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Programming multi-level quantum gates in disordered computing reservoirs via machine learning and TensorFlow [article]

Giulia Marcucci and Davide Pierangeli and Pepijn Pinkse and Mehul Malik and Claudio Conti
2019 arXiv   pre-print
Here we adopt the open-source programming library TensorFlow to design multi-level quantum gates including a computing reservoir represented by a random unitary matrix.  ...  In optics, the reservoir is a disordered medium or a multi-modal fiber. We show that trainable operators at the input and the readout enable one to realize multi-level gates.  ...  CONCLUSIONS We have investigated the use of machine learning paradigms for designing linear multi-level quantum gates by using a complex transmitting multi-modal system.  ... 
arXiv:1905.05264v3 fatcat:42opw5etnvgf5e6uq4i4hyp62y

Programming multi-level quantum gates indisordered computing reservoirs via machinelearning and TensorFlow

Claudio Conti, Giulia Marcucci, Davide Pierangeli, Mehul Malik, Pepijn Pinkse
2020 Optics Express  
Here we adopt the open-source programming library TensorFlow to design multi-level quantum gates, including a computing reservoir represented by a random unitary matrix.  ...  In optics, the reservoir is a disordered medium or a multi-modal fiber. We show that trainable operators at the input and the readout enable one to realize multi-level gates.  ...  Conclusion We have investigated the use of machine learning paradigms for designing linear multi-level quantum gates by using a complex transmitting multi-modal system.  ... 
doi:10.1364/oe.389432 pmid:32403865 fatcat:ifpua7sgrzf37cxyzrx5cuxw2i

Quantum Simulators: Architectures and Opportunities [article]

Ehud Altman, Kenneth R. Brown, Giuseppe Carleo, Lincoln D. Carr, Eugene Demler, Cheng Chin, Brian DeMarco, Sophia E. Economou, Mark A. Eriksson, Kai-Mei C. Fu, Markus Greiner, Kaden R. A. Hazzard, Randall G. Hulet (+23 others)
2019 arXiv   pre-print
in order to accelerate the progress in this field and to result in the first practical applications of quantum machines.  ...  effort in industry; and (2) support for fundamental research carried out by a blend of multi-investigator, multi-disciplinary collaborations with resources for quantum simulator software, hardware, and  ...  Applications to computer science could include hybrid digital/analog quantum computing, quantum approaches to combinatorial optimization problems, and quantum machine learning [20] .  ... 
arXiv:1912.06938v2 fatcat:mwsuktzfpzan7ol26etyrbystu

Quantum Simulators: Architectures and Opportunities

Ehud Altman, Kenneth R. Brown, Giuseppe Carleo, Lincoln D. Carr, Eugene Demler, Cheng Chin, Brian DeMarco, Sophia E. Economou, Mark A. Eriksson, Kai-Mei C. Fu, Markus Greiner, Kaden R.A. Hazzard (+25 others)
2021 PRX Quantum  
Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Commerce, the U.S.  ...  Applications to computer science could include hybrid digital-analog quantum computing, quantum approaches to combinatorial optimization problems, and quantum machine learning [21] .  ...  CONCLUSIONS Despite advances in high-performance conventional computing, machine learning, and artificial intelligence, simulations on classical hardware remain unable to address many key scientific problems  ... 
doi:10.1103/prxquantum.2.017003 fatcat:e3g43oz4cvehvfu6q4bsduncby

Can Biological Quantum Networks Solve NP-Hard Problems?

Göran Wendin
2019 Advanced Quantum Technologies  
On the other hand, artificial intelligence and machine learning implemented in complex dynamical systems based on genuine quantum networks can certainly be expected to show enhanced performance and quantum  ...  Quantum effects may certainly influence the functionality of various components and signalling pathways at the molecular level in the brain network, like ion ports, synapses, sensors, and enzymes.  ...  Acknowledgement This work has been partially supported by the Knut & Alice Wallenberg Foundation (KAW) (WACQT project), and by the EU Quantum Technologies Flagship (820363 -QpenSuperQ).  ... 
doi:10.1002/qute.201800081 fatcat:nud3r5wn3zbwbknq7hvbwamye4

2020 Index IEEE Journal of Selected Topics in Quantum Electronics Vol. 26

2020 IEEE Journal of Selected Topics in Quantum Electronics  
Quantum Dot Micropillar Arrays for Optical Reservoir Computing.  ...  Analysis of Microresonator-Based Logic Gate for High-Speed Optical Computing in Integrated Photonics.  ...  Time series All-Optical WDM Recurrent Neural Networks With Gating. Mourgias-Alexandris, G., +, JSTQE  ... 
doi:10.1109/jstqe.2020.3048204 fatcat:6feiciybzraibah57mi7up6ph4

The promise of spintronics for unconventional computing [article]

Giovanni Finocchio, Massimiliano Di Ventra, Kerem Y. Camsari, Karin Everschor-Sitte, Pedram Khalili Amiri, Zhongming Zeng
2019 arXiv   pre-print
three unconventional computing paradigms, namely, reservoir computing, probabilistic computing and memcomputing that in our opinion may be used to address some limitations of modern computers, providing  ...  In this perspective, we will discuss how spintronics may aid in the realization of efficient devices primarily based on magnetic tunnel junctions and how those devices can impact in the development of  ...  ") and sampling from a probability distribution. 16 The types of problems that can be addressed by p-circuits are relevant for Machine Learning and Quantum Computing.  ... 
arXiv:1910.07176v1 fatcat:tr5nlskrbzggrhnilqpt7jo6ie

Temporal Information Processing on Noisy Quantum Computers [article]

Jiayin Chen and Hendra I. Nurdin and Naoki Yamamoto
2020 arXiv   pre-print
The combination of machine learning and quantum computing has emerged as a promising approach for addressing previously untenable problems.  ...  We describe a subclass of the universal class that is readily implementable using quantum gates native to current noisy gate-model quantum computers.  ...  INTRODUCTION The ingenious use of quantum effects has led to a significant number of quantum machine learning algorithms that offer computational speed-ups [1, 2] .  ... 
arXiv:2001.09498v2 fatcat:klylwjyfrzf3lf7g7acw4lebrm

Thermodynamic Computing [article]

Tom Conte, Erik DeBenedictis, Natesh Ganesh, Todd Hylton, John Paul Strachan, R. Stanley Williams, Alexander Alemi, Lee Altenberg, Gavin Crooks, James Crutchfield, Lidia del Rio, Josh Deutsch, Michael DeWeese (+26 others)
2019 arXiv   pre-print
In terms of software, our ability to imagine and program effective computational abstractions and implementations are clearly challenged in complex domains.  ...  We propose that progress in computing can continue under a united, physically grounded, computational paradigm centered on thermodynamics.  ...  In thermodynamic parlance, machine learning systems evolve structure via parameter refinement (on a cost/loss/energy function) to exploit (informational) free energy in the data sets on which they are  ... 
arXiv:1911.01968v2 fatcat:uz4vlzhr3jfdxpbmyy6jq56dty

Artificial Intelligence and Advanced Materials [article]

Cefe López
2022 arXiv   pre-print
In a simultaneous progress race, new materials, systems and processes can be devised and optimized thanks to machine learning techniques and such progress can be turned into in-novative computing platforms  ...  Machine learning and its methods are reviewed to provide basic knowledge on its implementation and its potential.  ...  This length depends on the machine and the sequence but Kolmogorov proved that there exists a "universal" machine capable to carry out the same computation a program executes in a given machine but with  ... 
arXiv:2209.11618v2 fatcat:2tsodaoyjjb3jhuewnauvamjfy

A machine learning approach for automated fine-tuning of semiconductor spin qubits

Julian D. Teske, Simon Sebastian Humpohl, René Otten, Patrick Bethke, Pascal Cerfontaine, Jonas Dedden, Arne Ludwig, Andreas D. Wieck, Hendrik Bluhm
2019 Applied Physics Letters  
While spin qubits based on gate-defined quantum dots have demonstrated very favorable properties for quantum computing, one remaining hurdle is the need to tune each of them into a good operating regime  ...  We present an algorithm for the automated fine-tuning of quantum dots, and demonstrate its performance on a semiconductor singlet-triplet qubit in GaAs.  ...  reached. 17 Here, we propose and experimentally demonstrate an algorithm for the fine-tuning of qubits based on gate-defined quantum dots, which exploits machine learning for improving the efficiency  ... 
doi:10.1063/1.5088412 fatcat:oaq2fvt7tvdthbx6qrgx63wgv4

All-Electrical Control of a Hybrid Electron Spin/Valley Quantum Bit in SOI CMOS Technology

Leo Bourdet, Louis Hutin, Benoit Bertrand, Andrea Corna, Heorhii Bohuslavskyi, Anthony Amisse, Alessandro Crippa, Romain Maurand, Sylvain Barraud, Matias Urdampilleta, Christopher Bauerle, Tristan Meunier (+5 others)
2018 IEEE Transactions on Electron Devices  
This proposed scheme bears relevance to improve the trade-off between fast operations and slow decoherence for quantum computing on a Si qubit platform.  ...  We fabricated Quantum Dot (QD) devices using a standard SOI CMOS process flow, and demonstrated that the spin of confined electrons could be controlled via a local electrical-field excitation, owing to  ...  Part of the calculations was run on the TGCC/Curie and CINECA/Marconi machines using allocations from GENCI and PRACE.  ... 
doi:10.1109/ted.2018.2870115 fatcat:6skycyexsfad3eo7hhprlapmse

Quantum Inspire: QuTech's platform for co-development and collaboration in quantum computing

Thorsten Last, Nodar Samkharadze, Pieter Eendebak, Richard Versluis, Xiao Xue, Amir Sammak, Delphine Brousse, Kelvin Loh, Henk Polinder, Giordano Scappucci, Menno Veldhorst, Lieven Vandersypen (+5 others)
2020 Novel Patterning Technologies for Semiconductors, MEMS/NEMS and MOEMS 2020  
To this end we are developing Quantum Inspire (QI), a full-stack quantum computer prototype for future co-development and collaborative R&D in quantum computing.  ...  The first generation of QI's quantum processors consists of a double quantum dot hosted in an in-house grown SiGe/ 28 Si/SiGe heterostructure, and defined with a single layer of Al gates.  ...  The number of use cases in the realm of quantum simulations, optimization and machine learning is steadily growing.  ... 
doi:10.1117/12.2551853 fatcat:4gmo7mho5ncf7m6exgqdqwtzl4

The 2020 Magnetism Roadmap

Elena Y Vedmedenko, Roland Kenji Kawakami, Denis Sheka, Pietro Gambardella, Andrei Kirilyuk, Atsufumi Hirohata, Christian Binek, Oksana A Chubykalo-Fesenko, Stefano Sanvito, Brian Kirby, Julie Grollier, Karin Everschor-Sitte (+3 others)
2020 Journal of Physics D: Applied Physics  
Following the success and relevance of the 2014 and 2017 Magnetism Roadmap articles, this 2020 Magnetism Roadmap edition takes yet another timely look at newly relevant and highly active areas in magnetism  ...  The overall layout of this article is unchanged, given that it has proved the most appropriate way to convey the most relevant aspects of today's magnetism research in a wide variety of sub-fields to a  ...  relation to other computational paradigms and machine learning algorithms.  ... 
doi:10.1088/1361-6463/ab9d98 fatcat:mxxso2zyajbrfoefmk6a6wkxhi

Colloquium: Advances in automation of quantum dot devices control [article]

Justyna P. Zwolak, Jacob M. Taylor
2022 arXiv   pre-print
Further integration of theoretical, computational, and experimental efforts with computer science and ML holds tremendous potential in advancing semiconductor and other platforms for quantum computing.  ...  learning (ML) techniques.  ...  As a result, there has been great interest in exploring the potential of machine learning (ML) to solve the autotuning problem.  ... 
arXiv:2112.09362v2 fatcat:oc2vwjrptjf2rkwo4yghqwpqb4
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