Related papers: Robustness of Quantum Random Walk Search with multi-phase matching

Robustness of Quantum Random Walk Search with multi-phase matching

URL: http://arxiv.org/abs/2404.05084v1
Date: Sun, 7 Apr 2024 21:52:35 GMT
Title: Robustness of Quantum Random Walk Search with multi-phase matching
Authors: Hristo Tonchev, Petar Danev,
Abstract summary: We show that usage of a particular sequence of phases can make the algorithm more robust even if there is no preserved connection between the phases in the traversing coin.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: In our previous works, we have studied quantum random walk search algorithm on hypercube, with traversing coin constructed by using generalized Householder reflection and a phase multiplier. When the same phases are used each iteration, the algorithm is robust (stable against errors in the phases) if a certain connection between the phases in the traversing coin is preserved, otherwise small errors lead to poor algorithm performance. Here we investigate how the robustness changes if different phases are used, depending on the current iteration number. We numerically study six different examples with different phase sequences. We show that usage of a particular sequence of phases can make the algorithm more robust even if there is no preserved connection between the phases in the traversing coin.

Related papers

Robustness of different modifications of Grovers algorithm based on generalized Householder reflections with different phases [0.0]
We study five Grovers algorithm modifications, where each iteration is constructed by two generalized Householder reflections. By using semi-empirical methods, we investigate various characteristics of the dependence between the probability to find solution and the phase errors.
arXiv Detail & Related papers (2024-01-07T23:04:39Z)
Compatible Transformer for Irregularly Sampled Multivariate Time Series [75.79309862085303]
We propose a transformer-based encoder to achieve comprehensive temporal-interaction feature learning for each individual sample. We conduct extensive experiments on 3 real-world datasets and validate that the proposed CoFormer significantly and consistently outperforms existing methods.
arXiv Detail & Related papers (2023-10-17T06:29:09Z)
Robustness of Quantum Random Walk Search Algorithm in Hypercube when only first or both first and second neighbors are measured [0.0]
We study the robustness of two modifications of quantum random walk search algorithm on hypercube. The most important one, in view of our study of the robustness, is an increase in the stability of the algorithm, especially for large coin dimensions.
arXiv Detail & Related papers (2023-05-24T11:55:52Z)
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)
Reducing number of gates in quantum random walk search algorithm via modification of coin operators [0.0]
This paper examines a way to simplify the circuit of quantum random walk search algorithm. It is shown explicitly how to construct such walk coin in order to obtain more robust quantum algorithm.
arXiv Detail & Related papers (2022-04-27T11:41:08Z)
Dual-Frequency Quantum Phase Estimation Mitigates the Spectral Leakage of Quantum Algorithms [76.15799379604898]
Quantum phase estimation suffers from spectral leakage when the reciprocal of the record length is not an integer multiple of the unknown phase. We propose a dual-frequency estimator, which approaches the Cramer-Rao bound, when multiple samples are available.
arXiv Detail & Related papers (2022-01-23T17:20:34Z)
Optimizing the walk coin in the quantum random walk search algorithm through machine learning [0.0]
This paper examines the stability of the quantum random walk search algorithm, when the walk coin is constructed by generalized Householder reflection and additional phase shift. The optimization of the algorithm is done by numerical methods - Monte Carlo, neural networks, and supervised machine learning.
arXiv Detail & Related papers (2021-05-17T17:13:49Z)
Contrastive learning of strong-mixing continuous-time stochastic processes [53.82893653745542]
Contrastive learning is a family of self-supervised methods where a model is trained to solve a classification task constructed from unlabeled data. We show that a properly constructed contrastive learning task can be used to estimate the transition kernel for small-to-mid-range intervals in the diffusion case.
arXiv Detail & Related papers (2021-03-03T23:06:47Z)
Computational Phase Transitions: Benchmarking Ising Machines and Quantum Optimisers [0.0]
Hardest instances appear to be well-concentrated in a narrow region, with a control parameter allowing uniform random distributions. It has been established that one could observe a computational phase transition in a distribution produced from coherent Ising machine(s)
arXiv Detail & Related papers (2020-09-11T18:00:00Z)
Single-Timescale Stochastic Nonconvex-Concave Optimization for Smooth Nonlinear TD Learning [145.54544979467872]
We propose two single-timescale single-loop algorithms that require only one data point each step. Our results are expressed in a form of simultaneous primal and dual side convergence.
arXiv Detail & Related papers (2020-08-23T20:36:49Z)
Efficient classical simulation of random shallow 2D quantum circuits [104.50546079040298]
Random quantum circuits are commonly viewed as hard to simulate classically. We show that approximate simulation of typical instances is almost as hard as exact simulation. We also conjecture that sufficiently shallow random circuits are efficiently simulable more generally.
arXiv Detail & Related papers (2019-12-31T19:00:00Z)

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