Chemical space exploration with quantum computing

Lung-Yi Chen, Tai-Yue Li, Yi‐Pei Li +2 more

The generation of chemical molecular structures is crucial for advancements in drug design, materials science, and related fields. With the rise of artificial intelligence, numerous generative models have been developed to propose promising molecular structures to specific challenges. However, exploring the vast chemical space using classical generative models demands extensive chemical structure data, considerable computational resources, and a large number of model parameters, which hinders their efficiency. Quantum computing presents a promising alternative by exploiting quantum parallelism and entanglement, potentially reducing the computational overhead required for such tasks. In this study, we designed a quantum-based molecular generator (QMG) specifically tailored to generate small molecules containing carbon, nitrogen, and oxygen atoms. This model imposes strict constraints on the quantum circuit's output quantum states, significantly eliminating mathematically invalid connection graphs and enabling more efficient sampling of valid molecular structures. Remarkably, our quantum circuit, utilizing only 134 parameters, is capable of enumerating all molecular structures comprising up to nine heavy atoms, showcasing the parameter efficiency achievable through quantum superposition and entanglement. Our experimental results show that the output generated by this circuit exhibits a high degree of validity and uniqueness after Bayesian optimization, showing comparable performance to classical deep generative models. Furthermore, by fixing specific parameters in the quantum circuits, the quantum generator can constrain the chemical space and exclusively generate chemical molecules containing specified functional groups. This feature underscores its potential value for targeted applications in specific domains, especially in drug discovery. Overall, this compact design not only reduces parameter demands but also enables efficient exploration of a nontrivial portion of chemical space, demonstrating a key advantage of quantum-based generative models over larger classical counterparts.

Akshay Ajagekar, Fengqi You

Kan Hatakeyama‐Sato, Takahiro Kashikawa, Kôichi Kimura +1 more

Efficient screening of chemicals is essential for exploring new materials. However, the search space is astronomically large, making calculations with conventional computers infeasible. For example, an N‐component system of organic molecules generates >1060N candidates. Here, a quantum‐inspired annealing machine is used to tackle the challenge of the large search space. The prototype system extracts candidate chemicals and their composites with desirable parameters, such as melting temperature and ionic conductivity. The system can be at least 104–108 times faster than conventional approaches. Such dramatic acceleration is critical for exploring the enormous search space in virtual screening of materials.

Takumi Kato, Koji Okuwaki, Yuji Mochizuki +3 more

Ushaa Eswaran

This paper provides a comprehensive review of the emerging field of quantum robotics and its potential applications in space exploration and astrobiology. The current state-of-the-art in quantum robotics is discussed, including key capabilities enabled by the integration of quantum computing and quantum sensing with robotics. Potential uses of quantum robots for autonomous exploration, biomolecule and biomarker detection, chemical and biological simulations, and navigation in extraterrestrial environments are explored. Technical challenges such as dealing with space radiation, thermal regulation, power requirements, reliability, and communication networks are examined. Current and future space missions that could benefit from quantum robots, such as Mars rovers, exoplanet searches, and sample return missions, are discussed through case studies. The future outlook and prospects of quantum robotics in transforming space exploration and astrobiology through improved efficiency, accuracy, and autonomous operations are highlighted.

Neil Gershenfeld, Isaac L. Chuang