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.

翻译：量子与量子启发的机器学习因量子计算（尤其是近期设备）的普及而成为一个充满前景且具挑战性的研究领域。理论贡献指出，生成建模是实现这些技术首批真实世界量子优势实例的有前途方向。一些实证研究也展示了这种潜力，特别是当考虑基于张量网络的量子启发模型时。在本工作中，我们将基于张量网络的生成模型应用于分子发现问题。我们的方法利用了两个小分子数据集：QM9数据集中包含$4989$个分子的子集，以及道达尔能源公司内部包含$516$个已验证抗氧化剂的小数据集。我们使用多种基于样本的指标（反映各任务上的学习性能）和每个任务$3$个相关分子指标的多目标性能，将多个张量网络模型与生成对抗网络进行比较。我们还对模型输出进行了组合，并实证表明这种组合可能是有益的，倡导将经典生成学习与量子（启发式）生成学习统一起来。