analiza aiqbench
This paper is a quantum computing algorithms study: it proposes a distributed, low-depth exact version of Grover-style unstructured search, validated in simulation with the MindQuantum framework. The noise-resistance claims rest on simulated noise rather than hardware runs, so they should be read as preliminary. For quantum readers, the useful question is whether the depth reduction holds up on real NISQ devices and under realistic inter-node communication costs.
Low Depth Distributed Quantum Algorithms for Unordered Database Search
arXiv:2604.14081v2 Announce Type: replace Abstract: Grover's algorithm accelerates unstructured database search quadratically compared to classical algorithms. In the NISQ era, distributed quantum computing can decrease circuit depth and reduce noise. In this paper, an algorithm for constructing query operators for subfunctions is proposed. By dividing the target string of the search problem into several substrings and integrating the query operator of each subfunction, a low-depth distributed exact quantum search algorithm is designed. The contributions of this paper are as follows: (1) The proposed distributed algorithm has a lower circuit depth and can mitigate error accumulation compared to distributed quantum search algorithms; (2) The target can be accurately located by the proposed distributed algorithm; (3) Experiments conducted with the quantum software MindQuantum confirm the effectiveness and feasibility of the proposed distributed algorithm. Moreover, the introduction of noise to the circuit during these experiments indicates that the algorithm possesses an inherent capacity for noise resistance.
Abstract:Grover's algorithm accelerates unstructured database search quadratically compared to classical algorithms. In the NISQ era, distributed quantum computing can decrease circuit depth and reduce noise. In this paper, an algorithm for constructing query operators for subfunctions is proposed. By dividing the target string of the search problem into several substrings and integrating the query operator of each subfunction, a low-depth distributed exact quantum search algorithm is designed. The contributions of this paper are as follows: (1) The proposed distributed algorithm has a lower circuit depth and can mitigate error accumulation compared to distributed quantum search algorithms; (2) The target can be accurately located by the proposed distributed algorithm; (3) Experiments conducted with the quantum software MindQuantum confirm the effectiveness and feasibility of the proposed distributed algorithm. Moreover, the introduction of noise to the circuit during these experiments indicates that the algorithm possesses an inherent capacity for noise resistance.
arXiv:2604.14081 [quant-ph]
(or arXiv:2604.14081v2 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2604.14081 Focus to learn more arXiv-issued DOI via DataCite
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Text integral sub licența deschisă a publicației · 237 cuvinte
Subiecte conexe
Quantum Speedups for Testing Similar Means
arXiv · 4 aug. 2026
Using Non-Lipschitz Signum-based Functions for Distributed Optimization and Machine Learning: Trade-off Between Con-vergence Rate and Optimality Gap
arXiv · 4 aug. 2026
Leveraging Discrete Function Decomposability for Scientific Design
arXiv · 9 sept. 2026
Distributed Variational Quantum Linear Solver
arXiv · 5 oct. 2026