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Psi-Ontic provides strategy consulting services for the application of quantum computing in chemistry.  Our work is supported by a proprietary database covering hundreds of users, vendors, applications, and research papers for quantum computing in chemistry.

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 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.

AI Applications in Computational Chemistry have grown rapidly over the past few years, particularly for drug discovery and some aspects of materials design. IBM, Microsoft, and BioVia have been leaders in providing cloud-based software for AI in chemistry. Today, there are hundreds of start-ups and major pharma companies exploring AI methods for drug discovery. AI certainly has its place in the future of chemical discovery, but it still has limitations and can require enormous amounts of data and massive energy-intensive computing power.

​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. Also, it has the potential to greatly improve the efficiency of AI chemistry calculations.

​Some Key Questions:

  1. Will it ever be possible to build quantum computers capable of fast and accurate calculations on useful molecules such as drugs, materials, and catalysts? Possibly - present estimates are in the 2030 timeframe.

  2. Are there nearer-term opportunities for QC? These are limited with the present status of quantum computing technology. One potential opportunity is the application of quantum annealers (e.g. D-Wave) to optimize a given set of parameters for a molecule, and thereby reduce a list of millions of molecules to just hundreds of suitable candidates.

  3. How do we integrate future QC capabilities into the chemical discovery process without excessive, time-consuming human intervention (as is required today)? This is still unclear.​

  4. 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.

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



  • Market & technology analysis in the QC/Chemistry domain

  • Investment candidate search

  • Due-diligence support


  • Support in developing an R&D strategy for QC applications in chemistry

  • Support in finding the best use-cases and collaboration partners


  • Support in developing business growth strategy

  • Support in fund raising

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