1
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Stremoukhov S, Forsh P, Khabarova K, Kolachevsky N. Proposal for Trapped-Ion Quantum Memristor. ENTROPY (BASEL, SWITZERLAND) 2023; 25:1134. [PMID: 37628163 PMCID: PMC10453901 DOI: 10.3390/e25081134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/07/2023] [Accepted: 07/12/2023] [Indexed: 08/27/2023]
Abstract
A quantum memristor combines the memristive dynamics with the quantum behavior of the system. We analyze the idea of a quantum memristor based on ultracold ions trapped in a Paul trap. Corresponding input and output memristor signals are the ion electronic levels populations. We show that under certain conditions the output/input dependence is a hysteresis curve similar to classical memristive devices. This behavior becomes possible due to the partial decoherence provided by the feedback loop, which action depends on previous state of the system (memory). The feedback loop also introduces nonlinearity in the system. Ion-based quantum memristor possesses several advantages comparing to other platforms-photonic and superconducting circuits-due to the presence of a large number of electronic levels with different lifetimes as well as strong Coulomb coupling between ions in the trap. The implementation of the proposed ion-based quantum memristor will be a significant contribution to the novel direction of "quantum neural networks".
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Affiliation(s)
- Sergey Stremoukhov
- P.N. Lebedev Physical Institute of the Russian Academy of Science, Leninskiy Prospect, 53, 119991 Moscow, Russia; (S.S.); (P.F.); (K.K.)
- Faculty of Physics, Lomonosov Moscow State University, Leninskie Gory, 1/2, 119991 Moscow, Russia
- National Research Centre “Kurchatov Institute”, Akademika Kurchatova sq. 1, 123182 Moscow, Russia
| | - Pavel Forsh
- P.N. Lebedev Physical Institute of the Russian Academy of Science, Leninskiy Prospect, 53, 119991 Moscow, Russia; (S.S.); (P.F.); (K.K.)
- Faculty of Physics, Lomonosov Moscow State University, Leninskie Gory, 1/2, 119991 Moscow, Russia
| | - Ksenia Khabarova
- P.N. Lebedev Physical Institute of the Russian Academy of Science, Leninskiy Prospect, 53, 119991 Moscow, Russia; (S.S.); (P.F.); (K.K.)
- Russian Quantum Center, Bolshoy Bulvar, 30 Bld. 1, 121205 Moscow, Russia
| | - Nikolay Kolachevsky
- P.N. Lebedev Physical Institute of the Russian Academy of Science, Leninskiy Prospect, 53, 119991 Moscow, Russia; (S.S.); (P.F.); (K.K.)
- Russian Quantum Center, Bolshoy Bulvar, 30 Bld. 1, 121205 Moscow, Russia
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2
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Tang JL, Alvarado Barrios G, Solano E, Albarrán-Arriagada F. Tunable Non-Markovianity for Bosonic Quantum Memristors. ENTROPY (BASEL, SWITZERLAND) 2023; 25:e25050756. [PMID: 37238511 DOI: 10.3390/e25050756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 03/09/2023] [Accepted: 03/13/2023] [Indexed: 05/28/2023]
Abstract
We studied the tunable control of the non-Markovianity of a bosonic mode due to its coupling to a set of auxiliary qubits, both embedded in a thermal reservoir. Specifically, we considered a single cavity mode coupled to auxiliary qubits described by the Tavis-Cummings model. As a figure of merit, we define the dynamical non-Markovianity as the tendency of a system to return to its initial state, instead of evolving monotonically to its steady state. We studied how this dynamical non-Markovianity can be manipulated in terms of the qubit frequency. We found that the control of the auxiliary systems affects the cavity dynamics as an effective time-dependent decay rate. Finally, we show how this tunable time-dependent decay rate can be tuned to engineer bosonic quantum memristors, involving memory effects that are fundamental for developing neuromorphic quantum technologies.
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Affiliation(s)
- Jia-Liang Tang
- International Center of Quantum Artificial Intelligence for Science and Technology (QuArtist), Physics Department, Shanghai University, Shanghai 200444, China
| | | | - Enrique Solano
- Kipu Quantum, Greifswalderstrasse 226, 10405 Berlin, Germany
| | - Francisco Albarrán-Arriagada
- Departamento de Física, Universidad de Santiago de Chile (USACH), Avenida Víctor Jara 3493, Santiago 9170124, Chile
- Center for the Development of Nanoscience and Nanotechnology, Estación Central 9170124, Chile
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3
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Zhang W, Gao H, Deng C, Lv T, Hu S, Wu H, Xue S, Tao Y, Deng L, Xiong W. An ultrathin memristor based on a two-dimensional WS 2/MoS 2 heterojunction. NANOSCALE 2021; 13:11497-11504. [PMID: 34165120 DOI: 10.1039/d1nr01683k] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Memristors are regarded as one of the key devices to break through the traditional Von Neumann computer architecture due to their capability of simulating the function of neural synapses. Among various memristive materials, two-dimensional (2D) materials are promising candidates to build advanced memristors with extremely high integration density and low power consumption. However, memristors based on 2D materials usually suffer from poor endurance and retention due to their vulnerability to material degradation during the formation/fusing processes of conductive filament channels within the switching media of 2D materials. Here, a new memristor architecture based on a WS2/MoS2 2D semiconducting heterojunction (metal/heterojunction/metal, MHM) is proposed, which is completely different from the conventional metal/insulator/metal (MIM) sandwich structure. Through the introduction of a type-II 2D heterojunction, a resistance switching mechanism based on band modulation rather than the conductive filaments can be realized to eliminate the material degradation during the set/reset processes. A prototype MHM memristor based on the WS2/MoS2 heterojunction is successfully developed with a large switching on/off ratio up to 104 and a clearly extended endurance over 120 switching cycles, showing the advantage of the 2D WS2/MoS2 heterojunction over the individual MoS2 or WS2 layers in memristive performance. The proposed method for the MHM-type 2D memristor has the potential to achieve a large-scale integrated memristor matrix with low power consumption and high integration density, which is promising for future artificial intelligence and brain-like computing systems.
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Affiliation(s)
- Wenguang Zhang
- Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Hui Gao
- Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Chunsan Deng
- Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Ting Lv
- Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Sanlue Hu
- Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Hao Wu
- Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Songyan Xue
- Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Yufeng Tao
- Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China. and Institute of Micro-nano Optoelectronics and Terahertz Technology, Jiangsu University, Zhenjiang, 212013, China
| | - Leimin Deng
- Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Wei Xiong
- Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, China.
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4
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Lamata L. Quantum machine learning and quantum biomimetics: A perspective. MACHINE LEARNING: SCIENCE AND TECHNOLOGY 2020. [DOI: 10.1088/2632-2153/ab9803] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Abstract
Quantum machine learning has emerged as an exciting and promising paradigm inside quantum technologies. It may permit, on the one hand, to carry out more efficient machine learning calculations by means of quantum devices, while, on the other hand, to employ machine learning techniques to better control quantum systems. Inside quantum machine learning, quantum reinforcement learning aims at developing ‘intelligent’ quantum agents that may interact with the outer world and adapt to it, with the strategy of achieving some final goal. Another paradigm inside quantum machine learning is that of quantum autoencoders, which may allow one for employing fewer resources in a quantum device via a training process. Moreover, the field of quantum biomimetics aims at establishing analogies between biological and quantum systems, to look for previously inadvertent connections that may enable useful applications. Two recent examples are the concepts of quantum artificial life, as well as of quantum memristors. In this Perspective, we give an overview of these topics, describing the related research carried out by the scientific community.
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5
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Ding Y, Martín-Guerrero JD, Sanz M, Magdalena-Benedicto R, Chen X, Solano E. Retrieving Quantum Information with Active Learning. PHYSICAL REVIEW LETTERS 2020; 124:140504. [PMID: 32338974 DOI: 10.1103/physrevlett.124.140504] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 03/23/2020] [Accepted: 03/24/2020] [Indexed: 06/11/2023]
Abstract
Active learning is a machine learning method aiming at optimal design for model training. At variance with supervised learning, which labels all samples, active learning provides an improved model by labeling samples with maximal uncertainty according to the estimation model. Here, we propose the use of active learning for efficient quantum information retrieval, which is a crucial task in the design of quantum experiments. Meanwhile, when dealing with large data output, we employ active learning for the sake of classification with minimal cost in fidelity loss. Indeed, labeling only 5% samples, we achieve almost 90% rate estimation. The introduction of active learning methods in the data analysis of quantum experiments will enhance applications of quantum technologies.
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Affiliation(s)
- Yongcheng Ding
- International Center of Quantum Artificial Intelligence for Science and Technology (QuArtist) and Department of Physics, Shanghai University, 200444 Shanghai, China
- Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, 48080 Bilbao, Spain
| | - José D Martín-Guerrero
- IDAL, Electronic Engineering Department, University of Valencia, Avinguda Universitat s/n, 46100 Burjassot, Valencia, Spain
| | - Mikel Sanz
- Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, 48080 Bilbao, Spain
| | - Rafael Magdalena-Benedicto
- IDAL, Electronic Engineering Department, University of Valencia, Avinguda Universitat s/n, 46100 Burjassot, Valencia, Spain
| | - Xi Chen
- International Center of Quantum Artificial Intelligence for Science and Technology (QuArtist) and Department of Physics, Shanghai University, 200444 Shanghai, China
- Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, 48080 Bilbao, Spain
| | - Enrique Solano
- International Center of Quantum Artificial Intelligence for Science and Technology (QuArtist) and Department of Physics, Shanghai University, 200444 Shanghai, China
- Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, 48080 Bilbao, Spain
- IKERBASQUE, Basque Foundation for Science, Maria Diaz de Haro 3, 48013 Bilbao, Spain
- IQM, Munich, Germany
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6
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Quantum Memristors in Frequency-Entangled Optical Fields. MATERIALS 2020; 13:ma13040864. [PMID: 32074986 PMCID: PMC7079656 DOI: 10.3390/ma13040864] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Revised: 01/17/2020] [Accepted: 02/05/2020] [Indexed: 11/17/2022]
Abstract
A quantum memristor is a passive resistive circuit element with memory, engineered in a given quantum platform. It can be represented by a quantum system coupled to a dissipative environment, in which a system-bath coupling is mediated through a weak measurement scheme and classical feedback on the system. In quantum photonics, such a device can be designed from a beam splitter with tunable reflectivity, which is modified depending on the results of measurements in one of the outgoing beams. Here, we show that a similar implementation can be achieved with frequency-entangled optical fields and a frequency mixer that, working similarly to a beam splitter, produces state superpositions. We show that the characteristic hysteretic behavior of memristors can be reproduced when analyzing the response of the system with respect to the control, for different experimentally attainable states. Since memory effects in memristors can be exploited for classical and neuromorphic computation, the results presented in this work could be a building block for constructing quantum neural networks in quantum photonics, when scaling up.
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7
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Silva F, Sanz M, Seixas J, Solano E, Omar Y. Perceptrons from memristors. Neural Netw 2019; 122:273-278. [PMID: 31731044 DOI: 10.1016/j.neunet.2019.10.013] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Revised: 09/26/2019] [Accepted: 10/17/2019] [Indexed: 11/29/2022]
Abstract
Memristors, resistors with memory whose outputs depend on the history of their inputs, have been used with success in neuromorphic architectures, particularly as synapses and non-volatile memories. However, to the best of our knowledge, no model for a network in which both the synapses and the neurons are implemented using memristors has been proposed so far. In the present work we introduce models for single and multilayer perceptrons based exclusively on memristors. We adapt the delta rule to the memristor-based single-layer perceptron and the backpropagation algorithm to the memristor-based multilayer perceptron. Our results show that both perform as expected for perceptrons, including satisfying Minsky-Papert's theorem. As a consequence of the Universal Approximation Theorem, they also show that memristors are universal function approximators. By using memristors for both the neurons and the synapses, our models pave the way for novel memristor-based neural network architectures and algorithms. A neural network based on memristors could show advantages in terms of energy conservation and open up possibilities for other learning systems to be adapted to a memristor-based paradigm, both in the classical and quantum learning realms.
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Affiliation(s)
- Francisco Silva
- Instituto de Telecomunicações, Physics of Information and Quantum Technologies Group, Portugal.
| | - Mikel Sanz
- Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, E-48080 Bilbao, Spain.
| | - João Seixas
- Instituto de Telecomunicações, Physics of Information and Quantum Technologies Group, Portugal; Instituto Superior Técnico, Universidade de Lisboa, Portugal; CeFEMA, Instituto Superior Técnico, Universidade de Lisboa, Portugal; Laboratório de Instrumentação e Física Experimental de Partículas (LIP), Lisbon, Portugal.
| | - Enrique Solano
- Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, E-48080 Bilbao, Spain; IKERBASQUE, Basque Foundation for Science, Maria Diaz de Haro 3, 48013 Bilbao, Spain; International Center of Quantum Artificial Intelligence for Science and Technology (QuArtist), and Department of Physics, Shanghai University, 200444 Shanghai, China.
| | - Yasser Omar
- Instituto de Telecomunicações, Physics of Information and Quantum Technologies Group, Portugal; Instituto Superior Técnico, Universidade de Lisboa, Portugal.
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8
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Barrios GA, Retamal JC, Solano E, Sanz M. Analog simulator of integro-differential equations with classical memristors. Sci Rep 2019; 9:12928. [PMID: 31506446 PMCID: PMC6736973 DOI: 10.1038/s41598-019-49204-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 08/07/2019] [Indexed: 11/09/2022] Open
Abstract
An analog computer makes use of continuously changeable quantities of a system, such as its electrical, mechanical, or hydraulic properties, to solve a given problem. While these devices are usually computationally more powerful than their digital counterparts, they suffer from analog noise which does not allow for error control. We will focus on analog computers based on active electrical networks comprised of resistors, capacitors, and operational amplifiers which are capable of simulating any linear ordinary differential equation. However, the class of nonlinear dynamics they can solve is limited. In this work, by adding memristors to the electrical network, we show that the analog computer can simulate a large variety of linear and nonlinear integro-differential equations by carefully choosing the conductance and the dynamics of the memristor state variable. We study the performance of these analog computers by simulating integro-differential models related to fluid dynamics, nonlinear Volterra equations for population growth, and quantum models describing non-Markovian memory effects, among others. Finally, we perform stability tests by considering imperfect analog components, obtaining robust solutions with up to 13% relative error for relevant timescales.
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Affiliation(s)
- G Alvarado Barrios
- Departamento de Física, Universidad de Santiago de Chile (USACH), Avenida Ecuador 3493, 9170124, Santiago, Chile.
- Center for the Development of Nanoscience and Nanotechnology, Santiago, Chile.
- International Center of Quantum Artificial Intelligence for Science and Technology (QuArtist), and Department of Physics, Shanghai University, 200444, Shanghai, China.
| | - J C Retamal
- Departamento de Física, Universidad de Santiago de Chile (USACH), Avenida Ecuador 3493, 9170124, Santiago, Chile
- Center for the Development of Nanoscience and Nanotechnology, Santiago, Chile
| | - E Solano
- International Center of Quantum Artificial Intelligence for Science and Technology (QuArtist), and Department of Physics, Shanghai University, 200444, Shanghai, China
- Department of Physical Chemistry, University of the Basque Country UPV/EHU, Bilbao, Spain
- IKERBASQUE, Basque Foundation for Science, Maria Diaz de Haro 3, 48013, Bilbao, Spain
| | - M Sanz
- Department of Physical Chemistry, University of the Basque Country UPV/EHU, Bilbao, Spain.
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9
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Abstract
We present the first experimental realization of a quantum artificial life algorithm in a quantum computer. The quantum biomimetic protocol encodes tailored quantum behaviors belonging to living systems, namely, self-replication, mutation, interaction between individuals, and death, into the cloud quantum computer IBM ibmqx4. In this experiment, entanglement spreads throughout generations of individuals, where genuine quantum information features are inherited through genealogical networks. As a pioneering proof-of-principle, experimental data fits the ideal model with accuracy. Thereafter, these and other models of quantum artificial life, for which no classical device may predict its quantum supremacy evolution, can be further explored in novel generations of quantum computers. Quantum biomimetics, quantum machine learning, and quantum artificial intelligence will move forward hand in hand through more elaborate levels of quantum complexity.
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10
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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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11
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Gatti G, Barberena D, Sanz M, Solano E. Protected State Transfer via an Approximate Quantum Adder. Sci Rep 2017; 7:6964. [PMID: 28761133 PMCID: PMC5537370 DOI: 10.1038/s41598-017-06425-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2017] [Accepted: 06/12/2017] [Indexed: 11/13/2022] Open
Abstract
We propose a decoherence protected protocol for sending single photon quantum states through depolarizing channels. This protocol is implemented via an approximate quantum adder engineered through spontaneous parametric down converters, and shows higher success probability than distilled quantum teleportation protocols for distances below a threshold depending on the properties of the channel.
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Affiliation(s)
- G Gatti
- Departamento de Ciencias, Sección Física, Pontificia Universidad Católica del Perú, Apartado, 1761, Lima, Peru
| | - D Barberena
- Departamento de Ciencias, Sección Física, Pontificia Universidad Católica del Perú, Apartado, 1761, Lima, Peru
| | - M Sanz
- Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, E-48080, Bilbao, Spain.
| | - E Solano
- Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, E-48080, Bilbao, Spain.,IKERBASQUE, Basque Foundation for Science, Maria Diaz de Haro 3, 48011, Bilbao, Spain
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12
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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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13
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Abstract
Memristors, memcapacitors, and meminductors represent an innovative generation of circuit elements whose properties depend on the state and history of the system. The hysteretic behavior of one of their constituent variables, is their distinctive fingerprint. This feature endows them with the ability to store and process information on the same physical location, a property that is expected to benefit many applications ranging from unconventional computing to adaptive electronics to robotics. Therefore, it is important to find appropriate memory elements that combine a wide range of memory states, long memory retention times, and protection against unavoidable noise. Although several physical systems belong to the general class of memelements, few of them combine these important physical features in a single component. Here, we demonstrate theoretically a superconducting memory based on solitonic long Josephson junctions. Moreover, since solitons are at the core of its operation, this system provides an intrinsic topological protection against external perturbations. We show that the Josephson critical current behaves hysteretically as an external magnetic field is properly swept. Accordingly, long Josephson junctions can be used as multi-state memories, with a controllable number of available states, and in other emerging areas such as memcomputing, i.e., computing directly in/by the memory.
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14
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Advanced-Retarded Differential Equations in Quantum Photonic Systems. Sci Rep 2017; 7:42933. [PMID: 28230090 PMCID: PMC5322327 DOI: 10.1038/srep42933] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Accepted: 01/16/2017] [Indexed: 11/09/2022] Open
Abstract
We propose the realization of photonic circuits whose dynamics is governed by advanced-retarded differential equations. Beyond their mathematical interest, these photonic configurations enable the implementation of quantum feedback and feedforward without requiring any intermediate measurement. We show how this protocol can be applied to implement interesting delay effects in the quantum regime, as well as in the classical limit. Our results elucidate the potential of the protocol as a promising route towards integrated quantum control systems on a chip.
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15
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Pfeiffer P, Egusquiza IL, Di Ventra M, Sanz M, Solano E. Quantum memristors. Sci Rep 2016; 6:29507. [PMID: 27381511 PMCID: PMC4933948 DOI: 10.1038/srep29507] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Accepted: 06/17/2016] [Indexed: 11/27/2022] Open
Abstract
Technology based on memristors, resistors with memory whose resistance depends on the history of the crossing charges, has lately enhanced the classical paradigm of computation with neuromorphic architectures. However, in contrast to the known quantized models of passive circuit elements, such as inductors, capacitors or resistors, the design and realization of a quantum memristor is still missing. Here, we introduce the concept of a quantum memristor as a quantum dissipative device, whose decoherence mechanism is controlled by a continuous-measurement feedback scheme, which accounts for the memory. Indeed, we provide numerical simulations showing that memory effects actually persist in the quantum regime. Our quantization method, specifically designed for superconducting circuits, may be extended to other quantum platforms, allowing for memristor-type constructions in different quantum technologies. The proposed quantum memristor is then a building block for neuromorphic quantum computation and quantum simulations of non-Markovian systems.
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Affiliation(s)
- P Pfeiffer
- Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, E-48080 Bilbao, Spain
| | - I L Egusquiza
- Department of Theoretical Physics and History of Science, University of the Basque Country UPV/EHU, Apartado 644, E-48080 Bilbao, Spain
| | - M Di Ventra
- Department of Physics, University of California, San Diego, La Jolla, CA 92093, USA
| | - M Sanz
- Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, E-48080 Bilbao, Spain
| | - E Solano
- Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, E-48080 Bilbao, Spain.,IKERBASQUE, Basque Foundation for Science, Maria Diaz de Haro 3, 48013 Bilbao, Spain
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