TITLE: Application of quantum-inspired generative models to small molecular datasets 

ABSTRACT: Quantum and quantum-inspired machine learning has emerged as a promising and
challenging research field due to the increased popularity of quantum
computing, especially with near-term devices. Theoretical contributions point
toward generative modeling as a promising direction to realize the first
examples of real-world quantum advantages from these technologies. A few
empirical studies also demonstrate such potential, especially when considering
quantum-inspired models based on tensor networks. In this work, we apply
tensor-network-based generative models to the problem of molecular discovery.
In our approach, we utilize two small molecular datasets: a subset of $4989$
molecules from the QM9 dataset and a small in-house dataset of $516$ validated
antioxidants from TotalEnergies. We compare several tensor network models
against a generative adversarial network using different sample-based metrics,
which reflect their learning performances on each task, and multiobjective
performances using $3$ relevant molecular metrics per task. We also combined
the output of the models and demonstrate empirically that such a combination
can be beneficial, advocating for the unification of classical and
quantum(-inspired) generative learning.
Quantum and quantum-inspired machine learning has emerged as a promising and
challenging research field due to the increased popularity of quantum
computing, especially with near-term devices. Theoretical contributions point
toward generative modeling as a promising direction to realize the first
examples of real-world quantum advantages from these technologies. A few
empirical studies also demonstrate such potential, especially when considering
quantum-inspired models based on tensor networks. In this work, we apply
tensor-network-based generative models to the problem of molecular discovery.
In our approach, we utilize two small molecular datasets: a subset of $4989$
molecules from the QM9 dataset and a small in-house dataset of $516$ validated
antioxidants from TotalEnergies. We compare several tensor network models
against a generative adversarial network using different sample-based metrics,
which reflect their learning performances on each task, and multiobjective
performances using $3$ relevant molecular metrics per task. We also combined
the output of the models and demonstrate empirically that such a combination
can be beneficial, advocating for the unification of classical and
quantum(-inspired) generative learning.
Application of quantum-inspired generative models to small molecular datasets
http://arxiv.org/abs/2304.10867v1