End-To-End Quantum Machine Learning Implemented with Controlled Quantum Dynamics

Wu, Rongbo; Cao, Xi; Xie, Pinchen; Liu, Yu-xi

doi:10.1103/physrevapplied.14.064020

Cited by 22 publications

(13 citation statements)

References 26 publications

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“…KAK first proposed a quantum neural network in 1994 [4]. Then scholars extensively explored a variety of possible quantum neural network models based on the noisy mesoscale quantum devices [5], such as the quantum perceptron model [6], the quantum tensor neural network [7], the quantum convolutional neural networks [8], etc. These quantum neural network models simulate typical quantum systems with network structural characteristics in the quantum Hilbert space and ad-This work is supported by National Natural Science Foundation of China (No.…”

Section: Introductionmentioning

confidence: 99%

Quantum Graph Convolutional Neural Networks

Zheng

Gao

2021

Preprint

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At present, there are a large number of quantum neural network models to deal with Euclidean spatial data, while little research have been conducted on non-Euclidean spatial data. In this paper, we propose a novel quantum graph convolutional neural network (QGCN) model based on quantum parametric circuits and utilize the computing power of quantum systems to accomplish graph classification tasks in traditional machine learning. The proposed QGCN model has a similar architecture as the classical graph convolutional neural networks, which can illustrate the topology of the graph type data and efficiently learn the hidden layer representation of node features as well. Numerical simulation results on a graph dataset demonstrate that the proposed model can be effectively trained and has good performance in graph level classification tasks.

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Section: Introductionmentioning

confidence: 99%

Quantum Graph Convolutional Neural Networks

Zheng

Gao

2021

Preprint

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“…In can be evaluated with the finite difference method by making a small change of the each control parameter θ (k) i (t j ) [36]. The gradient of L with respect to W (k) can be derived from the gradient of L with respect to θ (k) En [33]. Therefore, we can apply the widely used stochastic gradientdescent algorithm in machine learning to update W (k) and θ (k) In by minimizing L on the training dataset D (see Supplementary Materials for details of the algorithms) [37].…”

Section: (K)mentioning

confidence: 99%

“…There are certainly much room for performance improvement by using more hardware-efficient quantum ansatzes, e.g., via deep optimization of the circuit architecture [31] and qubit mapping strategies [32]. Recently, a hardware-friendly end-to-end learning scheme (in the sense that the model is trained as a whole instead of being divided into separate modules) is proposed [33] by replacing the gate-based QNN with natural quantum dynamics driven by coherent control pulses. This model requires very little architecture design, system calibration, and no qubit mapping.…”

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confidence: 99%

Experimental Quantum End-to-End Learning on a Superconducting Processor

Pan¹,

Chen²,

Wang³

et al. 2022

Preprint

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Machine learning can be substantially powered by a quantum computer owing to its huge Hilbert space and inherent quantum parallelism. In the pursuit of quantum advantages for machine learning with noisy intermediatescale quantum devices, it was proposed that the learning model can be designed in an end-to-end fashion, i.e., the quantum ansatz is parameterized by directly manipulable control pulses without circuit design and compilation. Such gate-free models are hardware friendly and can fully exploit limited quantum resources. Here, we report the first experimental realization of quantum end-to-end machine learning on a superconducting processor. The trained model can achieve 98% recognition accuracy for two handwritten digits (via two qubits) and 89% for four digits (via three qubits) in the MNIST (Mixed National Institute of Standards and Technology) database. The experimental results exhibit the great potential of quantum end-to-end learning for resolving complex real-world tasks when more qubits are available.

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“…Many experimental proposals have been put forth for training a parameterized quantum circuit with a classical optimization loop [76] . Such optimization processes are very similar to those of quantum optimal controls [77,78] , but the underlying quantum dynamics is much more complicated as the currently available control resources are insufficient for full controllability to be achieved. In particular, an investigation of the optimization landscape in VQAbased large-scale quantum machine learning applications may provide insights for the realizability of quantum advantages in the NISQ era [79] .…”

Section: Concluding Remarks and Outlooksmentioning

confidence: 99%

“…Complex System Modeling and Simulation, June 2021, 1(2):[77][78][79][80][81][82][83][84][85][86][87][88][89][90] …”

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confidence: 99%

Optimization Landscape of Quantum Control Systems

Rabitz

2021

Complex Syst. Model. Simul.

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Optimization is ubiquitous in the control of quantum dynamics in atomic, molecular, and optical systems.The ease or difficulty of finding control solutions, which is practically crucial for developing quantum technologies, is highly dependent on the geometry of the underlying optimization landscapes. In this review, we give an introduction to the basic concepts in the theory of quantum optimal control landscapes, and their trap-free critical topology under two fundamental assumptions. Furthermore, the effects of various factors on the search effort are discussed, including control constraints, singularities, saddles, noises, and non-topological features of the landscapes. Additionally, we review recent experimental advances in the control of molecular and spin systems. These results provide an overall understanding of the optimization complexity of quantum control dynamics, which may help to develop more efficient optimization algorithms for quantum control systems, and as a promising extension, the training processes in quantum machine learning.

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End-To-End Quantum Machine Learning Implemented with Controlled Quantum Dynamics

Cited by 22 publications

References 26 publications

Quantum Graph Convolutional Neural Networks

Quantum Graph Convolutional Neural Networks

Experimental Quantum End-to-End Learning on a Superconducting Processor

Optimization Landscape of Quantum Control Systems

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