A guide through hype about computational chemistry on quantum computer

One of the many problems with hype in science is that it glosses over problems that means they do not get addressed which ultimately hinders real scientific progress. 

There is a lot of hype about how quantum computers will be able to solve problems in materials science that are of industrial significance and thus "herald a new era of chemical research". Such claims are carefully examined and deconstructed in the following preprint. Most of the authors are at Schrodinger, Inc.

How will quantum computers provide an industrially relevant computational advantage in quantum chemistry?

V.E. Elfving, B.W. Broer, M. Webber, J. Gavartin, M.D. Halls, K. P. Lorton, A. Bochevarov

The article is also a useful guide to current state-of-the-art computational chemistry on classical computers.

I reproduce most of the paper abstract below as it is helpful summary.

Numerous reports claim that quantum advantage, which should emerge as a direct consequence of the advent of quantum computers, will herald a new era of chemical research because it will enable scientists to perform the kinds of quantum chemical simulations that have not been possible before. Such simulations on quantum computers, promising a significantly greater accuracy and speed, are projected to exert a great impact on the way we can probe reality, predict the outcomes of chemical experiments, and even drive design of drugs, catalysts, and materials. 

In this work we review the current status of quantum hardware and algorithm theory and examine whether such popular claims about quantum advantage are really going to be transformative. We go over subtle complications of quantum chemical research that tend to be overlooked in discussions involving quantum computers. 

We estimate quantum computer resources that will be required for performing calculations on quantum computers with chemical accuracy for several types of molecules. In particular, we directly compare the resources and timings associated with classical and quantum computers for the molecules H2 for increasing basis set sizes, and Cr2 for a variety of complete active spaces (CAS) within the scope of the CASCI and CASSCF methods. The results obtained for the chromium dimer enable us to estimate the size of the active space at which computations of non-dynamic correlation on a quantum computer should take less time than analogous computations on a classical computer. Using this result, we speculate on the types of chemical applications for which the use of quantum computers would be both beneficial and relevant to industrial applications in the short term.

The authors present a useful typology of claims of quantum advantage that are irrelevant.

1. Irrelevance due to availability of accurate experimental results. 

2. Irrelevance due to availability of conventional computational results. 

3. Irrelevance due to real world complexity:

When simulated chemical processes are very complicated and involve potentially hundreds of intermediates, conformations, or reaction paths, as in catalytic and metabolic pathways, the real research bottleneck lies in a combinatorial explosion of possibilities to probe with simulation.

4. Irrelevance to industrial applications

Many of the issues discussed in the preprint are not unrelated to those associated with hype about using machine learning in computational materials science, and are beautifully critiqued by Roald Hoffmann and Jean-Paul Malrieu.