Year: 2022
Type: article
Abstract: Generative modelling has become a promising use case for near-term quantum computers. Due to the fundamentally probabilistic nature of quantum mechanics, quantum computers naturally model and learn probability distributions, perhaps more efficiently than can be achieved classically. The quantum circuit Born machine is an example of such a model, easily implemented on near-term quantum computers. However, the Born machine was originally defined to naturally represent discrete distributions. Since probability distributions of a continuous nature are commonplace in the world, it is essential to have a model which can efficiently represent them. Some proposals have been made in the literature to supplement the discrete Born machine with extra features to more easily learn continuous distributions; however, all invariably increase the resources required. In this work, we discuss the continuous variable Born machine, built on the alternative architecture of continuous variable quantum computing, which is much more suitable for modelling such distributions in a resource-minimal way. We provide numerical results indicating the model’s ability to learn both quantum and classical continuous distributions, including in the presence of noise.
Source: Quantum Machine Intelligence
Authors: Ieva Čepaitė, Brian Coyle, Elham Kashefi
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,780
Awards: G10676636, G1849294279, PHOTONICS QUANTUM SAMPLING MACHINE, EPSRC Centre for Doctoral Training in Pervasive Parallelism, Designing Out-of-Equilibrium Many-Body Quantum Systems, G4970100405, G5858470274, EPSRC Hub in Quantum Computing and Simulation, G6930418908, Programmable Atomic Large-Scale Quantum Simulation, G899235810
OpenAlex ID: W3097695244