2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)最新文献_第8页

Multiangle QAOA Does Not Always Need All Its Angles 多角度QAOA并不总是需要所有的角度

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-09-23 DOI: 10.1109/SEC54971.2022.00062

Kaiyan Shi, Rebekah Herrman, Ruslan Shaydulin, Shouvanik Chakrabarti, Marco Pistoia, Jeffrey Larson

{"title":"Multiangle QAOA Does Not Always Need All Its Angles","authors":"Kaiyan Shi, Rebekah Herrman, Ruslan Shaydulin, Shouvanik Chakrabarti, Marco Pistoia, Jeffrey Larson","doi":"10.1109/SEC54971.2022.00062","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00062","url":null,"abstract":"Introducing additional tunable parameters to quantum circuits is a powerful way of improving per-formance without increasing hardware requirements. A recently introduced multiangle extension of the quantum approximate optimization algorithm (ma-QAOA) signifi-cantly improves the solution quality compared with QAOA by allowing the parameters for each term in the Hamilto-nian to vary independently. Prior results suggest, however, considerable redundancy in parameters, the removal of which would reduce the cost of parameter optimization. In this work we show numerically the connection between the problem symmetries and the parameter redundancy by demonstrating that symmetries can be used to reduce the number of parameters used by ma-QAOA without decreasing the solution quality. We study Max-Cut on all 7,565 connected, non-isomorphic 8-node graphs with a nontrivial symmetry group and show numerically that in 67.4% of these graphs, symmetry can be used to reduce the number of parameters with no decrease in the objective, with the average ratio of parameters reduced by 28.1%. Moreover, we show that in 35.9% of the graphs this reduction can be achieved by simply using the largest symmetry. For the graphs where reducing the number of parameters leads to a decrease in the objective, the largest symmetry can be used to reduce the parameter count by 37.1% at the cost of only a 6.1% decrease in the objective. We demonstrate the central role of symmetries by showing that a random parameter reduction strategy leads to much worse performance.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"318 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123159078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Quantum Computing Methods for Supply Chain Management 供应链管理中的量子计算方法

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-09-17 DOI: 10.1109/SEC54971.2022.00059

Hansheng Jiang, Z. Shen, Junyu Liu

引用次数: 2

Quantum Data Reduction with Application to Video Classification 量子数据约简及其在视频分类中的应用

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-07-13 DOI: 10.1109/SEC54971.2022.00065

Kostas Blekos, D. Kosmopoulos

引用次数: 0

On Classifying Images using Quantum Image Representation 基于量子图像表示的图像分类研究

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2022-06-23 DOI: 10.1109/SEC54971.2022.00067

Ankit Khandelwal, M. Chandra, Sayantani Pramanik

引用次数: 2

A Quantum-Classical Hybrid Method for Image Classification and Segmentation 一种用于图像分类和分割的量子经典混合方法

2022 IEEE/ACM 7th Symposium on Edge Computing (SEC) Pub Date : 2021-09-25 DOI: 10.1109/SEC54971.2022.00068

Sayantani Pramanik, M. Chandra, C. V. Sridhar, Aniket Kulkarni, P. Sahoo, Vishwa Chethan, Hrishikesh Sharma, Ashutosh Paliwal, Vidyut Navelkar, Sudhakara Poojary, Pranav Shah, M. Nambiar

{"title":"A Quantum-Classical Hybrid Method for Image Classification and Segmentation","authors":"Sayantani Pramanik, M. Chandra, C. V. Sridhar, Aniket Kulkarni, P. Sahoo, Vishwa Chethan, Hrishikesh Sharma, Ashutosh Paliwal, Vidyut Navelkar, Sudhakara Poojary, Pranav Shah, M. Nambiar","doi":"10.1109/SEC54971.2022.00068","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00068","url":null,"abstract":"Enormous activity in the Quantum Computing area has resulted in it being considered, together with classical computers, for solving different difficult problems - including those of applied nature. An attempt is made in this work to assemble a pipeline consisting of both quantum and classical processing blocks for the task of image classification and segmentation, keeping in mind the present limitations of the gate-model quantum computers. It is based on the work done for the recent BMW Quantum Computing Challenge related to Automotive Industry. The pipeline handles the real-life sized images as the input and output, rather than the toy-sized examples prevalent in Quantum Computing literature. Apart from breaking down the problem to modules, some of which can be accommodated in the existing simulators and hardware, simplifications of the relevant quantum algorithms are also carried out. Its functionality and utility are brought out by applying it to surface crack segmentation on the popular Kaggle Surface Crack Detection data set. The results of the paper are not only limited to simulations, but also involve running models on Noisy, Intermediate-Scale Quantum processors through AWS. In its entirety, this work may lay the groundwork for quantum/quantum-enhanced image segmentation, and providing interested researchers with a stepping-stone in that direction, as the results demonstrate the efficacy of the proposed method, even with simple versions of the quantum modules.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116087950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 12