Quantum generative adversarial network for generating discrete distribution

Situ, Haozhen; He, Zhiyuan; Wang, Yuyi; Li, Lvzhou; Zheng, Shenggen

doi:10.1016/j.ins.2020.05.127

Cited by 105 publications

(56 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…[267] experimentally demonstrates a quantum circuit training algorithm using the bars-and-stripes dataset [275] and a quantum-classical hybrid machine based on trapped ions. Several extensions and training approaches for quantum generative modelling have been introduced, e.g., generative adversarial quantum machines [276][277][278][279], where the key element is to couple a quantum circuit generator and a classical or quantum discriminator which are trained simultaneously [199]. The learning is such that the generator tries to create statistics for data that mimic those of a dataset while the discriminator tries to discern true data from generated data.…”

Section: Quantum Physics-inspired Machine Learningmentioning

confidence: 99%

Machine learning for quantum matter

Carrasquilla

2020

Advances in Physics: X

152

107

View full text Add to dashboard Cite

Quantum matter, the research field studying phases of matter whose properties are intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter physics, materials science, statistical mechanics, quantum information, quantum gravity, and large-scale numerical simulations. Recently, researchers interested in quantum matter and strongly correlated quantum systems have turned their attention to the algorithms underlying modern machine learning with an eye on making progress in their fields. Here we provide a short review on the recent development and adaptation of machine learning ideas for the purpose advancing research in quantum matter, including ideas ranging from algorithms that recognize conventional and topological states of matter in synthetic experimental data, to representations of quantum states in terms of neural networks and their applications to the simulation and control of quantum systems. We discuss the outlook for future developments in areas at the intersection between machine learning and quantum many-body physics.

show abstract

Section: Quantum Physics-inspired Machine Learningmentioning

confidence: 99%

Machine learning for quantum matter

Carrasquilla

2020

Advances in Physics: X

152

107

View full text Add to dashboard Cite

show abstract

“…Recent advances in VQC have demonstrated various applications in a wide variety of machine learning tasks. For example, VQC has been shown to be successful in the task of classification [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 65 , 67 , 68 ], function approximation [ 20 , 30 , 31 ], generative machine learning [ 32 , 33 , 34 , 35 , 36 , 37 ], metric learning [ 38 , 39 ], deep reinforcement learning [ 40 , 41 , 42 , 43 , 44 , 69 ], sequential learning [ 30 , 45 , 70 ] and speech recognition [ 46 ].…”

Section: Variational Quantum Circuitsmentioning

confidence: 99%

“…The most notable progress is the development of variational algorithms [ 17 , 18 , 19 ] which enable the quantum machine learning on NISQ devices [ 20 ]. Recent efforts have demonstrated the promising application of NISQ devices in several machine learning tasks [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ].…”

Section: Introductionmentioning

confidence: 99%

Federated Quantum Machine Learning

Chen

Yoo

2021

Entropy

View full text Add to dashboard Cite

Distributed training across several quantum computers could significantly improve the training time and if we could share the learned model, not the data, it could potentially improve the data privacy as the training would happen where the data is located. One of the potential schemes to achieve this property is the federated learning (FL), which consists of several clients or local nodes learning on their own data and a central node to aggregate the models collected from those local nodes. However, to the best of our knowledge, no work has been done in quantum machine learning (QML) in federation setting yet. In this work, we present the federated training on hybrid quantum-classical machine learning models although our framework could be generalized to pure quantum machine learning model. Specifically, we consider the quantum neural network (QNN) coupled with classical pre-trained convolutional model. Our distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training. It demonstrates a promising future research direction for scaling and privacy aspects.

show abstract

“…In the case of small-scale numerical simulation, the wave function can be used to directly calculate the expected value. Another method is to calculate the probability distribution based on the wave function, and then sample the gradient for estimation [37].…”

Section: Adversarial Training Strategymentioning

confidence: 99%

A hybrid quantum-classical conditional generative adversarial network algorithm for human-centered paradigm in cloud

Liu

Zhang

Deng

et al. 2020

Preprint

View full text Add to dashboard Cite

As an emerging field that aims to bridge the gap between human activities and computing systems, human-centered computing (HCC) in cloud, edge, fog has had a huge impact on the artificial intelligence algorithms. The quantum generative adversarial network (QGAN) is considered to be one of the quantum machine learning algorithms with great application prospects, which also should be improved to conform to the human-centered paradigm. The generation process of QGAN is relatively random and the generated model does not conform to the human-centered concept, so it is not quite suitable for real scenarios. In order to solve these problems, a hybrid quantum-classical conditional generative adversarial network (QCGAN) algorithm is proposed, which is a knowledge-driven human-computer interaction computing mode in cloud. The purpose of stabilizing the generation process and the interaction between human and computing process is achieved by inputting conditional information in the generator and discriminator. The generator uses the parameterized quantum circuit with an all-to-all connected topology, which facilitates the tuning of network parameters during the training process. The discriminator uses the classical neural network, which effectively avoids the ”input bottleneck” of quantum machine learning. Finally, the BAS training set is selected to conduct experiment on the quantum cloud computing platform. The result shows that the QCGAN algorithm can effectively converge to the Nash equilibrium point after training and perform human-centered classification generation tasks.

show abstract

Quantum generative adversarial network for generating discrete distribution

Cited by 105 publications

References 32 publications

Machine learning for quantum matter

Machine learning for quantum matter

Federated Quantum Machine Learning

A hybrid quantum-classical conditional generative adversarial network algorithm for human-centered paradigm in cloud

Contact Info

Product

Resources

About