Quantum convolutional neural networks

By Iris Cong, Soonwon Choi, Mikhail D. Lukin. Nature Physics, 2018

1,575 citations recorded by the source corpus.

Neural network-based machine learning has recently proven successful for many complex applications ranging from image recognition to precision medicine. However, its direct application to problems in quantum physics is challenging due to the exponential complexity of many-body systems. Motivated by recent advances in realizing quantum information processors, we introduce and analyse a quantum circuit-based algorithm inspired by convolutional neural networks, a highly effective model in machine learning. Our quantum convolutional neural network (QCNN) uses only O(log(N)) variational parameters for input sizes of N qubits, allowing for its efficient training and implementation on realistic, near-term quantum devices. To explicitly illustrate its capabilities, we show that QCNNs can accurately recognize quantum states associated with a one-dimensional symmetry-protected topological phase, with performance surpassing existing approaches. We further demonstrate that QCNNs can be used to devise a quantum error correction scheme optimized for a given, unknown error model that substantially outperforms known quantum codes of comparable complexity. The potential experimental realizations and generalizations of QCNNs are also discussed. A quantum circuit-based algorithm inspired by convolutional neural networks is shown to successfully perform quantum phase recognition and devise quantum error correcting codes when applied to arbitrary input quantum states.

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

View Paper: https://www.semanticscholar.org/paper/f38b1c390eedc55cbbd5716e03b15b67ff7e942b

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In Atomic and Molecular Physics, and Optics

Citations in the corpus

Variational quantum algorithms (2020, methodology): “[151] introduced a Quantum Convolutional Neural Networks (QCNN).”
Quantum variational algorithms are swamped with traps (2022, neutral): “We also consider a class of models similar to quantum convolutional neural networks (QCNNs) 12 previously shown not to suffer from barrenplateaus 42 .”
Quantum computing: A taxonomy, systematic review and future directions (2020, result): “It is not yet fully established if quantum machine learning would offer superior computational efficiency when compared to classical machine learning implementations; however, recent work has shown promising results [7] [8].”
Effect of data encoding on the expressive power of variational quantum-machine-learning models (2020, background): “A lot of work has been done to understand the practical details of this approach, leading to useful training strategies [3, 7, 8] and ways to emulate and extend classical machine learning methods [2, 9–12].”
Challenges and opportunities in quantum machine learning (2022, methodology): “QML can enhance quantum computing by learning quantum error correction codes [11, 111] and syndrome decoders, performing quantum control, learning to mitigate errors, and compiling and optimizing quantum circuits.”
Quantum convolutional neural network for classical data classification (2021, methodology): “[30] show that the cost function gradient vanishes at a slower rate (with n, the number of initial qubits) when all unitary operators in the same layer are identical as in QCNN [14].”
Absence of Barren Plateaus in Quantum Convolutional Neural Networks (2020, methodology): “The latter corresponds to the QCNN architecture as originally introduced in [34].”