Quantum Machine Intelligence最新文献_第10页

Implementation of a Hamming distance-like genomic quantum classifier using inner products on ibmqx2 and ibmq_16_melbourne. 在ibmqx2和ibmq_16_melbourne上使用内积实现类似汉明距离的基因组量子分类器。

IF 4.8

Quantum Machine Intelligence Pub Date : 2020-01-01 Epub Date: 2020-07-17 DOI: 10.1007/s42484-020-00017-7

Kunal Kathuria, Aakrosh Ratan, Michael McConnell, Stefan Bekiranov

{"title":"Implementation of a Hamming distance-like genomic quantum classifier using inner products on ibmqx2 and ibmq_16_melbourne.","authors":"Kunal Kathuria, Aakrosh Ratan, Michael McConnell, Stefan Bekiranov","doi":"10.1007/s42484-020-00017-7","DOIUrl":"https://doi.org/10.1007/s42484-020-00017-7","url":null,"abstract":"Motivated by the problem of classifying individuals with a disease versus controls using a functional genomic attribute as input, we present relatively efficient general purpose inner product-based kernel classifiers to classify the test as a normal or disease sample. We encode each training sample as a string of 1 s (presence) and 0 s (absence) representing the attribute's existence across ordered physical blocks of the subdivided genome. Having binary-valued features allows for highly efficient data encoding in the computational basis for classifiers relying on binary operations. Given that a natural distance between binary strings is Hamming distance, which shares properties with bit-string inner products, our two classifiers apply different inner product measures for classification. The active inner product (AIP) is a direct dot product-based classifier whereas the symmetric inner product (SIP) classifies upon scoring correspondingly matching genomic attributes. SIP is a strongly Hamming distance-based classifier generally applicable to binary attribute-matching problems whereas AIP has general applications as a simple dot product-based classifier. The classifiers implement an inner product between N = 2 n dimension test and train vectors using n Fredkin gates while the training sets are respectively entangled with the class-label qubit, without use of an ancilla. Moreover, each training class can be composed of an arbitrary number m of samples that can be classically summed into one input string to effectively execute all test-train inner products simultaneously. Thus, our circuits require the same number of qubits for any number of training samples and are <math><mi>O</mi> <mo>(</mo> <mi>log</mi> <mi>N</mi> <mo>)</mo></math> in gate complexity after the states are prepared. Our classifiers were implemented on ibmqx2 (IBM-Q-team 2019b) and ibmq_16_melbourne (IBM-Q-team 2019a). The latter allowed encoding of 64 training features across the genome.","PeriodicalId":29924,"journal":{"name":"Quantum Machine Intelligence","volume":"2 1","pages":"1-26"},"PeriodicalIF":4.8,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s42484-020-00017-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38340063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 13

On the convergence of projective-simulation-based reinforcement learning in Markov decision processes. 论马尔可夫决策过程中基于投影模拟的强化学习的收敛性

IF 4.8

Quantum Machine Intelligence Pub Date : 2020-01-01 Epub Date: 2020-11-05 DOI: 10.1007/s42484-020-00023-9

W L Boyajian, J Clausen, L M Trenkwalder, V Dunjko, H J Briegel

{"title":"On the convergence of projective-simulation-based reinforcement learning in Markov decision processes.","authors":"W L Boyajian, J Clausen, L M Trenkwalder, V Dunjko, H J Briegel","doi":"10.1007/s42484-020-00023-9","DOIUrl":"10.1007/s42484-020-00023-9","url":null,"abstract":"In recent years, the interest in leveraging quantum effects for enhancing machine learning tasks has significantly increased. Many algorithms speeding up supervised and unsupervised learning were established. The first framework in which ways to exploit quantum resources specifically for the broader context of reinforcement learning were found is projective simulation. Projective simulation presents an agent-based reinforcement learning approach designed in a manner which may support quantum walk-based speedups. Although classical variants of projective simulation have been benchmarked against common reinforcement learning algorithms, very few formal theoretical analyses have been provided for its performance in standard learning scenarios. In this paper, we provide a detailed formal discussion of the properties of this model. Specifically, we prove that one version of the projective simulation model, understood as a reinforcement learning approach, converges to optimal behavior in a large class of Markov decision processes. This proof shows that a physically inspired approach to reinforcement learning can guarantee to converge.","PeriodicalId":29924,"journal":{"name":"Quantum Machine Intelligence","volume":"2 2","pages":"13"},"PeriodicalIF":4.8,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7644479/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9547619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Kernel methods in Quantum Machine Learning 量子机器学习中的核方法

IF 4.8

Quantum Machine Intelligence Pub Date : 2019-11-15 DOI: 10.1007/s42484-019-00007-4

R. Mengoni, Alessandra Di Pierro

引用次数: 33

A continuous-variable quantum-inspired algorithm for classical image segmentation 一种受连续变量量子启发的经典图像分割算法

IF 4.8

Quantum Machine Intelligence Pub Date : 2019-11-11 DOI: 10.1007/s42484-019-00009-2

A. Youssry, A. El-Rafei, Ri-gui Zhou

引用次数: 4

Application of machine intelligence for osteoarthritis classification: a classical implementation and a quantum perspective 机器智能在骨关节炎分类中的应用：经典实现和量子视角

IF 4.8

Quantum Machine Intelligence Pub Date : 2019-10-31 DOI: 10.1007/s42484-019-00008-3

S. Moustakidis, Eirini Christodoulou, E. Papageorgiou, Christos Kokkotis, N. Papandrianos, D. Tsaopoulos

引用次数: 22

Quantum semi-supervised kernel learning 量子半监督核学习

IF 4.8

Quantum Machine Intelligence Pub Date : 2019-09-25 DOI: 10.1007/s42484-021-00053-x

Seyran Saeedi, Ali (Aliakbar) Panahi, Tom Arodz

引用次数: 3

Nondestructive classification of quantum states using an algorithmic quantum computer 使用算法量子计算机的量子态无损分类

IF 4.8

Quantum Machine Intelligence Pub Date : 2019-09-12 DOI: 10.1007/s42484-019-00010-9

D. Babukhin, A. Zhukov, W. Pogosov

引用次数: 6

An evolutionary strategy for finding effective quantum 2-body Hamiltonians of p-body interacting systems 寻找p体相互作用系统有效量子2体哈密顿量的进化策略

IF 4.8

Quantum Machine Intelligence Pub Date : 2019-09-11 DOI: 10.1007/s42484-019-00011-8

G. Acampora, V. Cataudella, P. R. Hegde, P. Lucignano, G. Passarelli, A. Vitiello

引用次数: 10

Charged particle tracking with quantum annealing optimization 基于量子退火优化的带电粒子跟踪

IF 4.8

Quantum Machine Intelligence Pub Date : 2019-08-13 DOI: 10.1007/s42484-021-00054-w

Alexander Zlokapa, A. Anand, J. Vlimant, Javier Mauricio Duarte, Joshua Job, Daniel A. Lidar, M. Spiropulu

引用次数: 21

Optimizing quantum heuristics with meta-learning 优化量子启发式与元学习

IF 4.8

Quantum Machine Intelligence Pub Date : 2019-08-08 DOI: 10.1007/s42484-020-00022-w

M. Wilson, Rachel Stromswold, F. Wudarski, Stuart Hadfield, N. Tubman, E. Rieffel

引用次数: 57