Classical versus Quantum Models in Machine Learning: Insights from a Finance Application

TL;DR

This paper compares classical restricted Boltzmann machines and quantum circuit Born machines on real-world financial data, demonstrating quantum models' potential superior performance with near-term quantum hardware.

Contribution

It provides the first direct comparison of classical and quantum generative models on real-world datasets, highlighting quantum models' advantages in practical scenarios.

Findings

Quantum models outperform classical RBMs on financial datasets with equal resources.

QCBMs are implementable on near-term ion-trap quantum hardware.

Quantum models show promise for real-world applications in finance.

Abstract

Although several models have been proposed towards assisting machine learning (ML) tasks with quantum computers, a direct comparison of the expressive power and efficiency of classical versus quantum models for datasets originating from real-world applications is one of the key milestones towards a quantum ready era. Here, we take a first step towards addressing this challenge by performing a comparison of the widely used classical ML models known as restricted Boltzmann machines (RBMs), against a recently proposed quantum model, now known as quantum circuit Born machines (QCBMs). Both models address the same hard tasks in unsupervised generative modeling, with QCBMs exploiting the probabilistic nature of quantum mechanics and a candidate for near-term quantum computers, as experimentally demonstrated in three different quantum hardware architectures to date. To address the question of the performance of the quantum model on real-world classical data sets, we construct scenarios from a probabilistic version out of the well-known portfolio optimization problem in finance, by using time-series pricing data from asset subsets of the S\&P500 stock market index. It is remarkable to find that, under the same number of resources in terms of parameters for both classical and quantum models, the quantum models seem to have superior performance on typical instances when compared with the canonical training of the RBMs. Our simulations are grounded on a hardware efficient realization of the QCBMs on ion-trap quantum computers, by using their native gate sets, and therefore readily implementable in near-term quantum devices.