Benchmarking Quantum Processor Performance at Scale
As quantum processors grow, new performance benchmarks are required to
capture the full quality of the devices at scale. While quantum volume is an
excellent benchmark, it focuses on the highest quality subset of the device and
so is unable to indicate the average performance over a large number of
connected qubits. Furthermore, it is a discrete pass/fail and so is not
reflective of continuous improvements in hardware nor does it provide
quantitative direction to large-scale algorithms. For example, there may be
value in error mitigated Hamiltonian simulation at scale with devices unable to
pass strict quantum volume tests. Here we discuss a scalable benchmark which
measures the fidelity of a connecting set of two-qubit gates over $N$ qubits by
measuring gate errors using simultaneous direct randomized benchmarking in
disjoint layers. Our layer fidelity can be easily related to algorithmic run
time, via $\gamma$ defined in Ref.\cite{berg2022probabilistic} that can be used
to estimate the number of circuits required for error mitigation. The protocol
is efficient and obtains all the pair rates in the layered structure. Compared
to regular (isolated) RB this approach is sensitive to crosstalk. As an example
we measure a $N=80~(100)$ qubit layer fidelity on a 127 qubit fixed-coupling
"Eagle" processor (ibm\_sherbrooke) of 0.26(0.19) and on the 133 qubit
tunable-coupling "Heron" processor (ibm\_montecarlo) of 0.61(0.26). This can
easily be expressed as a layer size independent quantity, error per layered
gate (EPLG), which is here $1.7\times10^{-2}(1.7\times10^{-2})$ for
ibm\_sherbrooke and $6.2\times10^{-3}(1.2\times10^{-2})$ for ibm\_montecarlo.
David C. McKay Ian Hincks Emily J. Pritchett Malcolm Carroll Luke C. G. Govia Seth T. Merkel