Fully scalable randomized benchmarking without motion reversal

TL;DR

BiRB is a scalable randomized benchmarking protocol that simplifies error rate estimation by using circuits with i.i.d. layers, avoiding motion reversal, and enabling application to many qubits.

Contribution

BiRB introduces a new RB method that eliminates the need for motion reversal circuits, simplifying analysis and scaling to larger quantum systems.

Findings

Reliable extraction of error rates for Clifford gates

Simplifies benchmarking process without motion reversal

Scales efficiently to many qubits

Abstract

We introduce binary randomized benchmarking (BiRB), a protocol that streamlines traditional RB by using circuits consisting almost entirely of i.i.d. layers of gates. BiRB reliably and efficiently extracts the average error rate of a Clifford gate set by sending tensor product eigenstates of random Pauli operators through random circuits with i.i.d. layers. Unlike existing RB methods, BiRB does not use motion reversal circuits -- i.e., circuits that implement the identity (or a Pauli) operator -- which simplifies both the method and the theory proving its reliability. Furthermore, this simplicity enables scaling BiRB to many more qubits than the most widely-used RB methods.