Quantum Generative Adversarial Networks for Learning and Loading Random Distributions

Abstract

Quantum algorithms have the potential to outperform their classical counterparts in a variety of tasks. The realization of the advantage often requires the ability to load classical data efficiently into quantum states. However, the best known methods require $\mathcal{O}\left(2^n\right)$ gates to load an exact representation of a generic data structure into an $n$-qubit state. This scaling can easily predominate the complexity of a quantum algorithm and, thereby, impair potential quantum advantage. Our work presents a hybrid quantum-classical algorithm for efficient, approximate quantum state loading. More precisely, we use quantum Generative Adversarial Networks (qGANs) to facilitate efficient learning and loading of generic probability distributions -- implicitly given by data samples -- into quantum states. Through the interplay of a quantum channel, such as a variational quantum circuit, and a classical neural network, the qGAN can learn a representation of the probability distribution underlying the data samples and load it into a quantum state. The loading requires $\mathcal{O}\left(poly\left(n\right)\right)$ gates and can, thus, enable the use of potentially advantageous quantum algorithms, such as Quantum Amplitude Estimation. We implement the qGAN distribution learning and loading method with Qiskit and test it using a quantum simulation as well as actual quantum processors provided by the IBM Q Experience. Furthermore, we employ quantum simulation to demonstrate the use of the trained quantum channel in a quantum finance application.

arXiv comment14 pages, 13 figures

References in corpus (17)

• Quantum algorithm for solving linear systems of equations
• Barren plateaus in quantum neural network training landscapes
• The theory of variational hybrid quantum-classical algorithms
• Quantum Amplitude Amplification and Estimation
• Quantum principal component analysis
• An adaptive variational algorithm for exact molecular simulations on a quantum computer
• Quantum generative adversarial learning
• Quantum generative adversarial networks
• Quantum-state preparation with universal gate decompositions
• Quantum Risk Analysis
• Quantum computational finance: Monte Carlo pricing of financial derivatives
• Quantum generative adversarial learning in a superconducting quantum circuit
• Gradient Descent with Random Initialization: Fast Global Convergence for Nonconvex Phase Retrieval
• Black-box quantum state preparation without arithmetic
• Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step
• Learning and Inference on Generative Adversarial Quantum Circuits
• Adversarial quantum circuit learning for pure state approximation

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