Variational quantum circuits for quantum state tomography

Liu, Yong; Wang, Dongyang; Xue, Shichuan; Huang, Anqi; Fu, Xiang; Qiang, Xiaogang; Xu, Ping; Huang, He-Liang; Deng, Mingtang; Guo, Chu; Yang, Xuejun; Wu, Junjie

doi:10.1103/physreva.101.052316

Cited by 39 publications

(18 citation statements)

References 35 publications

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“…This has been used to construct a photonic neural network model having continuous variable gates. Variational circuits [14] behave similar to neural networks in that there is a definite input (input quantum state) which is embedded into the circuit using a suitable embedding, weights (circuit parameters) that need to be learnt by the model and quantum measurement of an observable say A, which generates a classical output on which training rules are applied for parametric learning. A three qumode CVQNN architecture is shown in Fig.…”

Section: Continuous Variable Quantum Neural Networkmentioning

confidence: 99%

COVID-19 Outbreak Prediction Using Quantum Neural Networks

Kairon

Bhattacharyya

2020

Advances in Intelligent Systems and Computing

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Artificial intelligence has become an important tool in fight against COVID-19. Machine learning models for COVID-19 global pandemic predictions have shown a higher accuracy than the previously used statistical models used by epidemiologists. With the advent of quantum machine learning, we present a comparative analysis of continuous variable quantum neural networks (variational circuits) and quantum backpropagation multilayer perceptron (QBMLP). We analyze the convoluted and sporadic data of two affected countries, and hope that our study will help in effective modeling of outbreak while throwing a light on bright future of quantum machine learning.

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Section: Continuous Variable Quantum Neural Networkmentioning

confidence: 99%

COVID-19 Outbreak Prediction Using Quantum Neural Networks

Kairon

Bhattacharyya

2020

Advances in Intelligent Systems and Computing

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“…In this work, we propose a unsupervised quantum machine learning algorithm for quantum process tomography, which is also a continuation of Ref. [21]. As shown in Fig.…”

Section: Introductionmentioning

confidence: 97%

“…Such examples include compressed sensing quantum process tomography that assumes the measurement outcomes are sparse [18], and tensor network states based quantum process tomography which assumes a low entanglement structure of the underlying quantum process [19,20]. Recently, it is shown that one could efficiently encode the information of certain quantum states into a parametric quantum circuit (PQC) using a gradient-based quantum machine learning algorithm, after which the unknown quantum state can be reconstructed classically with high fidelity using the optimal parameters of the PQC [21].…”

Section: Introductionmentioning

confidence: 99%

Variational quantum process tomography

Xue,

Liu,

Wang

et al. 2021

Preprint

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Quantum process tomography is an experimental technique to fully characterize an unknown quantum process. Standard quantum process tomography suffers from exponentially scaling of the number of measurements with the increasing system size. In this work, we put forward a quantum machine learning algorithm which approximately encodes the unknown unitary quantum process into a relatively shallow depth parametric quantum circuit. We demonstrate our method by reconstructing the unitary quantum processes resulting from the quantum Hamiltonian evolution and random quantum circuits up to 8 qubits. Results show that those quantum processes could be reconstructed with high fidelity, while the number of input states required are at least 2 orders of magnitude less than required by the standard quantum process tomography.

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“…If a quantum computer could easily produce complex distributions, it is also natural to postulate that it is able to learn patterns from certain data distributions which could be very difficult for classical computers [8]. Quantum machine learning (QML) attempts to utilize this power of quantum computers to achieve computational speedups or better performance for machine learning tasks [8][9][10][11][12][13][14], and parameterized quantum circuits (PQCs) offer a promising path for quantum machine learning in the NISQ era [15][16][17]. Compared with traditional quantum algorithms [12,[18][19][20] such as Shor's algorithm [18,21,22], PQCbased quantum machine learning algorithms are naturally robust to noise [23] and only require shallow quantum circuits, which is highly desirable for near-term noisy intermediate scale quantum devices.…”

Section: Introductionmentioning

confidence: 99%

Hybrid quantum-classical convolutional neural networks

Liu

Lim

Wood

et al. 2021

Sci. China Phys. Mech. Astron.

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126

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Deep learning has been shown to be able to recognize data patterns better than humans in specific circumstances or contexts. In parallel, quantum computing has demonstrated to be able to output complex wave functions with a few number of gate operations, which could generate distributions that are hard for a classical computer to produce. Here we propose a hybrid quantum-classical convolutional neural network (QCCNN), inspired by convolutional neural networks (CNNs) but adapted to quantum computing to enhance the feature mapping process. QCCNN is friendly to currently noisy intermediate-scale quantum computers, in terms of both number of qubits as well as circuit's depths, while retaining important features of classical CNN, such as nonlinearity and scalability. We also present a framework to automatically compute the gradients of hybrid quantum-classical loss functions which could be directly applied to other hybrid quantum-classical algorithms. We demonstrate the potential of this architecture by applying it to a Tetris dataset, and show that QCCNN can accomplish classification tasks with learning accuracy surpassing that of classical CNN with the same structure.

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Variational quantum circuits for quantum state tomography

Cited by 39 publications

References 35 publications

COVID-19 Outbreak Prediction Using Quantum Neural Networks

COVID-19 Outbreak Prediction Using Quantum Neural Networks

Variational quantum process tomography

Hybrid quantum-classical convolutional neural networks

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