Physics

Sociology

Qubit

Construct (python library)

Mathematics

Operating system

Data mining

Statistics

Computer engineering

Parallel computing

Artificial intelligence

Set (abstract data type)

Mirroring

Programming language

Class (philosophy)

Measure (data warehouse)

Computer science

Flexibility (engineering)

Quantum computer

Quantum mechanics

Verifiable secret sharing

Communication

Scalability

Quantum

Nature Physics

Quantum computers can now run interesting programs, but each processor’s capability—the set of programs that it can run successfully—is limited by hardware errors. These errors can be complicated, making it difficult to accurately predict a processor’s capability. Benchmarks can be used to measure capability directly, but current benchmarks have limited flexibility and scale poorly to many-qubit processors. We show how to construct scalable, efficiently verifiable benchmarks based on any program by using a technique that we call circuit mirroring. With it, we construct two flexible, scalable volumetric benchmarks based on randomized and periodically ordered programs. We use these benchmarks to map out the capabilities of twelve publicly available processors, and to measure the impact of program structure on each one. We find that standard error metrics are poor predictors of whether a program will run successfully on today’s hardware, and that current processors vary widely in their sensitivity to program structure. Evaluations of quantum computers across architectures need reliable benchmarks. A class of benchmarks that can directly reflect the structure of any algorithm shows that different quantum computers have considerable variations in performance.

Measuring the capabilities of quantum computers