Quantum Machine Intelligence最新文献_第2页

The effects of quantum hardware properties on the performances of variational quantum learning algorithms 量子硬件特性对变分量子学习算法性能的影响

IF 4.8

Quantum Machine Intelligence Pub Date : 2024-02-05 DOI: 10.1007/s42484-024-00144-5

G. Buonaiuto, Francesco Gargiulo, G. De Pietro, M. Esposito, Marco Pota

引用次数: 0

Quantum neural networks successfully calibrate language models 量子神经网络成功校准语言模型

IF 4.8

Quantum Machine Intelligence Pub Date : 2024-02-05 DOI: 10.1007/s42484-024-00139-2

J. Neto, Lluis Quiles Ardila, Thiago Nascimento Nogueira, Felipe Albuquerque, J. P. Papa, Rodrigo Capobianco Guido, Felipe Fernandes Fanchini

引用次数: 0

The effects of quantum hardware properties on the performances of variational quantum learning algorithms 量子硬件特性对变分量子学习算法性能的影响

IF 4.8

Quantum Machine Intelligence Pub Date : 2024-02-05 DOI: 10.1007/s42484-024-00144-5

G. Buonaiuto, Francesco Gargiulo, G. De Pietro, M. Esposito, Marco Pota

引用次数: 0

Time series quantum classifiers with amplitude embedding 带振幅嵌入的时间序列量子分类器

IF 4.8

Quantum Machine Intelligence Pub Date : 2023-11-27 DOI: 10.1007/s42484-023-00133-0

Manuel P. Cuéllar, Carlos Cano Gutierrez, Luis G. Baca Ruíz, Lorenzo Servadei

引用次数: 0

Conditional generative models for learning stochastic processes 学习随机过程的条件生成模型

Quantum Machine Intelligence Pub Date : 2023-10-13 DOI: 10.1007/s42484-023-00129-w

Salvatore Certo, Anh Pham, Nicolas Robles, Andrew Vlasic

引用次数: 2

Quantum convolutional neural networks for multi-channel supervised learning 多通道监督学习的量子卷积神经网络

Quantum Machine Intelligence Pub Date : 2023-10-09 DOI: 10.1007/s42484-023-00130-3

Anthony M. Smaldone, Gregory W. Kyro, Victor S. Batista

引用次数: 0

Interaction graph-based characterization of quantum benchmarks for improving quantum circuit mapping techniques 基于交互图的量子基准表征改进量子电路映射技术

Quantum Machine Intelligence Pub Date : 2023-10-06 DOI: 10.1007/s42484-023-00124-1

Medina Bandic, Carmen G. Almudever, Sebastian Feld

引用次数: 4

Resource saving via ensemble techniques for quantum neural networks 基于量子神经网络集成技术的资源节约

Quantum Machine Intelligence Pub Date : 2023-09-29 DOI: 10.1007/s42484-023-00126-z

Massimiliano Incudini, Michele Grossi, Andrea Ceschini, Antonio Mandarino, Massimo Panella, Sofia Vallecorsa, David Windridge

{"title":"Resource saving via ensemble techniques for quantum neural networks","authors":"Massimiliano Incudini, Michele Grossi, Andrea Ceschini, Antonio Mandarino, Massimo Panella, Sofia Vallecorsa, David Windridge","doi":"10.1007/s42484-023-00126-z","DOIUrl":"https://doi.org/10.1007/s42484-023-00126-z","url":null,"abstract":"Abstract Quantum neural networks hold significant promise for numerous applications, particularly as they can be executed on the current generation of quantum hardware. However, due to limited qubits or hardware noise, conducting large-scale experiments often requires significant resources. Moreover, the output of the model is susceptible to corruption by quantum hardware noise. To address this issue, we propose the use of ensemble techniques, which involve constructing a single machine learning model based on multiple instances of quantum neural networks. In particular, we implement bagging and AdaBoost techniques, with different data loading configurations, and evaluate their performance on both synthetic and real-world classification and regression tasks. To assess the potential performance improvement under different environments, we conducted experiments on both simulated, noiseless software and IBM superconducting-based QPUs, suggesting these techniques can mitigate the quantum hardware noise. Additionally, we quantify the amount of resources saved using these ensemble techniques. Our findings indicate that these methods enable the construction of large, powerful models even on relatively small quantum devices.","PeriodicalId":29924,"journal":{"name":"Quantum Machine Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135199040","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: 2

Hybrid quantum ResNet for car classification and its hyperparameter optimization 混合量子ResNet汽车分类及其超参数优化

Quantum Machine Intelligence Pub Date : 2023-09-29 DOI: 10.1007/s42484-023-00123-2

Asel Sagingalieva, Mo Kordzanganeh, Andrii Kurkin, Artem Melnikov, Daniil Kuhmistrov, Michael Perelshtein, Alexey Melnikov, Andrea Skolik, David Von Dollen

{"title":"Hybrid quantum ResNet for car classification and its hyperparameter optimization","authors":"Asel Sagingalieva, Mo Kordzanganeh, Andrii Kurkin, Artem Melnikov, Daniil Kuhmistrov, Michael Perelshtein, Alexey Melnikov, Andrea Skolik, David Von Dollen","doi":"10.1007/s42484-023-00123-2","DOIUrl":"https://doi.org/10.1007/s42484-023-00123-2","url":null,"abstract":"Abstract Image recognition is one of the primary applications of machine learning algorithms. Nevertheless, machine learning models used in modern image recognition systems consist of millions of parameters that usually require significant computational time to be adjusted. Moreover, adjustment of model hyperparameters leads to additional overhead. Because of this, new developments in machine learning models and hyperparameter optimization techniques are required. This paper presents a quantum-inspired hyperparameter optimization technique and a hybrid quantum-classical machine learning model for supervised learning. We benchmark our hyperparameter optimization method over standard black-box objective functions and observe performance improvements in the form of reduced expected run times and fitness in response to the growth in the size of the search space. We test our approaches in a car image classification task and demonstrate a full-scale implementation of the hybrid quantum ResNet model with the tensor train hyperparameter optimization. Our tests show a qualitative and quantitative advantage over the corresponding standard classical tabular grid search approach used with a deep neural network ResNet34. A classification accuracy of 0.97 was obtained by the hybrid model after 18 iterations, whereas the classical model achieved an accuracy of 0.92 after 75 iterations.","PeriodicalId":29924,"journal":{"name":"Quantum Machine Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135131986","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: 18

A quantum “black box” for entropy calculation 熵计算的量子“黑箱”

IF 4.8

Quantum Machine Intelligence Pub Date : 2023-08-31 DOI: 10.1007/s42484-023-00127-y

Michal Koren, Oded Koren, Or Peretz

引用次数: 0