Emphasizing the importance of this alliance, Peter Altmaier, Germany’s Federal Minister for Economic Affairs and Energy, said: “I am delighted that QUTAC has brought so many leading companies together to help Germany move forward in this key sector.”

Specifically, applications for the technology, chemical and pharmaceutical, insurance and automotive industries are to be brought to market maturity covering highly interesting areas, e.g., in logistics, transport, chemicals and the financial sector. Initial founding members comprise chemical and pharmaceutical companies BASF, Boehringer Ingelheim and Merck, technology companies Bosch, Siemens, Infineon and SAP, auto makers BMW and Volkswagen, and insurance company Munich Re.

Computational Chemistry Today

Not necessarily being at the forefront in adoption of disruptive innovation the chemical industry still has a long tradition when it comes down to using advanced computational chemistry tools with the purpose to design new molecules or to describe chemical reactions. For example, ab initio, semi-empirical or DFT (Density Functional Theory) methods have been applied already in the 1950s to describe simple molecular interactions or model molecular structure-response relationships, leveraging the basic principles of quantum mechanics. However, those methods were limited to very simple molecules, like e.g., molecular hydrogen, and failed when applied to bigger molecules like proteins or to solid materials for modelling e.g., heterogenous catalytic reactions.

Significant advancements in computational power and data storage capacity over the last years, along with falling prices for those, provided unprecedented opportunities for “classical” computational chemistry to accelerate development of new products and formulations along with reduced time to market. In 2018 BASF launched its 1.75 Petaflop Supercomputer “Quriosity”. With Quriosity, much more complex models and simulations are possible, in which significantly more parameters can be varied. This could not only result in substantially reduced development times, but also previously unknown relationships can be recognized and used to advance completely new research approaches. Examples are new polymer structures or innovative catalysts. By the same token, CAS’s SciFinderⁿ tool analyzes the vast amount of documented reactions and synthesis routes collected in CAS and presents results in an intuitive retrosynthesis plan which significantly narrows down time consuming lab trials for developing new target structures.

Opening new Frontiers with Quantum Computing

As an article from McKinsey & Company points out very well, making quantum computing commercially viable could not only dramatically boost the effectiveness of R&D and change the way new products are developed, but also act as a disruptive game changer along the entire chemical value chain.

In R&D, for example, development of new crop protection chemicals, biocides or active pharmaceutical ingredients (APIs) could experience new breakthroughs in terms of speed and performance and might even lead into a new area of blockbusters. Battery materials, semiconductors, magnets, and superconductors as well as OLEDs could be designed with unprecedented levels of precision. The same principles apply to developing innovative formulations by modelling specific interactions that formulation components should provide to serve a special application purpose.

In Manufacturing, simulations based on quantum computing could be used to better understand reaction mechanisms, to design improved catalysts, suppress by-products and to optimize process conditions. Finding and leveraging non-intuitive data correlations could help in maximizing throughput, product quality and asset performance while minimizing costs and energy consumption.

Boundary Conditions and Path Forward

Currently quantum computers are still a lab curiosity and researchers produce qubits, among others, from ions or superconducting loops, so-called SQUIDs. Maximum strings of qubits reached so far are in the range of 50 which would allow for precisely modelling a molecule just slightly bigger than ethanol, but leaders in this space already revealed a 5.000+ qubit system. There are numerous companies engaged in developing and scaling such computers ranging from large global technology players to small start-ups, and right now it is quite open who will win the race. Besides the extreme conditions necessary (complex cooling techniques) to run quantum computers, there are other issues like manipulating qubits and isolating them at the same time from the environment. Also, the errors increase exponentially with the number of qubits. This is particularly problematic because the usual bug fixes do not work. On top of that, completely new types of algorithms need to be developed. Overall, above challenges call for a “Quantum Computing as a Service” model as only very few companies can probably afford, neither may have the skills to operate and maintain a quantum computer in their own four walls.

According to Gartner’s Hype Cycle (see below) quantum computing is still on its way up the expectation curve and commercial viability maybe reached in 5 to 10 years from now.

However, to take advantage of this highly innovative technology chemical companies should start now looking into the right partnerships, make sure that possible use cases and applications are well understood, and that the right skills will be built on-time in-house to finally implement those.

The recent launch of QUTAC is exactly geared towards this goal and intended to create the basis for a successful industrialization of quantum computing in Germany and Europe, help them to become leaders and finally stay at the top.

Stefan Guertzgen, CHEManager