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Gao X, Erhard M, Zeilinger A, Krenn M. Computer-Inspired Concept for High-Dimensional Multipartite Quantum Gates. PHYSICAL REVIEW LETTERS 2020. [PMID: 32794870 DOI: 10.1038/s42254-020-0230-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
An open question in quantum optics is how to manipulate and control complex quantum states in an experimentally feasible way. Here we present concepts for transformations of high-dimensional multiphotonic quantum systems. The proposals rely on two new ideas: (i) a novel high-dimensional quantum nondemolition measurement, (ii) the encoding and decoding of the entire quantum transformation in an ancillary state for sharing the necessary quantum information between the involved parties. Many solutions can readily be performed in laboratories around the world and thereby we identify important pathways for experimental research in the near future. The concepts have been found using the computer algorithm melvin for designing computer-inspired quantum experiments. As opposed to the field of machine learning, here the human learns new scientific concepts by interpreting and analyzing the results presented by the machine. This demonstrates that computer algorithms can inspire new ideas in science, which has a widely unexplored potential that goes far beyond experimental quantum information science.
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Affiliation(s)
- Xiaoqin Gao
- Faculty of Physics, University of Vienna, Vienna, 1190, Austria
- Institute for Quantum Optics and Quantum Information (IQOQI) Vienna, Austrian Academy of Sciences, Vienna, 1190, Austria
- National Mobile Communications Research Laboratory and Quantum Information Research Center, Southeast University, Nanjing, 210096, China
| | - Manuel Erhard
- Faculty of Physics, University of Vienna, Vienna, 1190, Austria
- Institute for Quantum Optics and Quantum Information (IQOQI) Vienna, Austrian Academy of Sciences, Vienna, 1190, Austria
| | - Anton Zeilinger
- Faculty of Physics, University of Vienna, Vienna, 1190, Austria
- Institute for Quantum Optics and Quantum Information (IQOQI) Vienna, Austrian Academy of Sciences, Vienna, 1190, Austria
| | - Mario Krenn
- Faculty of Physics, University of Vienna, Vienna, 1190, Austria
- Institute for Quantum Optics and Quantum Information (IQOQI) Vienna, Austrian Academy of Sciences, Vienna, 1190, Austria
- Department of Chemistry and Computer Science, University of Toronto, Toronto, Ontario M5S 3G4, Canada
- Vector Institute for Artificial Intelligence, Toronto, Ontario M5G 1M1, Canada
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Multiqubit and multilevel quantum reinforcement learning with quantum technologies. PLoS One 2018; 13:e0200455. [PMID: 30024914 PMCID: PMC6053154 DOI: 10.1371/journal.pone.0200455] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Accepted: 06/25/2018] [Indexed: 11/19/2022] Open
Abstract
We propose a protocol to perform quantum reinforcement learning with quantum technologies. At variance with recent results on quantum reinforcement learning with superconducting circuits, in our current protocol coherent feedback during the learning process is not required, enabling its implementation in a wide variety of quantum systems. We consider diverse possible scenarios for an agent, an environment, and a register that connects them, involving multiqubit and multilevel systems, as well as open-system dynamics. We finally propose possible implementations of this protocol in trapped ions and superconducting circuits. The field of quantum reinforcement learning with quantum technologies will enable enhanced quantum control, as well as more efficient machine learning calculations.
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Dunjko V, Briegel HJ. Machine learning & artificial intelligence in the quantum domain: a review of recent progress. REPORTS ON PROGRESS IN PHYSICS. PHYSICAL SOCIETY (GREAT BRITAIN) 2018; 81:074001. [PMID: 29504942 DOI: 10.1088/1361-6633/aab406] [Citation(s) in RCA: 112] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Quantum information technologies, on the one hand, and intelligent learning systems, on the other, are both emergent technologies that are likely to have a transformative impact on our society in the future. The respective underlying fields of basic research-quantum information versus machine learning (ML) and artificial intelligence (AI)-have their own specific questions and challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been probing the question of the extent to which these fields can indeed learn and benefit from each other. Quantum ML explores the interaction between quantum computing and ML, investigating how results and techniques from one field can be used to solve the problems of the other. Recently we have witnessed significant breakthroughs in both directions of influence. For instance, quantum computing is finding a vital application in providing speed-ups for ML problems, critical in our 'big data' world. Conversely, ML already permeates many cutting-edge technologies and may become instrumental in advanced quantum technologies. Aside from quantum speed-up in data analysis, or classical ML optimization used in quantum experiments, quantum enhancements have also been (theoretically) demonstrated for interactive learning tasks, highlighting the potential of quantum-enhanced learning agents. Finally, works exploring the use of AI for the very design of quantum experiments and for performing parts of genuine research autonomously, have reported their first successes. Beyond the topics of mutual enhancement-exploring what ML/AI can do for quantum physics and vice versa-researchers have also broached the fundamental issue of quantum generalizations of learning and AI concepts. This deals with questions of the very meaning of learning and intelligence in a world that is fully described by quantum mechanics. In this review, we describe the main ideas, recent developments and progress in a broad spectrum of research investigating ML and AI in the quantum domain.
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Affiliation(s)
- Vedran Dunjko
- Institute for Theoretical Physics, University of Innsbruck, Innsbruck 6020, Austria. Max Planck Institute of Quantum Optics, Garching 85748, Germany
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Ebler D, Salek S, Chiribella G. Enhanced Communication with the Assistance of Indefinite Causal Order. PHYSICAL REVIEW LETTERS 2018; 120:120502. [PMID: 29694084 DOI: 10.1103/physrevlett.120.120502] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Indexed: 06/08/2023]
Abstract
In quantum Shannon theory, the way information is encoded and decoded takes advantage of the laws of quantum mechanics, while the way communication channels are interlinked is assumed to be classical. In this Letter, we relax the assumption that quantum channels are combined classically, showing that a quantum communication network where quantum channels are combined in a superposition of different orders can achieve tasks that are impossible in conventional quantum Shannon theory. In particular, we show that two identical copies of a completely depolarizing channel become able to transmit information when they are combined in a quantum superposition of two alternative orders. This finding runs counter to the intuition that if two communication channels are identical, using them in different orders should not make any difference. The failure of such intuition stems from the fact that a single noisy channel can be a random mixture of elementary, noncommuting processes, whose order (or lack thereof) can affect the ability to transmit information.
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Affiliation(s)
- Daniel Ebler
- Department of Computer Science, The University of Hong Kong, Pokfulam Road, Pokfulam 999077, Hong Kong
- HKU Shenzhen Institute of Research and Innovation, Kejizhong 2nd Road, Shenzhen 518057, China
| | - Sina Salek
- Department of Computer Science, The University of Hong Kong, Pokfulam Road, Pokfulam 999077, Hong Kong
| | - Giulio Chiribella
- Department of Computer Science, University of Oxford, Wolfson Building, Parks Road, Oxford OX1 3QD, United Kingdom
- Canadian Institute for Advanced Research, CIFAR Program in Quantum Information Science, Toronto, Ontario M5G 1Z8, Canada
- HKU Shenzhen Institute of Research and Innovation, Kejizhong 2nd Road, Shenzhen 518057, China
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Melnikov AA, Poulsen Nautrup H, Krenn M, Dunjko V, Tiersch M, Zeilinger A, Briegel HJ. Active learning machine learns to create new quantum experiments. Proc Natl Acad Sci U S A 2018; 115:1221-1226. [PMID: 29348200 PMCID: PMC5819408 DOI: 10.1073/pnas.1714936115] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
How useful can machine learning be in a quantum laboratory? Here we raise the question of the potential of intelligent machines in the context of scientific research. A major motivation for the present work is the unknown reachability of various entanglement classes in quantum experiments. We investigate this question by using the projective simulation model, a physics-oriented approach to artificial intelligence. In our approach, the projective simulation system is challenged to design complex photonic quantum experiments that produce high-dimensional entangled multiphoton states, which are of high interest in modern quantum experiments. The artificial intelligence system learns to create a variety of entangled states and improves the efficiency of their realization. In the process, the system autonomously (re)discovers experimental techniques which are only now becoming standard in modern quantum optical experiments-a trait which was not explicitly demanded from the system but emerged through the process of learning. Such features highlight the possibility that machines could have a significantly more creative role in future research.
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Affiliation(s)
- Alexey A Melnikov
- Institute for Theoretical Physics, University of Innsbruck, 6020 Innsbruck, Austria;
| | | | - Mario Krenn
- Vienna Center for Quantum Science and Technology, Faculty of Physics, University of Vienna, 1090 Vienna, Austria
- Institute for Quantum Optics and Quantum Information, Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Vedran Dunjko
- Institute for Theoretical Physics, University of Innsbruck, 6020 Innsbruck, Austria
| | - Markus Tiersch
- Institute for Theoretical Physics, University of Innsbruck, 6020 Innsbruck, Austria
| | - Anton Zeilinger
- Vienna Center for Quantum Science and Technology, Faculty of Physics, University of Vienna, 1090 Vienna, Austria;
- Institute for Quantum Optics and Quantum Information, Austrian Academy of Sciences, 1090 Vienna, Austria
| | - Hans J Briegel
- Institute for Theoretical Physics, University of Innsbruck, 6020 Innsbruck, Austria
- Department of Philosophy, University of Konstanz, 78457 Konstanz, Germany
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6
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Melnikov AA, Makmal A, Dunjko V, Briegel HJ. Projective simulation with generalization. Sci Rep 2017; 7:14430. [PMID: 29089575 PMCID: PMC5663920 DOI: 10.1038/s41598-017-14740-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2017] [Accepted: 10/16/2017] [Indexed: 11/09/2022] Open
Abstract
The ability to generalize is an important feature of any intelligent agent. Not only because it may allow the agent to cope with large amounts of data, but also because in some environments, an agent with no generalization capabilities cannot learn. In this work we outline several criteria for generalization, and present a dynamic and autonomous machinery that enables projective simulation agents to meaningfully generalize. Projective simulation, a novel, physical approach to artificial intelligence, was recently shown to perform well in standard reinforcement learning problems, with applications in advanced robotics as well as quantum experiments. Both the basic projective simulation model and the presented generalization machinery are based on very simple principles. This allows us to provide a full analytical analysis of the agent's performance and to illustrate the benefit the agent gains by generalizing. Specifically, we show that already in basic (but extreme) environments, learning without generalization may be impossible, and demonstrate how the presented generalization machinery enables the projective simulation agent to learn.
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Affiliation(s)
- Alexey A Melnikov
- Institute for Theoretical Physics, University of Innsbruck, Technikerstraße 21a, 6020, Innsbruck, Austria. .,Institute for Quantum Optics and Quantum Information, Austrian Academy of Sciences, Technikerstraße 21a, 6020, Innsbruck, Austria.
| | - Adi Makmal
- Institute for Theoretical Physics, University of Innsbruck, Technikerstraße 21a, 6020, Innsbruck, Austria
| | - Vedran Dunjko
- Institute for Theoretical Physics, University of Innsbruck, Technikerstraße 21a, 6020, Innsbruck, Austria
| | - Hans J Briegel
- Institute for Theoretical Physics, University of Innsbruck, Technikerstraße 21a, 6020, Innsbruck, Austria.,Department of Philosophy, University of Konstanz, Fach 17, 78457, Konstanz, Germany
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Lamata L. Basic protocols in quantum reinforcement learning with superconducting circuits. Sci Rep 2017; 7:1609. [PMID: 28487535 PMCID: PMC5431677 DOI: 10.1038/s41598-017-01711-6] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Accepted: 04/03/2017] [Indexed: 11/08/2022] Open
Abstract
Superconducting circuit technologies have recently achieved quantum protocols involving closed feedback loops. Quantum artificial intelligence and quantum machine learning are emerging fields inside quantum technologies which may enable quantum devices to acquire information from the outer world and improve themselves via a learning process. Here we propose the implementation of basic protocols in quantum reinforcement learning, with superconducting circuits employing feedback- loop control. We introduce diverse scenarios for proof-of-principle experiments with state-of-the-art superconducting circuit technologies and analyze their feasibility in presence of imperfections. The field of quantum artificial intelligence implemented with superconducting circuits paves the way for enhanced quantum control and quantum computation protocols.
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Affiliation(s)
- Lucas Lamata
- Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, 48080, Bilbao, Spain.
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8
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Learning robust pulses for generating universal quantum gates. Sci Rep 2016; 6:36090. [PMID: 27782219 PMCID: PMC5080597 DOI: 10.1038/srep36090] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2016] [Accepted: 10/10/2016] [Indexed: 11/08/2022] Open
Abstract
Constructing a set of universal quantum gates is a fundamental task for quantum computation. The existence of noises, disturbances and fluctuations is unavoidable during the process of implementing quantum gates for most practical quantum systems. This paper employs a sampling-based learning method to find robust control pulses for generating a set of universal quantum gates. Numerical results show that the learned robust control fields are insensitive to disturbances, uncertainties and fluctuations during the process of realizing universal quantum gates.
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9
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Quantum walks of interacting fermions on a cycle graph. Sci Rep 2016; 6:34226. [PMID: 27681057 PMCID: PMC5041091 DOI: 10.1038/srep34226] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2016] [Accepted: 09/07/2016] [Indexed: 11/29/2022] Open
Abstract
Quantum walks have been employed widely to develop new tools for quantum information processing recently. A natural quantum walk dynamics of interacting particles can be used to implement efficiently the universal quantum computation. In this work quantum walks of electrons on a graph are studied. The graph is composed of semiconductor quantum dots arranged in a circle. Electrons can tunnel between adjacent dots and interact via Coulomb repulsion, which leads to entanglement. Fermionic entanglement dynamics is obtained and evaluated.
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