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National Radio Science Conference

Enhanced Quantum Search via Partial Diffusion and Entanglement with Linear Optics

Ahmed S. El Fiky, Ahmed Younes, A. R. Abdulghany, Salah S. A. Obayya, Salem F. Hegazy

Published May 6, 2025• 0 citations
Publisher

Abstract

Searching for specific items in an unstructured database presents a vital computational problem. While the best-known classical search algorithm in a database of size$N$has a complexity$\mathrm{O}(N)$, Grover's quantum search algorithm exhibits a complexity$\mathrm{O}(\sqrt{N})$, featuring a quadratic speed-up. In this paper, we present an optical system that implements an enhanced version of the search algorithm proposed by Younis et al. The system uses linear optical components such as beam splitters, phase shifters, and wave plates, along with a single-photon source and photodetectors.

DOI
10.1109/nrsc65659.2025.11018556

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Searching for specific items in an unstructured database presents a vital computational problem. While the best-known classical search algorithm in a database of sizeNNNhas a complexityO(N)\mathrm{O}(N)O(N), Grover's quantum search algorithm exhibits a complexityO(N)\mathrm{O}(\sqrt{N})O(N​), featuring a quadratic speed-up. In this paper, we present an optical system that implements an enhanced version of the search algorithm proposed by Younis et al. The system uses linear optical components such as beam splitters, phase shifters, and wave plates, along with a single-photon source and photodetectors.
Quantum search is one of the most important algorithmic developments in quantum computing because it demonstrates how quantum principles can improve the efficiency of solving unstructured search problems. Grover’s algorithm provides a quadratic speed-up over classical linear search by reducing query complexity from O(N) to O(√N), where N represents the size of the search space. This review examines the principles, performance, and applications of Grover’s algorithm, with emphasis on its theoretical foundation and practical relevance. The discussion covers the roles of superposition, quantum interference, oracle operations, diffusion operators, and amplitude amplification in increasing the probability of measuring a desired solution. The manuscript also reviews the computational performance of quantum search in comparison with classical search methods and considers its relevance to database searching, combinatorial optimisation, cryptography, machine learning, bioinformatics, quantum chemistry, and materials science. The review highlights that Grover’s algorithm is not limited to database retrieval, as many computationally intensive problems can be reformulated as search or optimisation tasks. However, practical implementation remains constrained by current quantum hardware limitations, including decoherence, quantum noise, limited qubit availability, circuit depth, and the difficulty of designing efficient oracles. Despite these challenges, continuing progress in quantum hardware, error correction, cloud-based quantum platforms, and hybrid quantum-classical architectures suggests that quantum search will remain an important area of research. Overall, Grover’s algorithm represents a foundational quantum algorithm with significant theoretical value and potential long-term application in computational domains involving large and unstructured search spaces.
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We report on an experiment on Grover's quantum search algorithm showing that classical waves can search a N-item database as efficiently as quantum mechanics can. The transverse beam profile of a short laser pulse is processed iteratively as the pulse bounces back and forth between two mirrors. We directly observe the sought item being found in approximately square root[N] iterations, in the form of a growing intensity peak on this profile. Although the lack of quantum entanglement limits the size of our database, our results show that entanglement is neither necessary for the algorithm itself, nor for its efficiency.
The present study aims to examine the real-world implications of Grover's algorithm, specifically focusing on its quadratic speedup in contrast to classical linear search techniques employed for unsorted databases. The objective is to bridge the gap between theoretical potential and practical application in the field of quantum computing. This study conducts a comprehensive analysis of Grover's Algorithm and traditional linear search across databases of varying sizes, uniquely contributing by empirically validating Grover's Algorithm's superior performance and aligning it with theoretical expectations. Significantly, the study expands its analysis to encompass larger databases, further affirming the algorithm's utility in handling growing database sizes. This study significantly contributes to the progress of quantum computing through the practical implementation of theoretical principles as well as enhancing the scientific community's understanding of quantum search algorithms and their relative efficacy.
Grover's algorithm is one of the pioneering demonstrations of the advantages of quantum computing over its classical counterpart, providing - at most - a quadratic speed-up over the classical solution for unstructured database search. The original formulation of Grover's algorithm is non-deterministic, finding the answer with a probability that varies with the size of the search space and the number of marked elements. A recent reformulation introduced a deterministic form of Grover's algorithm that - in principle - finds the answer with certainty. Here we realise the deterministic Grover's algorithm on a programmable photonic integrated circuit, finding that it not only outperforms the original Grover's algorithm as predicted, but is also markedly more robust against technological imperfections. We explore databases of 4 to 10 elements, with every choice of a single marked element, achieving an average success probability of 99.77±0.05%99.77 \pm 0.05\%99.77±0.05%.
This article reviews recent progress in quantum database search algorithms. The subject is presented in a self-contained and pedagogical way. The problem of searching a large database (a Hilbert space) for a target item is performed by the famous Grover algorithm which locates the target item with high probability and a quadratic speed-up compared with the corresponding classical algorithm. If the database is partitioned into blocks and one is searching for the block containing the target item instead of the target item itself, then the problem is referred to as partial search. Partial search trades accuracy for speed and the most efficient version is the Grover–Radhakrishnan–Korepin (GRK) algorithm. The target block can be further partitioned into sub-blocks so that GRK's can be performed in a sequence called a hierarchy. We study the Grover search and GRK partial search in detail and prove that a GRK hierarchy is less efficient than a direct GRK partial search. Both the Grover search and the GRK partial search can be generalized to the case with several target items (or target blocks for a GRK). The GRK partial search algorithm can also be represented in terms of group theory.