variable quantum neural networks (2018, background): “[64] Krysta Svore Nathan Wiebe Maria Schuld, Alex Bocharov.”
Potential of quantum computing for drug discovery (2018, background): “networks for classification tasks [123].”
Parameterized quantum circuits as machine learning models (2019, methodology): “Schuld et al. [48] propose a VQM classiﬁer assuming amplitude-encoded input data.”
Variational Quantum Circuits for Deep Reinforcement Learning (2019, background): “Previous works [26, 20, 27] have demonstrated that the variational circuits can model any function approximators, classiﬁers and even quantum-many-body physics that are intractable on classical computers.”
Quantum Generative Adversarial Networks for learning and loading random distributions (2019, methodology): “[50] M.”
Robust data encodings for quantum classifiers (2020, methodology): “For the processing step (3), there have been many proposed QNN architectures in recent literature, including quantum convolutional neural networks [63, 64], strongly entangling ansätze [33], and more [35, 65].”
Evaluating analytic gradients on quantum hardware (2018, background): “[6], where Q̂ had the special form of a σz observable, which allowed the authors to evaluate expectations via overlaps of quantum states.”
General parameter-shift rules for quantum gradients (2021, neutral): “…as optimization [8], quantum chemistry [9, 10, 11, 12, 13], integer factorization [14], compilation [15], quantum control [16], matrix diagonaliza-David Wierichs: wierichs@thp.uni-koeln.de tion [17, 18], and variational quantum machine learning [19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31].”
Variational quantum algorithms (2020, methodology): “As shown in [133], parametrized quantum circuits can support linear transformations and non-linearity can be exploited from the tensor product structure of a quantum”
An initialization strategy for addressing barren plateaus in parametrized quantum circuits (2019, methodology): “Resolving this issue is critical to the scalability of algorithms such as the variational quantum eigensolver (VQE) [2, 3] and quantum neural networks (QNNs) [4, 5, 6].”