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.
Highlights
With the dawn of the noisy intermediate-scale quantum (NISQ) (Preskill 2018) device era comes a possibility of performing useful and large-scale computations that implement quantum information processing
In the field of quantum machine learning (QML) (Biamonte et al 2017; Dunjko and Briegel 2018; Ciliberto et al 2018; Benedetti et al 2019; Lamata 2020), the benefits of hybrid quantum-classical (HQC) are key in approaches that employ parameterized quantum circuits (PQC), which act as an ansatz solution to some particular problem that can be optimised classically
These quantum states are obtained via PQCs which are composed of a number of tunable quantum gates with parameters that can be optimised using a classical subroutine
Summary
With the dawn of the noisy intermediate-scale quantum (NISQ) (Preskill 2018) device era comes a possibility of performing useful and large-scale computations that implement quantum information processing. While NISQ technologies do not entail fault-tolerance or large numbers of qubits (generally in the range of about 50–200) which. In the field of quantum machine learning (QML) (Biamonte et al 2017; Dunjko and Briegel 2018; Ciliberto et al 2018; Benedetti et al 2019; Lamata 2020), the benefits of HQC are key in approaches that employ parameterized quantum circuits (PQC) ( referred to as a quantum neural networks), which act as an ansatz solution to some particular problem that can be optimised classically. QML has employed PQCs for several problems, including classification (Farhi and Neven 2018; Schuld and Killoran 2019; Havlıcek et al 2019; Schuld et al 2018; LaRose and Coyle 2020), generative modelling
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
