FAR-Qu

Sustainable Quantum Utility

We may see near-term practical demonstrations of quantum utility, but how do we know today’s empirical advantages will be sustainable as both quantum computers and problems grow in size and complexity?

Even if quantum computers end up being the fastest way to solve some problems, will they actually be the most cost effective overall?

Will some other resource bottleneck, such as power consumption, prevent practical adoption of an otherwise amazing quantum advantage?

Will quantum advantages for specialized model problems carry over to practical applications?

FAR-Qu’s Approach

Our team designs novel quantum algorithms for a broad range of scientific problems and aims for rigorously establishing sustainable quantum advantages.

Here is our perspective on the above questions:

We may see near-term practical demonstrations of quantum utility, but how do we know today’s empirical advantages will be sustainable as both quantum computers and problems grow in size and complexity?

Sustainable quantum utility claims should be backed by strong evidence or irrefutable mathematical proof of asymptotic advantage that holds as problems and quantum computers evolve.

Even if quantum computers end up being the fastest way to solve some problems, will they actually be the most cost effective overall?

Will some other resource bottleneck, such as power consumption, prevent practical adoption of an otherwise amazing quantum advantage?

We consider sustainable quantum utility under a range of computational resources, including: run time, memory/space, solution accuracy, power consumption, and communication costs. This leaves room for new kinds of unexpected unconventional quantum advantages not focusing solely on speed.

Will quantum advantages for specialized model problems carry over to practical applications?

We craft algorithms for fundamental simulation, optimization, and machine learning problems that are building blocks for many kinds of applications.