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Electronics

Effect of Additive Noise on the Performance of Quantum Computing: An Engineering Approach

Rajnish Kumar, Shlomi Arnon, Torben Larsen

Published Jul 7, 2026• 0 citations
Publisher

Abstract

Quantum computing algorithms are usually built by using gates and circuits to manipulate qubits. In this article, our analysis examines the impact of additive noise in a quantum circuit as an engineering simplification model. We evaluate these effects by computing the probability, fidelity, and signal-to-noise ratio (SNR). We study quantum noise from an amplitude-domain signal-processing perspective, bridging classical additive noise models with quantum state perturbations. We have analyzed the results of various quantum gates, such as X, Y, Z, and Hadamard gates. This study provides an approximation of the quantitative effect and qualitative analysis of the effect of additive noise on quantum gates, thereby contributing to a deeper understanding of the challenges and potential solutions in quantum computing. However, in some cases, the model does not represent the physical results due to approximation.

DOI
10.3390/electronics15132975

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Quantum computing promises revolutionary advancements across various fields, yet its realization hinges critically on the reliability of its hardware. Unlike classical computing, quantum systems are exceptionally sensitive to environmental noise and prone to errors, leading to significant challenges in maintaining coherence and accurate computation. This research presents a formal model designed to rigorously analyze the reliability of quantum computing hardware. The model incorporates probabilistic representations of quantum gates and qubits, alongside noise models that capture various sources of error, and evaluates the effectiveness of fault-tolerance techniques. The core of the model is a simulation framework allowing for comprehensive evaluation of the impact of these factors on the overall system performance. This approach provides a quantitative basis for designing more robust quantum computing systems and optimizing operational strategies, moving beyond intuitive estimations and towards a scientifically grounded understanding of quantum hardware reliability. The model is presented with detailed formulas and mathematical notations, offering a precise and verifiable framework for analysis.
Quantum information technologies are reshaping communication systems, but reliable integration of classical data, particularly images, into quantum frameworks remains a challenge. Circuit-based quantum image encoding methods are susceptible to noise-induced degradation, which undermines reproducibility and reliability. Here, we investigate the impact of quantum noise channels across the encoding and measurement stages within a hybrid quantum-classical distributed system. We also introduce noise into selected gate operations and measurements to demonstrate its distinct effects on the quantum circuit. To quantify the results, we compute the mean squared error (MSE), the structural similarity index measure (SSIM), and the peak signal-to-noise ratio (PSNR), and benchmark the results across various settings of the noise parameter. Finally, we present experimental results highlighting how image complexity affects encoding robustness using the Linnaeus and MNIST datasets. We anticipate that the presented results will contribute to the development of dependable quantum-classical orchestration strategies for resilient image processing.