Learning and Inference on Generative Adversarial Quantum Circuits

Abstract

Quantum mechanics is inherently probabilistic in light of Born's rule. Using quantum circuits as probabilistic generative models for classical data exploits their superior expressibility and efficient direct sampling ability. However, training of quantum circuits can be more challenging compared to classical neural networks due to lack of efficient differentiable learning algorithm. We devise an adversarial quantum-classical hybrid training scheme via coupling a quantum circuit generator and a classical neural network discriminator together. After training, the quantum circuit generative model can infer missing data with quadratic speed up via amplitude amplification. We numerically simulate the learning and inference of generative adversarial quantum circuit using the prototypical Bars-and-Stripes dataset. Generative adversarial quantum circuits is a fresh approach to machine learning which may enjoy the practically useful quantum advantage on near-term quantum devices.

7 pages, 6 figures

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• The Born Supremacy: Quantum Advantage and Training of an Ising Born Machine
• Machine Learning for Quantum Matter
• Quantum Adversarial Machine Learning
• Yao.jl: Extensible, Efficient Framework for Quantum Algorithm Design
• Quantum Machine Learning: from physics to software engineering
• Learning quantum data with the quantum Earth Mover's distance
• Information Scrambling in Quantum Neural Networks
• Anomaly detection with variational quantum generative adversarial networks
• Adaptive pruning-based optimization of parameterized quantum circuits
• Generative model benchmarks for superconducting qubits
• Generalized quantum circuit differentiation rules
• Realizing a quantum generative adversarial network using a programmable superconducting processor
• Variational inference with a quantum computer
• Generative Tensor Network Classification Model for Supervised Machine Learning
• Style-based quantum generative adversarial networks for Monte Carlo events
• Expressivity of Quantum Neural Networks
• Scrambling Ability of Quantum Neural Networks Architectures
• Quantum semi-supervised generative adversarial network for enhanced data classification
• A brief introduction to quantum algorithms
• A performance characterization of quantum generative models
• Hamiltonian operator approximation for energy measurement and ground state preparation
• Noise robustness and experimental demonstration of a quantum generative adversarial network for continuous distributions
• Tight and Efficient Gradient Bounds for Parameterized Quantum Circuits
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• Quantum Optimization for Training Quantum Neural Networks
• Parameterized quantum circuits as universal generative models for continuous multivariate distributions
• Implementation of quantum stochastic walks for function approximation, two-dimensional data classification, and sequence classification
• F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits
• A hybrid quantum-classical approach for inference on restricted Boltzmann machines
• Quantum subspace alignment for domain adaptation
• Protocols for Trainable and Differentiable Quantum Generative Modelling
• Quantum Generative Adversarial Networks in a Silicon Photonic Chip with Maximum Expressibility
• Towards AutoQML: A Cloud-Based Automated Circuit Architecture Search Framework
• Information flow in parameterized quantum circuits
• Multiqubit state learning with entangling quantum generative adversarial networks
• TILT: Achieving Higher Fidelity on a Trapped-Ion Linear-Tape Quantum Computing Architecture
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• Quantum circuit-like learning: A fast and scalable classical machine-learning algorithm with similar performance to quantum circuit learning
• A Parameter-Efficient Quantum Anomaly Detection Method on a Superconducting Quantum Processor
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• ReCon: Reconfiguring Analog Rydberg Atom Quantum Computers for Quantum Generative Adversarial Networks
• Quantum Sparse Coding and Decoding Based on Quantum Network