Quantum search algorithm on weighted databases

• URL: http://arxiv.org/abs/2312.01590v2
• Date: Wed, 27 Mar 2024 11:28:12 GMT
• Title: Quantum search algorithm on weighted databases
• Authors: Yifan Sun, Lian-Ao Wu,
• Abstract summary: Grover algorithm is a crucial solution for addressing unstructured search problems. This research extensively investigates Grover's search methodology within non-uniformly distributed databases. It is observed that the search process facilitated by this evolution does not consistently result in a speed-up.
• Score: 5.229564709919574
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Grover algorithm is a crucial solution for addressing unstructured search problems and has emerged as an essential quantum subroutine in various complex algorithms. This research extensively investigates Grover's search methodology within non-uniformly distributed databases, a scenario frequently encountered in practical applications. Our analysis reveals that the behavior of the Grover evolution differs significantly when applied to non-uniform databases compared to uniform or 'unstructured databases'. It is observed that the search process facilitated by this evolution does not consistently result in a speed-up, and we have identified specific criteria for such situations. Furthermore, we have extended this investigation to databases characterized by coherent states, confirming the speed-up achieved through Grover evolution via rigorous numerical verification. In conclusion, our study provides an enhancement to the original Grover algorithm, offering insights to optimize implementation strategies and broaden its range of applications.

Related papers

• A Learning Search Algorithm for the Restricted Longest Common Subsequence Problem [40.64116457007417]
  The Restricted Longest Common Subsequence (RLCS) problem has significant applications in bioinformatics. This paper introduces two novel approaches designed to enhance the search process by steering it towards promising regions. An important contribution of this paper is found in the generation of real-world instances where scientific abstracts serve as input strings.
  arXiv  Detail & Related papers  (2024-10-15T20:02:15Z)
• Biased Random-Key Genetic Algorithms: A Review [2.4578723416255754]
  The review encompasses over 150 papers with a wide range of applications. Scheduling is by far the most prevalent application area in this review, followed by network design and location problems. The most frequent hybridization method employed is local search, and new features aim to increase population diversity.
  arXiv  Detail & Related papers  (2023-12-01T22:32:58Z)
• Rethinking Complex Queries on Knowledge Graphs with Neural Link Predictors [58.340159346749964]
  We propose a new neural-symbolic method to support end-to-end learning using complex queries with provable reasoning capability. We develop a new dataset containing ten new types of queries with features that have never been considered. Our method outperforms previous methods significantly in the new dataset and also surpasses previous methods in the existing dataset at the same time.
  arXiv  Detail & Related papers  (2023-04-14T11:35:35Z)
• Research Trends and Applications of Data Augmentation Algorithms [77.34726150561087]
  We identify the main areas of application of data augmentation algorithms, the types of algorithms used, significant research trends, their progression over time and research gaps in data augmentation literature. We expect readers to understand the potential of data augmentation, as well as identify future research directions and open questions within data augmentation research.
  arXiv  Detail & Related papers  (2022-07-18T11:38:32Z)
• Frequent Itemset-driven Search for Finding Minimum Node Separators in Complex Networks [61.2383572324176]
  We propose a frequent itemset-driven search approach, which integrates the concept of frequent itemset mining in data mining into the well-known memetic search framework. It iteratively employs the frequent itemset recombination operator to generate promising offspring solution based on itemsets that frequently occur in high-quality solutions. In particular, it discovers 29 new upper bounds and matches 18 previous best-known bounds.
  arXiv  Detail & Related papers  (2022-01-18T11:16:40Z)
• Multidimensional Assignment Problem for multipartite entity resolution [69.48568967931608]
  Multipartite entity resolution aims at integrating records from multiple datasets into one entity. We apply two procedures, a Greedy algorithm and a large scale neighborhood search, to solve the assignment problem. We find evidence that design-based multi-start can be more efficient as the size of databases grow large.
  arXiv  Detail & Related papers  (2021-12-06T20:34:55Z)
• Exploring Complicated Search Spaces with Interleaving-Free Sampling [127.07551427957362]
  In this paper, we build the search algorithm upon a complicated search space with long-distance connections. We present a simple yet effective algorithm named textbfIF-NAS, where we perform a periodic sampling strategy to construct different sub-networks. In the proposed search space, IF-NAS outperform both random sampling and previous weight-sharing search algorithms by a significant margin.
  arXiv  Detail & Related papers  (2021-12-05T06:42:48Z)
• Grover search revisited; application to image pattern matching [0.8367938108534343]
  We propose a quantum algorithm that approximately executes the entire Grover database search or pattern matching algorithm. The key idea is to use the recently proposed approximate amplitude encoding method on a shallow quantum circuit.
  arXiv  Detail & Related papers  (2021-08-24T17:30:41Z)
• IGO-QNN: Quantum Neural Network Architecture for Inductive Grover Oracularization [0.0]
  We propose a novel paradigm of integration of Grover's algorithm in a machine learning framework: the inductive Grover oracular quantum neural network (IGO-QNN) The model defines a variational quantum circuit with hidden layers of parameterized quantum neurons densely connected via entangle synapses to encode a dynamic Grover's search oracle.
  arXiv  Detail & Related papers  (2021-05-25T01:52:44Z)
• Selection-Expansion: A Unifying Framework for Motion-Planning and Diversity Search Algorithms [69.87173070473717]
  We investigate the properties of two diversity search algorithms, the Novelty Search and the Goal Exploration Process algorithms. The relation to MP algorithms reveals that the smoothness, or lack of smoothness of the mapping between the policy parameter space and the outcome space plays a key role in the search efficiency.
  arXiv  Detail & Related papers  (2021-04-10T13:52:27Z)
• Quantum Search with Prior Knowledge [15.384459603233978]
  We propose a new generalization of Grover's search algorithm which performs better than the standard Grover algorithm in average under this setting. We prove that our new algorithm achieves the optimal expected success probability of finding the solution if the number of queries is fixed.
  arXiv  Detail & Related papers  (2020-09-18T09:50:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.