Experimentally finding dense subgraphs using a time-bin encoded Gaussian
boson sampling device
- URL: http://arxiv.org/abs/2204.05254v2
- Date: Mon, 20 Jun 2022 21:44:23 GMT
- Title: Experimentally finding dense subgraphs using a time-bin encoded Gaussian
boson sampling device
- Authors: S. Sempere-Llagostera, R. B. Patel, I. A. Walmsley and W. S.
Kolthammer
- Abstract summary: We use a time-bin encoded interferometer to implement GBS experimentally and extract samples to enhance the search for dense subgraphs in a graph.
Our results indicate an improvement over classical methods for subgraphs of sizes three and four in a graph containing ten nodes.
We numerically explore the role of imperfections in the optical circuit and on the performance of the algorithm.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Gaussian Boson Sampling (GBS) is a quantum computing concept based on drawing
samples from a multimode nonclassical Gaussian state using photon-number
resolving detectors. It was initially posed as a near-term approach aiming to
achieve quantum advantage, but several applications have been proposed ever
since, such as the calculation of graph features or molecular vibronic spectra,
among others. For the first time, we use a time-bin encoded interferometer to
implement GBS experimentally and extract samples to enhance the search for
dense subgraphs in a graph. Our results indicate an improvement over classical
methods for subgraphs of sizes three and four in a graph containing ten nodes.
In addition, we numerically explore the role of imperfections in the optical
circuit and on the performance of the algorithm.
Related papers
- Gaussian Boson Sampling to Accelerate NP-Complete Vertex-Minor Graph
Classification [0.9935277311162707]
We propose a hybrid quantum-classical algorithm for the NP-complete problem of determining if two graphs are minor of one another.
We find a graph embedding that allows trading between the one-shot classification accuracy and the amount of input squeezing.
We introduce a new classical algorithm based on graph spectra, which we show outperforms various well-known graph-similarity algorithms.
arXiv Detail & Related papers (2024-02-05T21:24:11Z) - Solving Graph Problems Using Gaussian Boson Sampling [22.516585968074146]
We use a noisy intermediate-scale quantum computer to solve graph problems.
We experimentally observe the presence of GBS enhancement with large photon-click number and an enhancement under certain noise.
Our work is a step toward testing real-world problems using the existing intermediate-scale quantum computers.
arXiv Detail & Related papers (2023-02-02T08:25:47Z) - Gaussian-boson-sampling-enhanced dense subgraph finding shows limited
advantage over efficient classical algorithms [0.0]
We investigate the effects of sources of error including loss and spectral impurity on GBS applied to dense subgraph finding algorithms.
We find that the effectiveness of these algorithms is remarkably robust to errors, to such an extent that there exist efficient classical algorithms that can simulate the underlying GBS.
arXiv Detail & Related papers (2023-01-30T19:00:03Z) - Certification of Gaussian Boson Sampling via graph theory [4.063872661554895]
We exploit a connection between photon counting of a genuine Gaussian Boson Sampling device and the number of perfect matchings in a graph.
Within this framework, two approaches that exploit the distributions of graph feature vectors and graph kernels are presented.
arXiv Detail & Related papers (2022-02-15T20:22:28Z) - 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) - Enhanced detection techniques of Orbital Angular Momentum states in the
classical and quantum regimes [48.7576911714538]
Complex structure inherent in Orbital Angular Momentum (OAM) states makes their detection and classification nontrivial.
Most of the current detection schemes are based on models of the OAM states built upon the use of Laguerre-Gauss modes.
We employ Hypergeometric-Gaussian modes as the basis states of a refined model that can be used -- in certain scenarios -- to better tailor OAM detection techniques.
arXiv Detail & Related papers (2022-01-19T18:46:34Z) - Sigma-Delta and Distributed Noise-Shaping Quantization Methods for
Random Fourier Features [73.25551965751603]
We prove that our quantized RFFs allow a high accuracy approximation of the underlying kernels.
We show that the quantized RFFs can be further compressed, yielding an excellent trade-off between memory use and accuracy.
We empirically show by testing the performance of our methods on several machine learning tasks that our method compares favorably to other state of the art quantization methods in this context.
arXiv Detail & Related papers (2021-06-04T17:24:47Z) - Regularization by Denoising Sub-sampled Newton Method for Spectral CT
Multi-Material Decomposition [78.37855832568569]
We propose to solve a model-based maximum-a-posterior problem to reconstruct multi-materials images with application to spectral CT.
In particular, we propose to solve a regularized optimization problem based on a plug-in image-denoising function.
We show numerical and experimental results for spectral CT materials decomposition.
arXiv Detail & Related papers (2021-03-25T15:20:10Z) - Plug-And-Play Learned Gaussian-mixture Approximate Message Passing [71.74028918819046]
We propose a plug-and-play compressed sensing (CS) recovery algorithm suitable for any i.i.d. source prior.
Our algorithm builds upon Borgerding's learned AMP (LAMP), yet significantly improves it by adopting a universal denoising function within the algorithm.
Numerical evaluation shows that the L-GM-AMP algorithm achieves state-of-the-art performance without any knowledge of the source prior.
arXiv Detail & Related papers (2020-11-18T16:40:45Z) - Nearly Linear Row Sampling Algorithm for Quantile Regression [54.75919082407094]
We give a row sampling algorithm for the quantile loss function with sample complexity nearly linear in the dimensionality of the data.
Based upon our row sampling algorithm, we give the fastest known algorithm for quantile regression and a graph sparsification algorithm for balanced directed graphs.
arXiv Detail & Related papers (2020-06-15T13:40:07Z) - Quantum Sampling Algorithms for Near-Term Devices [0.0]
We introduce a family of quantum algorithms that provide unbiased samples by encoding the entire Gibbs distribution.
We show that this approach leads to a speedup over a classical Markov chain algorithm.
It opens the door to exploring potentially useful sampling algorithms on near-term quantum devices.
arXiv Detail & Related papers (2020-05-28T14:46:20Z)
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.