QB: Quantum Benchmarking

It has been credibly hypothesized that quantum computers will revolutionize multiple scientific and technical fields within the next few decades. 

Examples include machine learning, quantum chemistry, materials discovery, molecular simulation, many-body physics, classification, nonlinear dynamics, supply chain optimization, drug discovery, battery catalysis, genomic analysis, fluid dynamics, and protein structure prediction. For many of these examples, like quantum chemistry and protein structure prediction, quantum computers are hypothesized to be useful simulators because the target problem is inherently quantum mechanical. 

Other examples, like classification and nonlinear dynamics, center around problems that have nothing to do with quantum systems, but involve combinatorial complexity that is intractable for conventional computers.

For each of the fields listed above, it is unclear exactly what size, quality, and configuration of quantum computer – if any – will enable the hypothesized revolutionary advances. 

The Quantum Benchmarking program will estimate the long-term utility of quantum computers by creating new benchmarks that quantitatively measure progress towards specific, transformational computational challenges. In parallel, the program will estimate the hardware-specific resources required to achieve different levels of benchmark performance.