Quantum Extreme Learning of molecular potential energy surfaces and force fields

• URL: http://arxiv.org/abs/2406.14607v1
• Date: Thu, 20 Jun 2024 18:00:01 GMT
• Title: Quantum Extreme Learning of molecular potential energy surfaces and force fields
• Authors: Gabriele Lo Monaco, Marco Bertini, Salvatore Lorenzo, G. Massimo Palma,
• Abstract summary: A quantum neural network is used to learn the potential energy surface and force field of molecular systems. This particular supervised learning routine allows for resource-efficient training, consisting of a simple linear regression performed on a classical computer. We have tested a setup that can be used to study molecules of any dimension and is optimized for immediate use on NISQ devices. Compared to other supervised learning routines, the proposed setup requires minimal quantum resources, making it feasible for direct implementation on quantum platforms.
• Score: 5.13730975608994
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum machine learning algorithms are expected to play a pivotal role in quantum chemistry simulations in the immediate future. One such key application is the training of a quantum neural network to learn the potential energy surface and force field of molecular systems. We address this task by using the quantum extreme learning machine paradigm. This particular supervised learning routine allows for resource-efficient training, consisting of a simple linear regression performed on a classical computer. We have tested a setup that can be used to study molecules of any dimension and is optimized for immediate use on NISQ devices with a limited number of native gates. We have applied this setup to three case studies: lithium hydride, water, and formamide, carrying out both noiseless simulations and actual implementation on IBM quantum hardware. Compared to other supervised learning routines, the proposed setup requires minimal quantum resources, making it feasible for direct implementation on quantum platforms, while still achieving a high level of predictive accuracy compared to simulations. Our encouraging results pave the way towards the future application to more complex molecules, being the proposed setup scalable.

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