Promised and Distributed Quantum Search

Harry Buhrman, Hein Röhrig

Rodolphe Griset, Ioannis Lavdas, Jiří Guth Jarkovský

Quantum search has emerged as one of the most promising fields in quantum computing. State-of-the-art quantum search algorithms enable the search for specific elements in a distribution by monotonically increasing the density... | Find, read and cite all the research you need on Tech Science Press

Hao Li, Daowen Qiu

C. M. A. U. Kaushalya, Mohamed Sabri, Anuradha Mahasinghe +3 more

Quantum walks offer promising advantages for search algorithms over graphs. Among these, Grover’s quantum search provides a quadratic speedup with a time complexity of for unstructured search problems. Nevertheless, Grover search performs poorly on cyclic graphs due to the dynamics of the Grover walk. This study explores the behavior of Grover’s quantum walk on cyclic graphs , analyzing the probability distribution of finding the marked vertex. To analyze this behavior, we extend each vertex of the cycle by attaching semi-infinite-length paths (tails). We develop a direct analytical approach to obtain the transition matrix , whose elements are independent of . For small cycles , we observe success probabilities exceeding 0.5. In contrast, with large cycles, the effectiveness drops rapidly. We observe that the success probability approaches zero as the size of the graph increases , indicating the limitations of the quantum search in cyclic structures.

Sanjar S. Kenjaev, Tojiyev Ma'ruf Ruzikulovich, Rashid Nasimov +2 more

Optimal query distribution in information systems plays a crucial role in minimizing delays, balancing workload, and ensuring system stability. Considering multi-factor parameters such as server capacity, network latency, and service rate, the fuzzy logic approach proves effective in handling uncertainty during decision-making. However, an increase in the number of linguistic variables leads to exponential growth of the fuzzy rule base, thereby increasing computational complexity and limiting real-time performance. Traditional optimization methods, including genetic algorithms, are constrained by slow convergence and a tendency to fall into local extrema in large search spaces. This paper proposes a multi-objective quantum optimization approach aimed at reducing the number of fuzzy rules in query distribution processes. By applying Grover's quantum algorithm, the search for optimal rules is significantly accelerated, and the search space is significantly reduced. Experimental results demonstrate reduced response time and improved load balancing. The proposed approach is evaluated as a promising quantum-intelligent mechanism for enhancing load management efficiency in cloud computing environments.

Tathagat Tulsi, Lov K. Grover, Apoorva D. Patel