Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry

• URL: http://arxiv.org/abs/2210.16823v2
• Date: Thu, 12 Jan 2023 09:48:02 GMT
• Title: Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry
• Authors: Po-Yu Kao, Ya-Chu Yang, Wei-Yin Chiang, Jen-Yueh Hsiao, Min-Hsiu Hsieh, and Yen-Chu Lin
• Abstract summary: We proposed a hybrid quantum-classical generative adversarial network (GAN) for small molecule discovery. We substituted each element of GAN with a variational quantum circuit (VQC) and demonstrated the quantum advantages in the small drug discovery.
• Score: 8.98977891798507
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The drug development process is not only time and resource-consuming but also has a low probability of success. Recent advances in machine learning and deep learning technology have reduced costs and improved pharmaceutical research and development. De novo drug design with desired biological activities is crucial for developing novel therapeutics for patients. It is also an important step to keep the drug discovery pipeline moving forward. Artificial intelligence has pushed the limit of conventional drug design approaches, and quantum computing has demonstrated its advantages in different applications, e.g., solving routing problems and stock price forecasting. We proposed a hybrid quantum-classical generative adversarial network (GAN) for small molecule discovery. We substituted each element of GAN with a variational quantum circuit (VQC) and demonstrated the quantum advantages in the small drug discovery. Utilizing a VQC in the noise generator of GAN to generate small molecules achieves better physicochemical properties and performance in the goal-directed benchmark than the classical counterpart. Moreover, we demonstrate the potential of a VQC with only tens of learnable parameters in the generator of GAN to generate small molecules, which is a more complex problem than other quantum computing applications. In the end, we also demonstrate the quantum advantage of a VQC in the discriminator of GAN. In this hybrid model, the number of learnable parameters is significantly less than the classical ones, and it can still generate valid molecules. The hybrid model with only tens of training parameters in the quantum discriminator outperforms the MLP-based one in terms of generated molecule properties and KL-divergence.

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