Application of quantum-inspired generative models to small molecular datasets

IEEE International Conference on Quantum Computing and Engineering (QCE), pp. 342–348

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 combine 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.

Authors 5

1. Charles Moussa Aachen

   Leiden University

   Affiliation as printed

   Leiden University,LIACS,Leiden,Netherlands

   LIACS, Leiden University, Leiden, Netherlands

2. Hao Wang Aachen

   Leiden University

   Affiliation as printed

   Leiden University,LIACS,Leiden,Netherlands

   LIACS, Leiden University, Leiden, Netherlands

3. Mauricio Araya‐Polo

   Total (United States)

   Affiliation as printed

   EP Research & Technology, TotalEnergies,Houston,USA

4. Thomas Bäck Aachen

   Leiden University

   Affiliation as printed

   Leiden University,LIACS,Leiden,Netherlands

   LIACS, Leiden University, Leiden, Netherlands

5. Vedran Dunjko Aachen

   Leiden University

   Affiliation as printed

   Leiden University,LIACS,Leiden,Netherlands

   LIACS, Leiden University, Leiden, Netherlands