One of the most promising applications of a quantum computing is the design of new drugs and materials:

Quantum computers promise to run calculations far beyond the reach of any conventional supercomputer. They might revolutionize the discovery of new materials by making it possible to simulate the behavior of matter down to the atomic level.

source: MIT Technology Review - Will Knight - February 21, 2018

At this moment quantum computers are not powerful enough to do these calculations / simulations. So we have not yet the proper hardware for this.

But assuming we have today quantum computers that are powerful enough, do we know how to use them to revolutionize the design of better drugs and materials in some domains ?

So is the current obstacle for this revolution that we do not have yet the proper hardware (= powerful quantum computer) or are there actually 2 obstacles:

  • having powerful quantum computer (hardware)
  • knowing which quantum algorithms are needed (software)

1 Answer 1


There is not a complete story from "run quantum computation" to "make a billion dollars via slightly better batteries". The vague idea is that a new tool capable of giving new insights into the behavior of materials will lead to important discoveries.

It's unrealistic to expect a complete quantum-compute-to-engineering-improvement story since the most important discoveries are often the ones that aren't foreseen. I'm sure there are thousands, if not millions, of examples of this happening with classical computers. For example, the study of chaos theory can be traced back to computer simulations of weather behaving in an unexpected way.

One specific thing we know quantum computers will be able to do reasonably well is compute the ground state energy of small but classically hard systems. Error-corrected algorithms to do this have improved immensely over the past few years (from runtimes of months with very very optimistic assumptions about hardware [1] down to runtimes of hours with plausible 15-years-from-now hardware assumptions [2]). There has also been substantial improvement in NISQ algorithms, though it remains to be seen if they will improve enough to successfully run classically hard cases on non-error-corrected hardware.

Apparently knowing the ground state energy of a system allows predicting reaction rates and other important chemistry numbers. But I'm not a chemist so I can't give much more detail than that.

  • 1
    $\begingroup$ Thanks for the response. if you say "down to runtimes of hours with plausible 15-years-from-now hardware assumptions" do you mean that it is likely (or plausible) that within 15 years we can calculate the ground state energy of those systems within hours on quantum computers ? In same paragraph you also mention "small" system, but the nitrogenase covered in [1] doesn't really look small. So what do you mean by small ? $\endgroup$
    – JanVdA
    Sep 3, 2018 at 15:25
  • $\begingroup$ Different algorithms can have different ideas of what "small", but IIRC it basically comes down to "how many places/orbitals can electrons occupy?" and/or "how well does the basis you have chosen approximate the system?". You are right that you would not want to simulate the entire Nitrogenase molecule. I think you would focus on just the FeMo cofactor part. I would consider FeMoCo "large" because it has thousands of electrons / places electrons can be, which will increase the runtime of the algorithm I linked from hours to days. But I really don't understand the chemistry enough to be sure. $\endgroup$ Sep 3, 2018 at 15:44
  • $\begingroup$ Thanks for the response. I am a bit wondering what is your answer on my question: "Assuming we have today quantum computers that are powerful enough, do we know how to use them to revolutionize the design of better drugs and materials in some domains ?" I think in your response there are elements supporting the "yes" as well as the "no". So this confuses me a bit. $\endgroup$
    – JanVdA
    Sep 4, 2018 at 11:29
  • 2
    $\begingroup$ My answer is that it's complicated and it should confuse you. $\endgroup$ Sep 4, 2018 at 17:12

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