Measuring progress towards useful quantum computation using precision benchmarks

Measuring a quantum computer’s capability, i.e., learning what circuits it can run with high fidelity, is essential for understanding that device’s computational power and for tracking technological progress. However, now that state-of-the-art quantum computers can run circuits that are infeasible to classically simulate, even directly verifying that a quantum computer has executed a specific computation with some given fidelity has become practically impossible (for general computations). In this talk, I will present methods for learning a quantum computer’s capability that are efficient and scalable. I will show how a method called mirror circuit fidelity estimation can be used to efficiently estimate the fidelity with which a quantum computer can implement any given circuit, on any number of qubits. I will then show how this method can be used to create efficient and principled algorithmic benchmarks that measure progress towards implementing a full-scale algorithm. To fully understand a quantum computer’s capability, we must go beyond measuring a processor’s execution fidelity for one or more given circuits: we need to predict this fidelity for general circuits. I will discuss methods for constructing models for a quantum computer’s capability, including conventional error models as well as machine learned surrogates, and highlight the main scientific challenges in creating accurate capability models.

SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.