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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)
JanVdA
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1 Answers1

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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.

Craig Gidney
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