ACM Transactions on Quantum Computing最新文献

ARQUIN : Architectures for Multinode Superconducting Quantum Computers ARQUIN：多节点超导量子计算机体系结构

ACM Transactions on Quantum Computing Pub Date : 2024-07-26 DOI: 10.1145/3674151

James A. Ang, Gabriella Carini, Yanzhu Chen, Isaac Chuang, Michael Demarco, Sophia Economou, A. Eickbusch, Andrei Faraon, Kai-Mei C. Fu, Steven Girvin, M. Hatridge, A. Houck, Paul Hilaire, Kevin Krsulich, Ang Li, Chenxu Liu, Yuan Liu, M. Martonosi, David McKay, Jim Misewich, Mark Ritter, R. Schoelkopf, S. Stein, S. Sussman, Hong Tang, Wei Tang, T. Tomesh, N. Tubman, Chen Wang, Nathan Wiebe, Yongxi Yao, D. Yost, Yiyu Zhou

{"title":"ARQUIN : Architectures for Multinode Superconducting Quantum Computers","authors":"James A. Ang, Gabriella Carini, Yanzhu Chen, Isaac Chuang, Michael Demarco, Sophia Economou, A. Eickbusch, Andrei Faraon, Kai-Mei C. Fu, Steven Girvin, M. Hatridge, A. Houck, Paul Hilaire, Kevin Krsulich, Ang Li, Chenxu Liu, Yuan Liu, M. Martonosi, David McKay, Jim Misewich, Mark Ritter, R. Schoelkopf, S. Stein, S. Sussman, Hong Tang, Wei Tang, T. Tomesh, N. Tubman, Chen Wang, Nathan Wiebe, Yongxi Yao, D. Yost, Yiyu Zhou","doi":"10.1145/3674151","DOIUrl":"https://doi.org/10.1145/3674151","url":null,"abstract":"Many proposals to scale quantum technology rely on modular or distributed designs wherein individual quantum processors, called nodes, are linked together to form one large multinode quantum computer (MNQC). One scalable method to construct an MNQC is using superconducting quantum systems with optical interconnects. However, internode gates in these systems may be two to three orders of magnitude noisier and slower than local operations. Surmounting the limitations of internode gates will require improvements in entanglement generation, use of entanglement distillation, and optimized software and compilers. Still, it remains unclear what performance is possible with current hardware and what performance algorithms require. In this paper, we employ a systems analysis approach to quantify overall MNQC performance in terms of hardware models of internode links, entanglement distillation, and local architecture. We show how to navigate tradeoffs in entanglement generation and distillation in the context of algorithm performance, lay out how compilers and software should balance between local and internode gates, and discuss when noisy quantum internode links have an advantage over purely classical links. We find that a factor of 10-100x better link performance is required and introduce a research roadmap for the co-design of hardware and software towards the realization of early MNQCs. While we focus on superconducting devices with optical interconnects, our approach is general across MNQC implementations","PeriodicalId":504393,"journal":{"name":"ACM Transactions on Quantum Computing","volume":"47 47","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141799825","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: 0

Utilizing classical programming principles in the Intel Quantum SDK: implementation of quantum lattice Boltzmann method 在英特尔量子 SDK 中利用经典编程原理：量子点阵玻尔兹曼法的实现

ACM Transactions on Quantum Computing Pub Date : 2024-07-05 DOI: 10.1145/3678185

Tejas Shinde, L. Budinski, Ossi Niemimäki, Valtteri Lahtinen, Helena Liebelt, Rui Li

引用次数: 0

Synthesis Techniques for Fault-tolerant Quantum Circuit Implementation using the Clifford+Z_N-group 利用克利福德+Z_N 组实现容错量子电路的合成技术

ACM Transactions on Quantum Computing Pub Date : 2024-06-14 DOI: 10.1145/3673240

L. Biswal, Debjyoti Bhattacharjee, Amlan Chakrabarti, Anupam Chattopadhyay

引用次数: 0

Q-SupCon: Quantum-Enhanced Supervised Contrastive Learning Architecture within the Representation Learning Framework Q-SupCon：表征学习框架内的量子增强监督对比学习架构

ACM Transactions on Quantum Computing Pub Date : 2024-04-22 DOI: 10.1145/3660647

Asitha Kottahachchi Kankanamge Don, Ibrahim Khalil

{"title":"Q-SupCon: Quantum-Enhanced Supervised Contrastive Learning Architecture within the Representation Learning Framework","authors":"Asitha Kottahachchi Kankanamge Don, Ibrahim Khalil","doi":"10.1145/3660647","DOIUrl":"https://doi.org/10.1145/3660647","url":null,"abstract":"In the evolving landscape of data privacy regulations, the challenge of providing extensive data for robust deep classification models arises. The accuracy of these models relies on the amount of training data, due to the multitude of parameters that require tuning. Unfortunately, obtaining such ample data proves challenging, particularly in domains like medical applications, where there is a pressing need for robust models for early disease detection but a shortage of labeled data. Nevertheless, the classical supervised contrastive learning models, have shown the potential to address this challenge up to a certain limit, by utilizing deep encoder models. However, recent advancements in quantum machine learning enable the extraction of meaningful representations from extremely limited and simple data. Thus, replacing classical counterparts in classical or hybrid quantum-classical supervised contrastive models enhances feature learning capability with minimal data. Therefore, this work proposes the Q-SupCon model, a fully quantum-powered supervised contrastive learning model comprising a quantum data augmentation circuit, quantum encoder, quantum projection head, and quantum variational classifier, enabling efficient image classification with minimal labeled data. Furthermore, the novel model attains 80%, 60%, and 80% test accuracy on MNIST, KMNIST, and FMNIST datasets, marking a significant advancement in addressing the data scarcity challenge.","PeriodicalId":504393,"journal":{"name":"ACM Transactions on Quantum Computing","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140672932","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: 0

A characterization of quantum generative models 量子生成模型的特征

ACM Transactions on Quantum Computing Pub Date : 2024-04-04 DOI: 10.1145/3655027

Carlos A. Riofrío, Oliver Mitevski, Caitlin Jones, Florian Krellner, Aleksandar Vučković, Joseph Doetsch, Johannes Klepsch, T. Ehmer, André Luckow

{"title":"A characterization of quantum generative models","authors":"Carlos A. Riofrío, Oliver Mitevski, Caitlin Jones, Florian Krellner, Aleksandar Vučković, Joseph Doetsch, Johannes Klepsch, T. Ehmer, André Luckow","doi":"10.1145/3655027","DOIUrl":"https://doi.org/10.1145/3655027","url":null,"abstract":"\u0000 Quantum generative modeling is a growing area of interest for industry-relevant applications. This work systematically compares a broad range of techniques to guide quantum computing practitioners when deciding which models and methods to use in their applications. We compare fundamentally different architectural ansatzes of parametric quantum circuits: 1. A\u0000 continuous\u0000 architecture, which produces continuous-valued data samples, and 2. a\u0000 discrete\u0000 architecture, which samples on a discrete grid. We also compare the performance of different data transformations: the min-max and the probability integral transforms. We use two popular training methods: 1. quantum circuit Born machines (QCBM), and 2. quantum generative adversarial networks (QGAN). We study their performance and trade-offs as the number of model parameters increases, with a baseline comparison of similarly trained classical neural networks. The study is performed on six low-dimensional synthetic and two real financial data sets. Our two key findings are that: 1. For all data sets, our quantum models require similar or fewer parameters than their classical counterparts. In the extreme case, the quantum models require two orders of magnitude less parameters. 2. We empirically find that a variant of the\u0000 discrete\u0000 architecture, which learns the copula of the probability distribution, outperforms all other methods.\u0000","PeriodicalId":504393,"journal":{"name":"ACM Transactions on Quantum Computing","volume":"24 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140744887","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: 0

Quantum Measurement Classification using Statistical Learning 利用统计学习进行量子测量分类

ACM Transactions on Quantum Computing Pub Date : 2024-02-09 DOI: 10.1145/3644823

Zachery Utt, Daniel Volya, Prabhat Mishra

{"title":"Quantum Measurement Classification using Statistical Learning","authors":"Zachery Utt, Daniel Volya, Prabhat Mishra","doi":"10.1145/3644823","DOIUrl":"https://doi.org/10.1145/3644823","url":null,"abstract":"Interpreting the results of a quantum computer can pose a significant challenge due to inherent noise in these mesoscopic quantum systems. Quantum measurement, a critical component of quantum computing, involves determining the probabilities linked with qubit states post-multiple circuit computations based on quantum readout values provided by hardware. While there are promising classification-based solutions, they can either misclassify or necessitate excessive measurements, thereby proving to be costly. This paper puts forth an efficient method to discern the quantum state by analyzing the probability distributions of data post-measurement. Specifically, we employ cumulative distribution functions to juxtapose the measured distribution of a sample against the distributions of basis states. The efficacy of our approach is demonstrated through experimental results on a superconducting transmon qubit architecture, which show a substantial decrease (88%) in single qubit readout error compared to state of the art measurement techniques. Moreover, we report additional error reduction (12%) compared to state-of-the-art measurement techniques when our technique is applied to enhance existing multi-qubit classification techniques. We also demonstrate the applicability of our proposed method for higher dimensional quantum systems, including classification of single qutrits as well as multiple qutrits.","PeriodicalId":504393,"journal":{"name":"ACM Transactions on Quantum Computing","volume":" 56","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139788217","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: 0

Quantum Measurement Classification using Statistical Learning 利用统计学习进行量子测量分类

ACM Transactions on Quantum Computing Pub Date : 2024-02-09 DOI: 10.1145/3644823

Zachery Utt, Daniel Volya, Prabhat Mishra

{"title":"Quantum Measurement Classification using Statistical Learning","authors":"Zachery Utt, Daniel Volya, Prabhat Mishra","doi":"10.1145/3644823","DOIUrl":"https://doi.org/10.1145/3644823","url":null,"abstract":"Interpreting the results of a quantum computer can pose a significant challenge due to inherent noise in these mesoscopic quantum systems. Quantum measurement, a critical component of quantum computing, involves determining the probabilities linked with qubit states post-multiple circuit computations based on quantum readout values provided by hardware. While there are promising classification-based solutions, they can either misclassify or necessitate excessive measurements, thereby proving to be costly. This paper puts forth an efficient method to discern the quantum state by analyzing the probability distributions of data post-measurement. Specifically, we employ cumulative distribution functions to juxtapose the measured distribution of a sample against the distributions of basis states. The efficacy of our approach is demonstrated through experimental results on a superconducting transmon qubit architecture, which show a substantial decrease (88%) in single qubit readout error compared to state of the art measurement techniques. Moreover, we report additional error reduction (12%) compared to state-of-the-art measurement techniques when our technique is applied to enhance existing multi-qubit classification techniques. We also demonstrate the applicability of our proposed method for higher dimensional quantum systems, including classification of single qutrits as well as multiple qutrits.","PeriodicalId":504393,"journal":{"name":"ACM Transactions on Quantum Computing","volume":"393 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139848086","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: 0