Application-Motivated, Holistic Benchmarking of a Full Quantum Computing Stack

Abstract

Quantum computing systems need to be benchmarked in terms of practical tasks they would be expected to do. Here, we propose 3 "application-motivated" circuit classes for benchmarking: deep (relevant for state preparation in the variational quantum eigensolver algorithm), shallow (inspired by IQP-type circuits that might be useful for near-term quantum machine learning), and square (inspired by the quantum volume benchmark). We quantify the performance of a quantum computing system in running circuits from these classes using several figures of merit, all of which require exponential classical computing resources and a polynomial number of classical samples (bitstrings) from the system. We study how performance varies with the compilation strategy used and the device on which the circuit is run. Using systems made available by IBM Quantum, we examine their performance, showing that noise-aware compilation strategies may be beneficial, and that device connectivity and noise levels play a crucial role in the performance of the system according to our benchmarks.

Benchmarking Quantum Coprocessors in an Application-Centric, Hardware-Agnostic, and Scalable Way

“…Recently, Ref. [MSSD20] proposed a series of benchmarks that comprise the QV, VB and XEB metrics together with the l 1 norm to compare probability distributions. Similar limitations in terms of the classical complexity to compute the metric and difficulty to use it as a proxy for an actual application also apply to this work.…”

Scaling of multi-core quantum architectures

“…These can provide the necessary freedom to explore algorithm parameters like number of qubits, gates and the percentage of two-qubit gates mentioned previously. The importance of using the family of random circuit benchmarks was pointed out by some previous works as well [38,39,40,25,41,42,43], which also introduced various ways of generating them. They differ in type of randomness, shape (width vs. depth), gate density per layer, etc.…”

A decentralizedagent‐basedsemantic service control andself‐adaptationin smart health mobile applications

“…Simon et al [20] presented a mathematical model with Cloud Quantum Computing for deploying large-size health applications. The performance measures utilized in this approach were computational time, storage overhead and computation overhead.…”

“…Several approaches and platforms 4,5,8–18 have been proposed for reconfigurable IoT systems in the field of smart health. The three main categories of approaches are semantic‐based platforms, 4,5,8–11 rules‐based approaches, 12 and metaheuristic‐based approaches 13–18 .…”

“…Several approaches and platforms 4,5,8–18 have been proposed for reconfigurable IoT systems in the field of smart health. The three main categories of approaches are semantic‐based platforms, 4,5,8–11 rules‐based approaches, 12 and metaheuristic‐based approaches 13–18 . For example, Naqvi et al 4 have proposed a semantic‐based context‐driven approach with QoC (Quality of Context) for mobile cloud computing.…”