Quantum Optical Approach to the $K$ Nearest Neighbour Algorithm
- URL: http://arxiv.org/abs/2404.12033v1
- Date: Thu, 18 Apr 2024 09:33:31 GMT
- Title: Quantum Optical Approach to the $K$ Nearest Neighbour Algorithm
- Authors: Vivek Mehta, Francesco Petruccione, Utpal Roy,
- Abstract summary: We construct a hybrid quantum-classical approach for the $K$-Nearest Neighbour algorithm.
The information is embedded in a phase-distributed multimode coherent state with the assistance of a single photon.
We provide the quantum optical architecture corresponding to our algorithm.
- Score: 1.904851064759821
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: We construct a hybrid quantum-classical approach for the $K$-Nearest Neighbour algorithm, where the information is embedded in a phase-distributed multimode coherent state with the assistance of a single photon. The task of finding the closeness between the data points is delivered by the quantum optical computer, while the sorting and class assignment are performed by a classical computer. We provide the quantum optical architecture corresponding to our algorithm. The subordinate optical network is validated by numerical simulation. We also optimize the computational resources of the algorithm in the context of space, energy requirements and gate complexity. Applications are presented for diverse and well-known public benchmarks and synthesized data sets.
Related papers
- Performance Benchmarking of Quantum Algorithms for Hard Combinatorial Optimization Problems: A Comparative Study of non-FTQC Approaches [0.0]
This study systematically benchmarks several non-fault-tolerant quantum computing algorithms across four distinct optimization problems.
Our benchmark includes noisy intermediate-scale quantum (NISQ) algorithms, such as the variational quantum eigensolver.
Our findings reveal that no single non-FTQC algorithm performs optimally across all problem types, underscoring the need for tailored algorithmic strategies.
arXiv Detail & Related papers (2024-10-30T08:41:29Z) - Solving Combinatorial Optimization Problems on a Photonic Quantum Computer [0.0]
Combinatorial optimization problems pose significant computational challenges across various fields, from logistics to cryptography.
Traditional computational methods often struggle with their exponential complexity, motivating exploration into alternative paradigms such as quantum computing.
We demonstrate how photonic quantum computers can efficiently explore solution spaces and identify optimal solutions for a range of problems.
arXiv Detail & Related papers (2024-09-19T20:57:24Z) - Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties?
We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.
We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
arXiv Detail & Related papers (2024-08-22T08:21:28Z) - Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
Quantum computing sets the foundation for new ways of designing algorithms.
New challenges arise concerning which field quantum speedup can be achieved.
Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
arXiv Detail & Related papers (2024-02-26T09:32:07Z) - QArchSearch: A Scalable Quantum Architecture Search Package [1.725192300740999]
We present textttQArchSearch, an AI based quantum architecture search package with the textttQTensor library as a backend.
We show that the search package is able to efficiently scale the search to large quantum circuits and enables the exploration of more complex models for different quantum applications.
arXiv Detail & Related papers (2023-10-11T20:00:33Z) - Quantum algorithms: A survey of applications and end-to-end complexities [90.05272647148196]
The anticipated applications of quantum computers span across science and industry.
We present a survey of several potential application areas of quantum algorithms.
We outline the challenges and opportunities in each area in an "end-to-end" fashion.
arXiv Detail & Related papers (2023-10-04T17:53:55Z) - A Universal Quantum Algorithm for Weighted Maximum Cut and Ising
Problems [0.0]
We propose a hybrid quantum-classical algorithm to compute approximate solutions of binary problems.
We employ a shallow-depth quantum circuit to implement a unitary and Hermitian operator that block-encodes the weighted maximum cut or the Ising Hamiltonian.
Measuring the expectation of this operator on a variational quantum state yields the variational energy of the quantum system.
arXiv Detail & Related papers (2023-06-10T23:28:13Z) - Quantum Annealing for Single Image Super-Resolution [86.69338893753886]
We propose a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem.
The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
arXiv Detail & Related papers (2023-04-18T11:57:15Z) - Quantum Clustering with k-Means: a Hybrid Approach [117.4705494502186]
We design, implement, and evaluate three hybrid quantum k-Means algorithms.
We exploit quantum phenomena to speed up the computation of distances.
We show that our hybrid quantum k-Means algorithms can be more efficient than the classical version.
arXiv Detail & Related papers (2022-12-13T16:04:16Z) - Benchmarking Small-Scale Quantum Devices on Computing Graph Edit
Distance [52.77024349608834]
Graph Edit Distance (GED) measures the degree of (dis)similarity between two graphs in terms of the operations needed to make them identical.
In this paper we present a comparative study of two quantum approaches to computing GED.
arXiv Detail & Related papers (2021-11-19T12:35:26Z) - Resource-efficient encoding algorithm for variational bosonic quantum
simulations [0.0]
In the Noisy Intermediate Scale Quantum (NISQ) era of quantum computing, quantum resources are limited.
We present a resource-efficient quantum algorithm for bosonic ground and excited state computations.
arXiv Detail & Related papers (2021-02-23T19:00:05Z)
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.