Sebastian Brandhofer

Abstract

While scalable, fully error corrected quantum computing is years or even decades away, there is considerable interest in noisy intermediate-scale quantum computing (NISQ). In this paper, we introduce the ArsoNISQ framework that determines the tolerable error rate of a given quantum algorithm computation, i.e. quantum circuits, and the success probability of the computation given a success criterion and a NISQ computer. ArsoNISQ is based on simulations of quantum circuits subject to errors according to the Pauli error model. ArsoNISQ was evaluated on a set of quantum algorithms that can incur a quantum speedup or are otherwise relevant to NISQ computing. Despite optimistic expectations in recent literature, we did not observe quantum algorithms with intrinsic robustness, i.e. algorithms that tolerate one error on average, in this evaluation. The evaluation demonstrated, however, that the quantum circuit size sets an upper bound for its tolerable error rate and quantified the difference in tolerate error rates for quantum circuits of similar sizes. Thus, the framework can assist quantum algorithm developers in improving their implementation and selecting a suitable NISQ computing platform. Extrapolating the results into the quantum advantage regime suggests that the error rate of larger quantum computers must decrease substantially or active quantum error correction will need to be deployed for most of the evaluated algorithms

Abstract

First quantum computers very recently have demonstrated “quantum supremacy” or “quantum advantage”: Executing a computation that would have been impossible on a classical machine. Today’s quantum computers follow the NISQ paradigm: They exhibit error rates that are much higher than in conventional electronics and have insufficient quantum resources to support powerful error correction protocols. This raises questions which relevant computations are within the reach of NISQ architectures. Several “NISQ-era algorithms” are assumed to match the specifics of such computers; for instance, variational optimisers are based on intertwining relatively short quantum and classical computations, thus maximizing the chances of success. This paper will critically assess the promise and challenge of NISQ computing. What has this field achieved so far, what are we likely to achieve soon, where do we have to be skeptical and wait for the advent of larger-scale fully error-corrected architectures?

Abstract

On-chip instrumentation is mandatory for efficient bring-up, test and diagnosis, post-silicon validation, as well as in-field calibration, maintenance, and fault tolerance. Reconfigurable scan networks (RSNs) provide a scalable and efficient scan-based access mechanism to such instruments. The correct operation of this access mechanism is crucial for all manufacturing, bring-up and debug tasks as well as for in-field operation, but it can be affected by faults and design errors. This work develops for the first time fault-tolerant RSNs such that the resulting scan network still provides access to as many instruments as possible in presence of a fault. The work contributes a model and an algorithm to compute scan paths in faulty RSNs, a metric to quantify its fault tolerance and a synthesis algorithm that is based on graph connectivity and selective hardening of control logic in the scan network. Experimental results demonstrate that fault-tolerant RSNs can be synthesized with only moderate hardware overhead.