Randomized benchmarking into the quantum advantage regime

Randomized benchmarking (RB) methods are widely used for quantifying the performance of quantum processors. However, most existing protocols are limited in scalability, e.g., because they require classical computations that scale exponentially in the number of qubits. In this talk I will present scalable RB protocols that can benchmark universal gate sets, mid-circuit measurements, and compiler performance. These techniques are practical even in the many-qubit regime, where classical simulations of general circuits are infeasible. I will show how random circuits with a reflection structure, known as randomized mirror circuits, can be used to efficiently and reliably estimate the average error rate of universal gate sets. I will then show how these methods can be adapted to construct scalable and efficient full-stack quantum computing benchmarks, including a scalable version of the quantum volume benchmark. RB with mirror circuits uses a streamlined, gate-efficient fidelity estimation technique. A similar technique can be used to simplify RB of Clifford gates by removing its inversion step. I will show how this idea enables RB of mid-circuit measurements, which are a critical quantum computing primitive that cannot be benchmarked using conventional RB methods. Using theory, simulations, and experiments on contemporary quantum processors, I will show that our RB methods are scalable, reliable, and powerful tools for understanding quantum computer performance. Our experimental results demonstrate the importance of scalable benchmarks for fully capturing the error present in medium- and large-scale quantum processors.