Searching and Sorting Algorithms for Quantum Annealing Computers
- URL: http://arxiv.org/abs/2204.13233v1
- Date: Thu, 28 Apr 2022 00:18:57 GMT
- Title: Searching and Sorting Algorithms for Quantum Annealing Computers
- Authors: Robert A. Dunn
- Abstract summary: A sorting algorithm for data sets is provided, with a consideration of sort stability.
Scalability of the algorithms is characterized as a function of problem size.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Algorithms for searching and sorting data sets on quantum annealing systems
are presented. Search algorithms for unordered data sets are developed. A
sorting algorithm for data sets is provided, with a consideration of sort
stability. Scalability of the algorithms, considering both the number of qubits
required and the qubit connectivity, is characterized as a function of problem
size.
Related papers
- Structured search algorithm: A quantum leap [0.0]
Grover's quantum search algorithm showcases the prowess of quantum algorithms.
This letter advances Grover's algorithm using a structured search method to attain an unbounded search speed.
arXiv Detail & Related papers (2025-04-04T13:16:53Z) - Comparing Algorithms for Loading Classical Datasets into Quantum Memory [0.0]
We compare various algorithms for loading classical datasets into quantum memory.
We evaluate state preparation algorithms based on five key attributes.
We also visually compare three metrics (namely, circuit depth, qubit count, and classical runtime)
arXiv Detail & Related papers (2024-07-22T15:43:18Z) - Generalized quantum Arimoto-Blahut algorithm and its application to
quantum information bottleneck [55.22418739014892]
We generalize the quantum Arimoto-Blahut algorithm by Ramakrishnan et al.
We apply our algorithm to the quantum information bottleneck with three quantum systems.
Our numerical analysis shows that our algorithm is better than their algorithm.
arXiv Detail & Related papers (2023-11-19T00:06:11Z) - Encoding of data sets and algorithms [0.0]
In many high-impact applications, it is important to ensure the quality of output of a machine learning algorithm.
We have initiated a mathematically rigorous theory to decide which models are close to each other in terms of certain metrics.
A given threshold metric acting on this grid will express the nearness (or statistical distance) from each algorithm and data set of interest to any given application.
arXiv Detail & Related papers (2023-03-02T05:29:27Z) - Algorithm Selection on a Meta Level [58.720142291102135]
We introduce the problem of meta algorithm selection, which essentially asks for the best way to combine a given set of algorithm selectors.
We present a general methodological framework for meta algorithm selection as well as several concrete learning methods as instantiations of this framework.
arXiv Detail & Related papers (2021-07-20T11:23:21Z) - A Note on Enumeration by Fair Sampling [0.0]
This note describes an algorithm for enumerating all the elements in a finite set based on uniformly random sampling from the set.
Our algorithm is based on a lemma of the coupon collector's problem and is an improved version of the algorithm described in arXiv:2007.08487 ( 2020)
arXiv Detail & Related papers (2021-04-05T14:56:58Z) - Quantum Algorithms for String Processing [58.720142291102135]
We provide a quantum algorithm for the String matching problem that uses exponentially less quantum memory than existing ones.
Using the same ideas, we provide two algorithms for the String comparing problem.
The second algorithm works exponentially faster than the existing one.
arXiv Detail & Related papers (2020-12-01T09:59:06Z) - Towards Meta-Algorithm Selection [78.13985819417974]
Instance-specific algorithm selection (AS) deals with the automatic selection of an algorithm from a fixed set of candidates.
We show that meta-algorithm-selection can indeed prove beneficial in some cases.
arXiv Detail & Related papers (2020-11-17T17:27:33Z) - 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) - Fast Search on Binary Codes by Weighted Hamming Distance [38.50174794945964]
A fast search algorithm is proposed to perform the non-exhaustive search for $K$ nearest binary codes by weighted Hamming distance.
A fast search framework based on the proposed search algorithm is designed to solve the problem of long binary codes.
arXiv Detail & Related papers (2020-09-18T02:24:44Z) - A Systematic Characterization of Sampling Algorithms for Open-ended
Language Generation [71.31905141672529]
We study the widely adopted ancestral sampling algorithms for auto-regressive language models.
We identify three key properties that are shared among them: entropy reduction, order preservation, and slope preservation.
We find that the set of sampling algorithms that satisfies these properties performs on par with the existing sampling algorithms.
arXiv Detail & Related papers (2020-09-15T17:28:42Z) - Extreme Algorithm Selection With Dyadic Feature Representation [78.13985819417974]
We propose the setting of extreme algorithm selection (XAS) where we consider fixed sets of thousands of candidate algorithms.
We assess the applicability of state-of-the-art AS techniques to the XAS setting and propose approaches leveraging a dyadic feature representation.
arXiv Detail & Related papers (2020-01-29T09:40:58Z)
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.