Quantum versus classical generative modelling in finance

Finding a concrete use case for quantum computers in the near term is still an open question, with machine learning typically touted as one of the first fields which will be impacted by quantum technologies. In this work, we investigate and compare the capabilities of quantum versus classical models for the task of generative modelling in machine learning. We use a real world financial dataset consisting of correlated currency pairs and compare two models in their ability to learn the resulting distribution-a restricted Boltzmann machine, and a quantum circuit Born machine. We provide extensive numerical results indicating that the simulated Born machine always at least matches the performance of the Boltzmann machine in this task, and demonstrates superior performance as the model scales. We perform experiments on both simulated and physical quantum chips using the Rigetti QCS(TM) platform, and also are able to partially train the largest instance to date of a quantum circuit Born machine on quantum hardware. Finally, by studying the entanglement capacity of the training Born machines, we find that entanglement typically plays a role in the problem instances which demonstrate an advantage over the Boltzmann machine.

Automated summary

Comparing the capabilities of quantum and classical models in generative modeling in machine learning, it was found that the simulated Born machine matches or exceeds the performance of the Boltzmann machine, especially as the model scales. Entanglement capacity plays a role in problem instances where the Born machine demonstrates an advantage over the Boltzmann machine. Experiments were conducted on simulated and physical quantum chips using the Rigetti QCS(TM) platform, with the largest instance of a quantum circuit Born machine partially trained on quantum hardware.