Chemistry is arguably the most important science for humanity. Chemistry is used to understand almost everything about life, including evolution, genetics, metabolism, neuroscience, and disease. Chemistry is also the basis for new drug discovery, new materials, agriculture, food, energy, climate and much more.
Computational Chemistry 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. In recent years, AI has emerged as a powerful tool in chemical discovery, particularly for drug discovery, but it is still limited in its capabilities.
Quantum Computing (QC) is now being promoted as a critical future technology for chemical discovery 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, catalysts, batteries, climate and other applications.
Some Key Questions
Will it ever be possible to build quantum computers capable of fast and accurate molecular electronic structure calculations on useful molecules such as drugs, materials and catalysts? Present estimates are in the 2030 timeframe.
How do we integrate future QC capabilities into the chemical discovery process without excessive, time-consuming human intervention? This is still unclear.
How can we leverage the convergence of QC and Gen AI for chemical discovery? This is an area of great potential opportunity, but there is still much work to do.
There is still 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.
FOR CORPORATE, PRIVATE EQUITY AND VC INVESTORS
Market & technology analysis in the QC/Chemistry domain
Investment candidate search
FOR CORPORATE USERS OF QC IN CHEMISTRY
Support in developing an R&D strategy for QC applications in chemistry
Support in finding the best use-cases and collaboration partners
FOR START-UPS IN THE QC/CHEMISTRY DOMAIN
Support in developing a business growth strategy
Support in fund raising
Nobody knows yet. This is a brief overview of issues and present status.
The impact of quantum computing will vary significantly between business sectors. For some it could be a disruptive game-changer and for others just an incremental efficiency improvement. Here we look at a few different sectors and consider the rationale and impact.