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There is much uncertainty about the potential value of Quantum Computing in the process of chemical discovery. Psi-Ontic provides strategy consulting and M&A advisory services for investors in this domain.

 

Chemistry is the most important science for humanity. Chemistry is used to understand everything about life, including evolution, genetics, metabolism, neuroscience, and disease. Chemistry is the basis for new drug discovery, medical care, agriculture, food, materials, energy, climate and more. Every company that produces a Physical Product relies on the science of Chemistry, from steel production to a COVID vaccine.

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Computational Chemistry is a science that has evolved over the past 60 years, with the aim of eliminating or reducing the need for physical laboratory experiments via computational models (just as CAE has been highly successful in engineering). The goal is to revolutionize the scope, speed, and success of chemical discovery. Unfortunately, this goal has not fully materialized for many chemistry problems. The computational models are too complex to be solved on any high-performance computers available today, for problems of real-world interest and to a required degree of accuracy.

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Quantum Computational Chemistry is the application of Quantum Computing (QC) to computational chemistry and is now being promoted as a critical future technology for chemical discovery in the 2030+ timeframe. Chemistry has been described as the "Killer App" for QC and major consulting firms have estimated many $Billions of potential value creation for QC in chemistry. Over 50 major corporations have publicly announced R&D projects in the application of QC in chemistry. There are dozens of vendors supporting these efforts, from IBM, Google, and Microsoft to numerous start-ups, plus many universities.  The future promise of QC in chemistry is that it will be able to run calculations in minutes and for much larger molecules (such as drugs and proteins), and for materials and biological systems. However, even if QC can speed up calculations, it is still not clear how it will radically improve the process of chemical discovery.

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Machine Learning (ML) versus QC - which will win? It is not a question of winning; both are just potential tools in the toolbox of chemical discovery. Human intellect is still the major ingredient for chemical discovery. ML is simply pure mathematics, a "fancy least-squared analysis of a cost function". As such, ML is a no-brainer. We just need vast chemistry databases, massive amounts of computing power and hope that the calculations will converge to the right answer. In contrast, QC is based on the principles of quantum mechanics, which is a theory of physics still without universal acceptance. We have no idea whether QC will ever be useful for chemical discovery. However, if it can be made to work, it could leapfrog the present power of ML.

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There is much uncertainty about the potential value of QC in the process of chemical discovery. Psi-Ontic works with clients to provide a factual basis to help them navigate the uncertainty.

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