Characterizing large-scale quantum computers via cycle benchmarking

Abstract

Quantum computers promise to solve certain problems more efficiently than their digital counterparts. A major challenge towards practically useful quantum computing is characterizing and reducing the various errors that accumulate during an algorithm running on large-scale processors. Current characterization techniques are unable to adequately account for the exponentially large set of potential errors, including cross-talk and other correlated noise sources. Here we develop cycle benchmarking, a rigorous and practically scalable protocol for characterizing local and global errors across multi-qubit quantum processors. We experimentally demonstrate its practicality by quantifying such errors in non-entangling and entangling operations on an ion-trap quantum computer with up to 10 qubits, and total process fidelities for multi-qubit entangling gates ranging from for 2 qubits to for 10 qubits. Furthermore, cycle benchmarking data validates that the error rate per single-qubit gate and per two-qubit coupling does not increase with increasing system size.

Accrediting outputs of noisy intermediate-scale quantum computing devices

“…On the contrary, benchmarking isolated gates can sometimes yield over-estimates of their fidelities [21], and consequently of the fidelity of the resulting circuit. We note that noise of the type N2 excludes unbounded gate-dependent errors in single-qubit gates such as systematic over-or under-rotations, as also is the case for other works [16][17][18][19][20][21].…”

“…Another approach employed in experiments consists of individually testing classes of gates present in the target circuit. This is typically undertaken using a family of protocols centered around randomized benchmarking and its extensions [16][17][18][19][20][21]. These protocols allow extraction of the fidelity of gates or cycles of gates and can witness progresses towards fault-tolerant quantum computing [22].…”

“…In addition to its ready implementability on NISQ devices, our accreditation protocol can detect all types of noise typically considered by techniques centered around randomized benchmarking and its extensions [16][17][18][19][20][21]. Moreover it can detect noise that may be missed by those techniques such as noise correlated in time.…”

t|ket⟩: a retargetable compiler for NISQ devices

“…Incorporating a very naive noise model into t|ket 's qubit placement algorithm (Section 9.2) made a noticeable difference our results. However it is well known that the noise channels in real devices are much more complex and more difficult to characterise [95,60,96]. Incorporating better analysis of the device errors into the compilation process, and techniques to suppress and mitigate errors [25,23,22] surely have a role to play in the compilation process for NISQ devices for the foreseeable future.…”

“…Attempting to use the actual fidelity as a cost function would require accurate simulation of the quantum circuit with realistic noise models, which is both computationally expensive and highly dependent on a specific target architecture. Further, because real devices have noise sources that are complex and hard to characterise, simple extrapolation from single-gate performance can significantly overestimate the actual performance of the device, necessitating more sophisticated, holistic measures [58,59,60]. However, simpler metrics can give good, device-independent approximations to noise.…”