Three crucial factors are considered In determining which applications can best be used by quantum computing: Progress to date, Difficulty, and Payoff. Most of the applications discussed will be in hybrid platforms, combining both quantum and classical computing in a cloud environment.
Machine learning in the form of voice, image, and handwriting recognition is of considerable current interest. But each – due to its need for extreme accuracy – proves to be difficult and computationally expensive. Still, because of the potential payoff, research using Boltzman distributions is ongoing.
Computational chemistry offers the possibility of large payoffs if quantum computing is used to find the right catalyst in materials science.
Some other significant problems that could produce large payoffs if solved include:
- Replace the Haber process to produce ammonia for fertilizers
- Find new material for a room temperature superconductor
- Find a catalyst that can improve the efficiency of carbon sequestration
- Develop chemistry by which the performance of lithium-ion batteries can be improved
Financial portfolio optimization is yet another area in which quantum computing could make a difference. Presently, simulations for finding the best mix of investments are run on classical computers that require huge amounts of computer time. That time could be reduced and the solutions found improved with quantum technology. If the billions of dollars handled by money managers reached a 1 percent improvement, the return would be considerable.
Quantum computing could improve the managing and scheduling of such large-scale businesses as airports where classical computing is often overwhelmed.
Drug design and cybersecurity are two world-wide issues that could benefit from the use of quantum computing. Simulating drug reaction would speed development and lower costs. Earlier recognition of security threats and mitigating their damage could be objectives best resolved with quantum computing.
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