Efficient Online Quantum Generative Adversarial Learning Algorithms with Applications

Abstract

The exploration of quantum algorithms that possess quantum advantages is a central topic in quantum computation and quantum information processing. One potential candidate in this area is quantum generative adversarial learning (QuGAL), which conceptually has exponential advantages over classical adversarial networks. However, the corresponding learning algorithm remains obscured. In this paper, we propose the first quantum generative adversarial learning algorithm-- the quantum multiplicative matrix weight algorithm (QMMW)-- which enables the efficient processing of fundamental tasks. The computational complexity of QMMW is polynomially proportional to the number of training rounds and logarithmically proportional to the input size. The core concept of the proposed algorithm combines QuGAL with online learning. We exploit the implementation of QuGAL with parameterized quantum circuits, and numerical experiments for the task of entanglement test for pure state are provided to support our claims.

References in corpus (31)

• Quantum Computing in the NISQ era and beyond
• Entanglement of Formation of an Arbitrary State of Two Qubits
• Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer
• Quantum secret sharing
• On the Measurement of Qubits
• Separability criterion and inseparable mixed states with positive partial transposition
• Parameterized quantum circuits as machine learning models
• 14-qubit entanglement: creation and coherence
• Evaluating analytic gradients on quantum hardware
• An adaptive variational algorithm for exact molecular simulations on a quantum computer
• Bell Inequalities and the Separability Criterion
• General Entanglement Breaking Channels
• Quantum generative adversarial learning
• 18-qubit entanglement with photon's three degrees of freedom
• A complete family of separability criteria
• 12-photon entanglement and scalable scattershot boson sampling with optimal entangled-photon pairs from parametric down-conversion
• Observation of multi-component atomic Schrödinger cat states of up to 20 qubits
• Quantum Tomography via Compressed Sensing: Error Bounds, Sample Complexity, and Efficient Estimators
• Entanglement Certification $-$ From Theory to Experiment
• The Expressive Power of Parameterized Quantum Circuits
• Quantum generative adversarial learning in a superconducting quantum circuit
• Experimental Machine Learning of Quantum States
• Quantum Adversarial Machine Learning
• Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving
• Detecting quantum entanglement with unsupervised learning
• Realizing a quantum generative adversarial network using a programmable superconducting processor
• Experimental Simultaneous Learning of Multiple Non-Classical Correlations
• Entanglement Structure: Entanglement Partitioning in Multipartite Systems and Its Experimental Detection Using Optimizable Witnesses
• Experimentally Robust Self-testing for Bipartite and Tripartite Entangled States
• An improved semidefinite programming hierarchy for testing entanglement
• Experimental hybrid quantum-classical reinforcement learning by boson sampling: how to train a quantum cloner

Cited by in corpus (7)

• Quantum circuit architecture search for variational quantum algorithms
• Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry
• Fundamental Limitation on the Detectability of Entanglement
• Protocols for Trainable and Differentiable Quantum Generative Modelling
• Schrödinger as a Quantum Programmer: Estimating Entanglement via Steering
• Variational-toolbox-based separability detection of multiqubit states
• Contextuality-enhanced quantum state discrimination under fixed failure probability