Classical versus quantum models in machine learning: insights from a finance application
[3]
- Zapata Computing Canada Inc., Toronto, ON (Canada); National Australia Bank, London (United Kingdom)
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Rigetti Computing, Berkeley, CA (United States)
- Zapata Computing Canada Inc., Toronto, ON (Canada); Rigetti Computing, Berkeley, CA (United States); Univ. College London (United Kingdom)
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.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1648867
- Journal Information:
- Machine Learning: Science and Technology, Vol. 1, Issue 3; ISSN 2632-2153
- Publisher:
- IOP PublishingCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Quantum versus classical generative modelling in finance
|
journal | April 2021 |
| Quantum Computer-Aided design of Quantum Optics Hardware | text | January 2020 |
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