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Jacob Biamonte
621 followers -
Lead of the Quantum Complexity Science Initiative
Lead of the Quantum Complexity Science Initiative

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New work with +Jacob Biamonte using quantum thermodynamics and quantum computing concepts to build a theory at the edge of network science and information theory. (Direct link: https://journals.aps.org/prx/abstract/10.1103/PhysRevX.6.041062)

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+André Xuereb is organizing another massive workshop in Malta, this time on quantum technologies in space. His workshops are good. I will be there.

Submission deadline: December 31, 2017
More information at: https://qtspacemalta.sciencesconf.org/

Programme committee

Mauro Paternostro (chair)

Angelo Bassi

Simon Gröblacher

Yasser Omar

Hendrik Ulbricht

Rainer Kaltenbaek

Christoph Marquardt
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Quantum Machine Learning

with Peter Wittek, +Nicola Pancotti, Patrick Rebentrost, +Nathan Wiebe, Seth Lloyd --- https://arxiv.org/abs/1611.09347

Recent progress implies that a crossover between machine learning and quantum information processing benefits both fields. Traditional machine learning has dramatically improved the benchmarking and control of experimental quantum computing systems, including adaptive quantum phase estimation and designing quantum computing gates. On the other hand, quantum mechanics offers tantalizing prospects to enhance machine learning, ranging from reduced computational complexity to improved generalization performance. The most notable examples include quantum enhanced algorithms for principal component analysis, quantum support vector machines, and quantum Boltzmann machines. Progress has been rapid, fostered by demonstrations of midsized quantum optimizers which are predicted to soon outperform their classical counterparts. Further, we are witnessing the emergence of a physical theory pinpointing the fundamental and natural limitations of learning. Here we survey the cutting edge of this merger and list several open problems.
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Harvard Workshop on Subfactor Theory, Quantum Field Theory, and Quantum Information - Oct 8th, 9th and 10th (http://www.math.harvard.edu/conferences/qft16/)

There's recently been some exciting developments in the area of tensor networks, revolving around what's known as the 'two-string' model and bringing with it some of the magical machinery that lead Jones to a fields medal. He'll be attending this workshop as will many others including +Zoltan Zimboras and +Alex Wozniakowski (who is partly responsible for this workshop even happening at all!). After the meeting, perhaps we'll head on up and see +Jay Whitfield, now assistant prof. at Dartmouth.
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Spectral entropies as information-theoretic tools for complex network comparison

A new paper with +Manlio De Domenico which maps ideas from quantum statistical mechanics over to the network comparison problem

http://arxiv.org/abs/1609.01214

(Submitted on 5 Sep 2016)

Any physical system can be viewed from the perspective that information is implicitly represented in its state. However, the quantification of this information when it comes to complex networks has remained largely elusive. In this work, we use techniques inspired by quantum statistical mechanics to define an entropy measure for complex networks and to develop a set of information-theoretic tools, based on network spectral properties, such as Renyi q-entropy, generalized Kullback-Leibler and Jensen-Shannon divergences, the latter allowing us to define a natural distance measure between complex networks. First we show that by minimizing the Kullback-Leibler divergence between an observed network and a parametric network model, inference of model parameter(s) by means of maximum-likelihood estimation can be achieved and model selection can be performed appropriate information criteria. Second, we show that the information-theoretic metric quantifies the distance between pairs of networks and we can use it, for instance, to cluster the layers of a multilayer system. By applying this framework to networks corresponding to sites of the human microbiome, we perform hierarchical cluster analysis and recover with high accuracy existing community-based associations. Our results imply that spectral based statistical inference in complex networks results in demonstrably superior performance as well as a conceptual backbone, filling a gap towards a network information theory. 
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You've heard about Schrödinger's cat but what about Wigner's friend?

"The thought experiment posits a friend of Wigner who performs the Schrödinger's cat experiment after Wigner leaves the laboratory. Only when he returns does Wigner learn the result of the experiment from his friend, that is, whether the cat is alive or dead. The question is raised: was the state of the system a superposition of "dead cat/sad friend" and "live cat/happy friend," only determined when Wigner learned the result of the experiment, or was it determined at some previous point?"

https://en.wikipedia.org/wiki/Wigner's_friend

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Our artist is working on some 3D tensor network visualizations - here's an AKLT tensor network state.
-- http://quamplexity.org/tensor-network-states/

illustrated by +Lusa Zeglova 
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I wish I had time to attend this big Italian event on complex networks in Milan. 

Dear Colleagues,
The Fifth International Workshop on Complex Networks and their Applications will be held in Milan – Italy on November 30 – December 02, 2016.
http://www.complexnetworks.org.
 
* Deadline for submission: September 05, 2016 *
 
Keynote Speakers (confirmed):
Guido Caldarelli                IMT Lucca, Italy
Raissa D'Souza                   University of California, Davis, USA
Renauld Lambiotte          University of Namur, Belgium
Yamir Moreno                   University of Zaragoza, Spain
Aiko Yoneki                        University of Cambridge, UK
Ben T. Zhao                         University of California, Santa Barbara, USA
 
Tutorials (confirmed):
Ernesto Estrada                 University of Strathclyde, Glasgow, UK
Bruno Goncalves              New York University, USA
 
Full papers and Extended Abstracts are welcome:
·         Accepted papers will be included in the proceedings published by Springer
·         Accepted extended abstracts will be included in the book of abstracts (with ISBN)
Extended versions of accepted contributions will be invited for publication in special issues of international journals edited by Springer:
·         Computational Social Networks
·         Applied Network Science
Both type of contributions can be accepted either for oral or poster presentation.
Full papers that, at the time of submission, are under review for or have already been published or accepted for publication in a journal or conference will not be accepted. This restriction does not apply to extended abstracts.
Accepted Extended abstracts will be published in the Book of Abstracts (with ISBN) together with the abstracts of the keynote presentations.
Accepted Full papers will be included in the workshop proceedings edited by Springer. Authors will be required to transfer copyright to Springer. The proceedings are published on the Studies in Computational Intelligence Series (http://www.springer.com/series/7092).
 
Scope of the Workshop
The International Workshop on Complex Networks and their Applications aims at bringing together researchers and practitioners from different science communities working on areas related to complex networks.
Two types of contributions are welcome: theoretical developments arising from practical problems, and case studies where methodologies are applied.
Authors are encouraged to submit both theoretical and applied papers on their research in complex networks. Topics for the workshop include, but are not limited to:
 
Models of Complex Networks
Structural Network Properties and Analysis
Complex Networks and Epidemics
Community Structure in Networks
Community Discovery in Complex Networks
Motif Discovery in Complex Networks
Complex Networks Mining
Dynamics and Evolution Patterns of Complex Networks
Link Prediction
Multiplex Networks
Network Controllability
Synchronization in Networks
Structural Network Properties and Analysis
Algorithms for Complex Network Analysis
Visual Representation of Complex Networks
Applications of Complex Network Analysis
Large-scale Graph Analytics
Social Reputation, Influence, and Trust
Information Spreading in Social Media
Rumour and Viral Marketing in Social Networks
Recommendation Systems and Complex Networks
Financial and Economic Networks
Biological networks
Complex Networks and Mobility
 
Important dates
Paper Submission: September 05, 2016
Acceptance/Reject notification: October 02, 2016
Camera ready: October 09, 2016
Author registration: October 20, 2016
 
For any question, please email the General Chair Hocine Cherifi
(hocine.cherifi@u-bourgogne.fr).
 
 
Sabrina Gaito
Dipartimento di Informatica
Università degli Studi di Milano
tel: +390250316282
skype: sabrina.consoli
web: nptlab.di.unimi.it
 
************************************************************************
               Join us at Complex Networks 2016 Milan
 
                    complexnetworks.org
************************************************************************
 

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This winter I will be lecturing an intensive course on quantum computing at Zhejiang University, Hangzhou.  The trip will include several visits to research groups in nearby cities, several lectures and also a research focused mini-course on tensor network states. My visit and the course will be hosted by Wu Junde and the course is open to interested graduate students and advanced undergraduates.   
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Tensor networks resource page updated---a tutorial will soon be published on the arXiv. #TensorNetworks  
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