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arXiv:1207.3313 · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE

Modeling of quantum noise and the quality of hardware components of quantum computers

Yu. I. Bogdanov, Andrey Yu. Chernyavskiy, Alexander Holevo, V. F. Lukichev, A. A. Orlikovsky

Published Jan 8, 2013• 14 citations
arXivHTMLPDF

Abstract

In the present paper methods and algorithms of modeling quantum operations for quantum computer integrated circuits design are developed. The results of modeling of practically important quantum gates: controlled-NOT (CNOT), and controlled Z-transform (CZ) subject to different decoherence mechanisms are presented. These mechanisms include analysis of depolarizing quantum noise and processes of amplitude and phase relaxation.

DOI
10.1117/12.2017414

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In the present paper methods and algorithms of modeling quantum operations for quantum computer integrated circuits design are developed. The results of modeling of practically important quantum gates: controlled-NOT (CNOT), and controlled Z-transform (CZ) subject to different decoherence mechanisms are presented. These mechanisms include analysis of depolarizing quantum noise and processes of amplitude and phase relaxation.
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The theory of quantum computing is being actively developed. Despite the fact that quantum computing device has some peculiarities, any task designed for a classical computer can be reproduced on a quantum system. The aim of the research is to develop a methodology for constructing models of quantum systems simulators using hardware simulators. The subject of this research is methods for constructing quantum computing devices and systems. In the course of the research, the system analysis methods and computer modeling, methods of object-oriented design were used. Modern high-level languages were used to solve the problems of constructing a modular quantum system with an open architecture. The work of a quantum microarchitecture using an algorithm for optimizing a quantum computational process has been developed and modeled. A universal methodology for modeling algorithms of a quantum nature using the hardware core and requirements for the mutual operation of software and hardware components for the efficient operation of a quantum system is derived.
The article is devoted to the consideration of the fundamental principles of op-eration of quantum computing devices. Particular attention is paid to the key concepts of quan-tum mechanics, including superposition, entanglement and measurement of states, as well as their application in computing processes. The main types of qubits, quantum logic gates, princi-ples of constructing algorithms and hardware platforms for implementing quantum computing are described. Current problems, such as decoherence and error correction, as well as the pro-spects for using quantum computers in science, cryptography and modeling of complex systems are discussed.
This study investigates hybrid quantum neural networks for fruit quality assessment, with a focus on the impact of the entangling gate choice. Two architectures were developed: NNQEv1, utilizing controlled-NOT (CNOT) gates, and NNQEv2, employing controlled-phase (CZ) gates. A theoretical justification is provided, based on gate decomposition and hardware-aware noise considerations, suggesting the CZ-based architecture is likely to be more stable. The performance of the models was evaluated through the computational execution of their quantum circuits on classical hardware and compared against classical and state-of-the-art deep learning models. The proposed models demonstrated competitive performance, achieving test accuracies of 98.7% on MNIST, 98.6% on the FruitQ dataset, and 96.7% on a custom, data-scarce Apple dataset. The experimental results align with the theoretical analysis: the CZ-based NNQEv2 model, when compared to the CNOT-based NNQEv1, consistently showed more stable training dynamics and yielded tighter confidence intervals in cross-validation. This work presents a foundational, computational study on the role of gate-level design choices, intended to inform the development of future quantum machine learning algorithms.