Quantum generative adversarial network for generating discrete distribution

Abstract

Quantum machine learning has recently attracted much attention from the community of quantum computing. In this paper, we explore the ability of generative adversarial networks (GANs) based on quantum computing. More specifically, we propose a quantum GAN for generating classical discrete distribution, which has a classical-quantum hybrid architecture and is composed of a parameterized quantum circuit as the generator and a classical neural network as the discriminator. The parameterized quantum circuit only consists of simple one-qubit rotation gates and two-qubit controlled-phase gates that are available in current quantum devices. Our scheme has the following characteristics and potential advantages: (i) It is intrinsically capable of generating discrete data (e.g., text data), while classical GANs are clumsy for this task due to the vanishing gradient problem. (ii) Our scheme avoids the input/output bottlenecks embarrassing most of the existing quantum learning algorithms that either require to encode the classical input data into quantum states, or output a quantum state corresponding to the solution instead of giving the solution itself, which inevitably compromises the speedup of the quantum algorithm. (iii) The probability distribution implicitly given by data samples can be loaded into a quantum state, which may be useful for some further applications.

References in corpus (31)

• Adam: A Method for Stochastic Optimization
• Auto-Encoding Variational Bayes
• Conditional Generative Adversarial Nets
• Quantum Machine Learning
• Quantum algorithm for solving linear systems of equations
• Quantum support vector machine for big data classification
• Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
• Quantum Circuit Learning
• Quantum principal component analysis
• Characterizing Quantum Supremacy in Near-Term Devices
• Circuit-centric quantum classifiers
• Quantum generative adversarial learning
• Quantum Data Fitting
• Quantum generative adversarial networks
• Quantum autoencoders for efficient compression of quantum data
• Quantum-enhanced machine learning
• A generative modeling approach for benchmarking and training shallow quantum circuits
• Towards Quantum Machine Learning with Tensor Networks
• Differentiable Learning of Quantum Circuit Born Machine
• Quantum generalisation of feedforward neural networks
• Learning the quantum algorithm for state overlap
• Entanglement-Based Machine Learning on a Quantum Computer
• Experimental Quantum Computing to Solve Systems of Linear Equations
• Quantum Variational Autoencoder
• Quantum generative adversarial learning in a superconducting quantum circuit
• Learning and Inference on Generative Adversarial Quantum Circuits
• Quantum algorithm for association rules mining
• Quantum algorithm for association rules mining
• Adversarial quantum circuit learning for pure state approximation
• Quantum algorithm for visual tracking
• Inductive supervised quantum learning

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• Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics
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• Quantum Generative Adversarial Networks in a Silicon Photonic Chip with Maximum Expressibility
• Photonic quantum generative adversarial networks for classical data
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• Towards AutoQML: A Cloud-Based Automated Circuit Architecture Search Framework
• Learning the expressibility of quantum circuit ansatz using transformer
• On Applying the Lackadaisical Quantum Walk Algorithm to Search for Multiple Solutions on Grids
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• Constraint-Aware Quantum Optimization via Hamming Weight Operators
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• Modeling stochastic eye tracking data: A comparison of quantum generative adversarial networks and Markov models
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• Many-body thermal states on a quantum computer: a variational approach