A Deployable Quantum Access Points Selection Algorithm for Large-Scale Localization
- URL: http://arxiv.org/abs/2407.08943v1
- Date: Fri, 12 Jul 2024 02:59:37 GMT
- Title: A Deployable Quantum Access Points Selection Algorithm for Large-Scale Localization
- Authors: Ahmed Shokry, Moustafa Youssef,
- Abstract summary: We introduce a quantum APs selection algorithm for large-scale localization systems.
By selecting fewer than 14% of the available APs in the environment, our quantum algorithm achieves the same floor localization accuracy as utilizing the entire set of APs.
- Score: 4.962238993531738
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Effective access points (APs) selection is a crucial step in localization systems. It directly affects both localization accuracy and computational efficiency. Classical APs selection algorithms are usually computationally expensive, hindering the deployment of localization systems in a large worldwide scale. In this paper, we introduce a quantum APs selection algorithm for large-scale localization systems. The proposed algorithm leverages quantum annealing to eliminate redundant and noisy APs. We explain how to formulate the APs selection problem as a quadratic unconstrained binary optimization (QUBO) problem, suitable for quantum annealing, and how to select the minimum number of APs that maintain the same overall localization system accuracy as the complete APs set. Based on this, we further propose a logarithmic-complexity algorithm to select the optimal number of APs. We implement our quantum algorithm on a real D-Wave Systems quantum machine and assess its performance in a real test environment for a floor localization problem. Our findings reveal that by selecting fewer than 14% of the available APs in the environment, our quantum algorithm achieves the same floor localization accuracy as utilizing the entire set of APs and a superior accuracy over utilizing the reduced dataset by classical APs selection counterparts. Moreover, the proposed quantum algorithm achieves more than an order of magnitude speedup over the corresponding classical APs selection algorithms, emphasizing the efficiency of the proposed quantum algorithm for large-scale localization systems.
Related papers
- Multiscale Quantum Approximate Optimization Algorithm [0.0]
The quantum approximate optimization algorithm (QAOA) is one of the canonical algorithms designed to find approximate solutions to optimization problems.
We propose a new version of QAOA that incorporates the capabilities of QAOA and the real-space renormalization group transformation.
arXiv Detail & Related papers (2023-12-11T07:47:16Z) - Local to Global: A Distributed Quantum Approximate Optimization
Algorithm for Pseudo-Boolean Optimization Problems [7.723735038335632]
Quantum Approximate Optimization Algorithm (QAOA) is considered as a promising candidate to demonstrate quantum supremacy.
limited qubit availability and restricted coherence time challenge QAOA to solve large-scale pseudo-Boolean problems.
We propose a distributed QAOA which can solve a general pseudo-Boolean problem by converting it to a simplified Ising model.
arXiv Detail & Related papers (2023-10-08T08:07:11Z) - Learning Regions of Interest for Bayesian Optimization with Adaptive
Level-Set Estimation [84.0621253654014]
We propose a framework, called BALLET, which adaptively filters for a high-confidence region of interest.
We show theoretically that BALLET can efficiently shrink the search space, and can exhibit a tighter regret bound than standard BO.
arXiv Detail & Related papers (2023-07-25T09:45:47Z) - Quantum Search Approaches to Sampling-Based Motion Planning [0.0]
We present a novel formulation of traditional sampling-based motion planners as database-oracle structures that can be solved via quantum search algorithms.
We consider two complementary scenarios: for simpler sparse environments, we formulate the Quantum Full Path Search Algorithm (q-FPS), which creates a superposition of full random path solutions.
For dense unstructured environments, we formulate the Quantum Rapidly Exploring Random Tree algorithm, q-RRT, that creates quantum superpositions of possible parent-child connections.
arXiv Detail & Related papers (2023-04-10T19:12:25Z) - Universal quantum state preparation via revised greedy algorithm [2.718317980347176]
We propose a revised version to design dynamic pulses to realize universal quantum state preparation.
We implement this scheme to the universal preparation of single- and two-qubit state in the context of semiconductor quantum dots and superconducting circuits.
arXiv Detail & Related papers (2021-08-07T02:44:15Z) - Quantum Approximate Optimization Algorithm Based Maximum Likelihood
Detection [80.28858481461418]
Recent advances in quantum technologies pave the way for noisy intermediate-scale quantum (NISQ) devices.
Recent advances in quantum technologies pave the way for noisy intermediate-scale quantum (NISQ) devices.
arXiv Detail & Related papers (2021-07-11T10:56:24Z) - Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex
Decentralized Optimization Over Time-Varying Networks [79.16773494166644]
We consider the task of minimizing the sum of smooth and strongly convex functions stored in a decentralized manner across the nodes of a communication network.
We design two optimal algorithms that attain these lower bounds.
We corroborate the theoretical efficiency of these algorithms by performing an experimental comparison with existing state-of-the-art methods.
arXiv Detail & Related papers (2021-06-08T15:54:44Z) - Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices.
We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT)
Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
arXiv Detail & Related papers (2020-10-01T18:14:11Z) - Adaptive Sampling for Best Policy Identification in Markov Decision
Processes [79.4957965474334]
We investigate the problem of best-policy identification in discounted Markov Decision (MDPs) when the learner has access to a generative model.
The advantages of state-of-the-art algorithms are discussed and illustrated.
arXiv Detail & Related papers (2020-09-28T15:22:24Z) - AP-Loss for Accurate One-Stage Object Detection [49.13608882885456]
One-stage object detectors are trained by optimizing classification-loss and localization-loss simultaneously.
The former suffers much from extreme foreground-background imbalance due to the large number of anchors.
This paper proposes a novel framework to replace the classification task in one-stage detectors with a ranking task.
arXiv Detail & Related papers (2020-08-17T13:22:01Z)
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.