Quantum circuit learning

By Kosuke Mitarai, Makoto Negoro, Masahiro Kitagawa, Keisuke Fujii. Physical Review A, 2018

1,720 citations recorded by the source corpus.

We propose a classical-quantum hybrid algorithm for machine learning on near-term quantum processors, which we call quantum circuit learning. A quantum circuit driven by our framework learns a given task by tuning parameters implemented on it. The iterative optimization of the parameters allows us to circumvent the high-depth circuit. Theoretical investigation shows that a quantum circuit can approximate nonlinear functions, which is further confirmed by numerical simulations. Hybridizing a low-depth quantum circuit and a classical computer for machine learning, the proposed framework paves the way toward applications of near-term quantum devices for quantum machine learning.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/4d931ea98be69882f547ec6c1b42b78c3e13c36d

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Citations in the corpus

Quantum Computing for Finance: State-of-the-Art and Future Prospects (2020, background): “in continuous quantum systems [105] that guarantees linear separability or amplitude encoding [106] that can exploit tensorial feature map or density-operator encoding [107].”
Variational quantum algorithms (2020, methodology): “Surprisingly, there is a hardware-friendly protocol to evaluate the partial derivative of C(θ) with respect to θl often referred to as the parameter-shift rule [63–66].”
Diagnosing Barren Plateaus with Tools from Quantum Optimal Control (2021, background): “[32] Kosuke Mitarai, Makoto Negoro, Masahiro Kitagawa, and Keisuke Fujii.”
Variational Quantum Linear Solver (2019, methodology): “This enables one to take a gradientdescent optimization approach to VQLS, and is inspired by gradient-based approaches in previous work [33, 58] for other VHQCAs.”
Variational Quantum Circuits for Deep Reinforcement Learning (2019, methodology): “For example, the work [20] shows that a variational quantum circuits can approximate an analytical function f ( x ) .”
General parameter-shift rules for quantum gradients (2021, methodology): “, G has two eigenvalues), the derivative can be computed via the parameter-shift rule [16, 23, 38]”
Absence of Barren Plateaus in Quantum Convolutional Neural Networks (2020, background): “[53] K.”
Continuous-variable quantum neural networks (2018, methodology): “, “layered”) quantum circuit building blocks to form a larger computation [36,71].”
Estimating the gradient and higher-order derivatives on quantum hardware (2020, methodology): “[10–13], but extended to the Hessian.”
Materials challenges and opportunities for quantum computing hardware (2021, positive): “Variational quantum algorithms such as variational quantum eigensolvers ( 12 ) and quantum circuit learning ( 13 ) have been used to calculate energies of simple molecules ( 14 – 16 ), simulate lattice models ( 17 ), and improve efficiency of machine learning ( 18 ).”
Quantum computing with neutral atoms (2020, background): “poses than data intensive applications such as the ones mentioned above. New quantum applications for Machine learning are today rapidly developing [7,84], and several classiers have been elaborated [85,86] as well as sampling methods of classically inaccessible systems. Another promising direction ”
Effect of data encoding on the expressive power of variational quantum-machine-learning models (2020, methodology): “We start with an analysis of the popular strategy [3, 25, 30–35] of using single-qubit Pauli rotations in the encoding subroutine S(x) in order to showcase the practical value of the approach.”