Abstract
In this paper we present the high-level functionalities of a quantum-classical machine learning software, whose purpose is to learn the main features (the fingerprint) of quantum noise sources affecting a quantum device, as a quantum computer. Specifically, the software architecture is designed to classify successfully (more than 99% of accuracy) the noise fingerprints in different quantum devices with similar technical specifications, or distinct time-dependences of a noise fingerprint in single quantum machines.
Highlights
The most promising quantum technology is represented by quantum computers, i.e., quantum devices for quantum computing, among which it is worth mentioning superconducting circuits [6,7], trapped-ions quantum computers [8,9], photonic chips [10,11] and topological qubits [12]
In the paper [17], we have recently observed on some IBM quantum computers that main features of the noise sources affecting the devices are specific of each single computer and have a clear time-dependence
The pipeline designed for the creation of the dataset, set as input of the machine learning (ML) models, is constituted of several scripts that can be customized according to the needs of the user
Summary
Quantum technologies are a fast developing scientific and industrial field [1]. They have been already implemented in several different platforms, as for instance photonic circuits [2,3], and Rydberg atoms [4], superconducting devices [5] and others. In the paper [17], we have recently observed on some IBM quantum computers that main features of the noise sources affecting the devices are specific of each single computer and have a clear time-dependence. For such a purpose, a testbed quantum circuit – composed by a fixed number of qubits – is designed, made run for a sufficient number of times and locally measured in correspondence of each qubit. No quantum noise modelling is required nor, in principle, the testbed circuit has to be controlled by time-dependent pulses [20] For these reasons, the use of a ML technique is the most natural choice to perform classification, since it naturally provides a black-box model with predictive outcomes.
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