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International Conference on Quantum Communications, Networking, and Computing (QCNC)

Quantum Mean Filter: Features and Issues

Freddy Alejandro Chaurra-Gutierrez, Claudia Feregrino-Uribe, Julio César Pérez-Sansalvador, Gustavo Rodríguez‐Gómez

Published Mar 31, 2025• 0 citations
Publisher

Abstract

Quantum Mean Filtering (QMF) leverages quantum properties for efficient filtering but faces challenges such as storage overhead, garbage information, boundary conditions, and iterative filtering limitations. Therefore, this paper proposes an improved QMF using Quantum Block Representation by Basis States (QBRBS) and an entire shift operation. The proposed method decreases storage requirements, eliminates garbage information, handles boundary conditions, enables iterative filtering, and reduces quantum complexity. Comparative analysis shows that QBRBS-based QMF outperforms traditional QMF in terms of storage efficiency, measurement complexity, iterative capabilities, and quantum circuit complexity, significantly enhancing the practicality of quantum filtering.

DOI
10.1109/qcnc64685.2025.00101

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Quantum information processing can overcome the limitations of classical computation.Consequently, image filtering using quantum computation has become a research hotspot.Here, a quantum algorithm is presented on the basis of the classical image filtering principle to detect and cancel the noise of an image.To this end, a quantum algorithm that completes the image filtering task is proposed and implemented.The novel enhanced quantum representation of digital images is introduced.Then, four basic modules, namely, position-shifting, parallel-CNOT, parallel-swap, and compare the max, are demonstrated.Two composite modules that can be utilised to realise the reversible logic circuit of the proposed quantum algorithm are designed on the basis of these basic modules.Simulation-based experimental results show the feasibility and the capabilities of the proposed quantum image filtering scheme.In addition, our proposal has outperformed its classical counterpart and other existing quantum image filtering schemes supported by detailed theoretical analysis of the computational complexity.Thus, it can potentially be used for highly efficient image filtering in a quantum computer age.
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We present the first quantum version of the one-dimensional integer wavelet S-Transform (QIST). This version is based on a new Quantum Block Representation by Basis States (QBRBS) and the design of a quantum rounding operator. The QBRBS format facilitates storing and manipulating pairs of signal elements, decreasing the use of additional qubits and gates. Also, the quantum rounding operator exploits the features of the S-Transform to avoid nonlinearities. Complete quantum circuits for addition, subtraction, and halving operations were developed. The complexity analysis showed a polynomial quantum complexity, O(n), and a time complexity of O(1). The experiments performed on MATLAB and Qiskit verify the theoretical results coinciding with the results of the classical version. Finally, the new quantum transform’s feasibility and accuracy enable new quantum developments in information hiding, compression, and IoT applications.
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Private Information Retrieval (PIR) enables users to retrieve records from a database without revealing their query targets to the server. While lattice-based PIR schemes have gained attention due to their quantum-resistance properties, existing approaches face significant challenges in terms of computational overhead and noise management. Current state-of-the-art solutions like SealPIR, OnionPIR, and SPIRAL suffer from three main limitations: (1) complex ciphertext query expansion requiring iterative homomorphic operations,(2) substantial computational costs due to polynomial operations in ring-LWEbased schemes, and (3) inefficient conversion between heterogeneous ciphertexts that increases both communication overhead and noise growth. In this paper, we propose a novel coefficient extraction-based PIR scheme that addresses these challenges through two key innovations: A direct coefficient extraction technique that eliminates iterative homomorphic operations; a unified algebraic structure across RLWE, LWE, and LWEGSW schemes for seamless ciphertext conversion. Compared to existing approaches, our scheme achieves: significant reduction in computational overhead through optimized homomorphic operations compared to baseline approaches; substantially decreased communication costs via efficient ciphertext structure design- Better controlled noise growth allowing smaller parameter settings; maintained security based on standard RLWE/LWE hardness assumptions. These improvements make our scheme particularly suitable for practical PIR applications requiring both efficiency and strong security guarantees.