Fully scalable randomized benchmarking without motion reversal

• URL: http://arxiv.org/abs/2309.05147v1
• Date: Sun, 10 Sep 2023 21:45:00 GMT
• Title: Fully scalable randomized benchmarking without motion reversal
• Authors: Jordan Hines, Daniel Hothem, Robin Blume-Kohout, Birgitta Whaley, Timothy Proctor
• Abstract summary: BiRB reliably and efficiently extracts the average error rate of a Clifford gate set. Unlike existing RB methods, BiRB does not use motion reversal circuits.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 though 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.

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