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Umer MJ, Sharif MI. A Comprehensive Survey on Quantum Machine Learning and Possible Applications. INTERNATIONAL JOURNAL OF E-HEALTH AND MEDICAL COMMUNICATIONS 2022. [DOI: 10.4018/ijehmc.315730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Machine learning is a branch of artificial intelligence that is being used at a large scale to solve science, engineering, and medical tasks. Quantum computing is an emerging technology that has a very high computational ability to solve complex problems. Classical machine learning with traditional systems has some limitations for problem-solving due to a large amount of data availability. Quantum machine learning has high performance and computational ability that can effectively be used to solve computation tasks. This study reviews the latest articles in quantum computing and quantum machine learning. Building blocks of quantum computing and different flavors of quantum algorithms are also discussed. The latest work in quantum neural networks is also presented. In the end, different possible applications of quantum computing are also discussed.
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2
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Elliott TJ, Yang C, Binder FC, Garner AJP, Thompson J, Gu M. Extreme Dimensionality Reduction with Quantum Modeling. PHYSICAL REVIEW LETTERS 2020; 125:260501. [PMID: 33449713 DOI: 10.1103/physrevlett.125.260501] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Accepted: 10/23/2020] [Indexed: 05/23/2023]
Abstract
Effective and efficient forecasting relies on identification of the relevant information contained in past observations-the predictive features-and isolating it from the rest. When the future of a process bears a strong dependence on its behavior far into the past, there are many such features to store, necessitating complex models with extensive memories. Here, we highlight a family of stochastic processes whose minimal classical models must devote unboundedly many bits to tracking the past. For this family, we identify quantum models of equal accuracy that can store all relevant information within a single two-dimensional quantum system (qubit). This represents the ultimate limit of quantum compression and highlights an immense practical advantage of quantum technologies for the forecasting and simulation of complex systems.
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Affiliation(s)
- Thomas J Elliott
- Department of Mathematics, Imperial College London, London SW7 2AZ, United Kingdom
- Complexity Institute, Nanyang Technological University, Singapore 637335, Singapore
- Nanyang Quantum Hub, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore
| | - Chengran Yang
- Complexity Institute, Nanyang Technological University, Singapore 637335, Singapore
- Nanyang Quantum Hub, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore
| | - Felix C Binder
- Institute for Quantum Optics and Quantum Information, Austrian Academy of Sciences, Boltzmanngasse 3, Vienna 1090, Austria
| | - Andrew J P Garner
- Institute for Quantum Optics and Quantum Information, Austrian Academy of Sciences, Boltzmanngasse 3, Vienna 1090, Austria
- Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, Singapore 117543, Singapore
| | - Jayne Thompson
- Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, Singapore 117543, Singapore
| | - Mile Gu
- Complexity Institute, Nanyang Technological University, Singapore 637335, Singapore
- Nanyang Quantum Hub, School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore
- Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, Singapore 117543, Singapore
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3
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Hassanzadeh P. Towards the quantum-enabled technologies for development of drugs or delivery systems. J Control Release 2020; 324:260-279. [DOI: 10.1016/j.jconrel.2020.04.050] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Revised: 04/28/2020] [Accepted: 04/29/2020] [Indexed: 12/20/2022]
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4
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Loomis SP, Crutchfield JP. Thermal Efficiency of Quantum Memory Compression. PHYSICAL REVIEW LETTERS 2020; 125:020601. [PMID: 32701316 DOI: 10.1103/physrevlett.125.020601] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2019] [Accepted: 06/08/2020] [Indexed: 05/23/2023]
Abstract
Quantum coherence allows for reduced-memory simulators of classical processes. Using recent results in single-shot quantum thermodynamics, we derive a minimal work cost rate for quantum simulators that is quasistatically attainable in the limit of asymptotically infinite parallel simulation. Comparing this cost with the classical regime reveals that quantizing classical simulators not only results in memory compression but also in reduced dissipation. We explore this advantage across a suite of representative examples.
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Affiliation(s)
- Samuel P Loomis
- Complexity Sciences Center and Physics Department, University of California at Davis, One Shields Avenue, Davis, California 95616, USA
| | - James P Crutchfield
- Complexity Sciences Center and Physics Department, University of California at Davis, One Shields Avenue, Davis, California 95616, USA
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Ghafari F, Tischler N, Di Franco C, Thompson J, Gu M, Pryde GJ. Interfering trajectories in experimental quantum-enhanced stochastic simulation. Nat Commun 2019; 10:1630. [PMID: 30967533 PMCID: PMC6456595 DOI: 10.1038/s41467-019-08951-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Accepted: 02/04/2019] [Indexed: 12/02/2022] Open
Abstract
Simulations of stochastic processes play an important role in the quantitative sciences, enabling the characterisation of complex systems. Recent work has established a quantum advantage in stochastic simulation, leading to quantum devices that execute a simulation using less memory than possible by classical means. To realise this advantage it is essential that the memory register remains coherent, and coherently interacts with the processor, allowing the simulator to operate over many time steps. Here we report a multi-time-step experimental simulation of a stochastic process using less memory than the classical limit. A key feature of the photonic quantum information processor is that it creates a quantum superposition of all possible future trajectories that the system can evolve into. This superposition allows us to introduce, and demonstrate, the idea of comparing statistical futures of two classical processes via quantum interference. We demonstrate interference of two 16-dimensional quantum states, representing statistical futures of our process, with a visibility of 0.96 ± 0.02. Quantum devices should allow simulating stochastic processes using less memory than classical counterparts, but only if quantum coherence is maintained through multiple steps. Here, the authors demonstrate a coherence-preserving three-step stochastic simulation using photons.
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Affiliation(s)
- Farzad Ghafari
- Centre for Quantum Dynamics, Griffith University, Brisbane, QLD, 4111, Australia.
| | - Nora Tischler
- Centre for Quantum Dynamics, Griffith University, Brisbane, QLD, 4111, Australia
| | - Carlo Di Franco
- School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, 639673, Singapore.,Complexity Institute, Nanyang Technological University, Singapore, 639673, Singapore
| | - Jayne Thompson
- Centre for Quantum Technologies, National University of Singapore, Singapore, 117543, Singapore
| | - Mile Gu
- School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore, 639673, Singapore. .,Complexity Institute, Nanyang Technological University, Singapore, 639673, Singapore. .,Centre for Quantum Technologies, National University of Singapore, Singapore, 117543, Singapore.
| | - Geoff J Pryde
- Centre for Quantum Dynamics, Griffith University, Brisbane, QLD, 4111, Australia.
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6
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Yang C, Binder FC, Narasimhachar V, Gu M. Matrix Product States for Quantum Stochastic Modeling. PHYSICAL REVIEW LETTERS 2018; 121:260602. [PMID: 30636154 DOI: 10.1103/physrevlett.121.260602] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Revised: 08/10/2018] [Indexed: 06/09/2023]
Abstract
In stochastic modeling, there has been a significant effort towards finding predictive models that predict a stochastic process' future using minimal information from its past. Meanwhile, in condensed matter physics, matrix product states (MPS) are known as a particularly efficient representation of 1D spin chains. In this Letter, we associate each stochastic process with a suitable quantum state of a spin chain. We then show that the optimal predictive model for the process leads directly to an MPS representation of the associated quantum state. Conversely, MPS methods offer a systematic construction of the best known quantum predictive models. This connection allows an improved method for computing the quantum memory needed for generating optimal predictions. We prove that this memory coincides with the entanglement of the associated spin chain across the past-future bipartition.
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Affiliation(s)
- Chengran Yang
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371 Singapore, Singapore
- Complexity institute, Nanyang Technological University, 639798 Singapore, Singapore
| | - Felix C Binder
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371 Singapore, Singapore
- Complexity institute, Nanyang Technological University, 639798 Singapore, Singapore
| | - Varun Narasimhachar
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371 Singapore, Singapore
- Complexity institute, Nanyang Technological University, 639798 Singapore, Singapore
| | - Mile Gu
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371 Singapore, Singapore
- Complexity institute, Nanyang Technological University, 639798 Singapore, Singapore
- Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, 117543 Singapore, Singapore
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7
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Mancino L, Cavina V, De Pasquale A, Sbroscia M, Booth RI, Roccia E, Gianani I, Giovannetti V, Barbieri M. Geometrical Bounds on Irreversibility in Open Quantum Systems. PHYSICAL REVIEW LETTERS 2018; 121:160602. [PMID: 30387653 DOI: 10.1103/physrevlett.121.160602] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2018] [Revised: 08/01/2018] [Indexed: 06/08/2023]
Abstract
The Clausius inequality has deep implications for reversibility and the arrow of time. Quantum theory is able to extend this result for closed systems by inspecting the trajectory of the density matrix on its manifold. Here we show that this approach can provide an upper and lower bound to the irreversible entropy production for open quantum systems as well. These provide insights on how the information on the initial state is forgotten through a thermalization process. Limits of the applicability of our bounds are discussed and demonstrated in a quantum photonic simulator.
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Affiliation(s)
- Luca Mancino
- Dipartimento di Scienze, Università degli Studi Roma Tre, Via della Vasca Navale 84, 00146, Rome, Italy
| | - Vasco Cavina
- NEST, Scuola Normale Superiore and Istituto Nanoscienze-CNR, Piazza dei Cavalieri 7, I-56126, Pisa, Italy
| | - Antonella De Pasquale
- NEST, Scuola Normale Superiore and Istituto Nanoscienze-CNR, Piazza dei Cavalieri 7, I-56126, Pisa, Italy
- Dipartimento di Fisica, Università di Firenze, Via G. Sansone 1, I-50019 Sesto Fiorentino (FI), Italy
- INFN Sezione di Firenze, via G.Sansone 1, I-50019 Sesto Fiorentino (FI), Italy
| | - Marco Sbroscia
- Dipartimento di Scienze, Università degli Studi Roma Tre, Via della Vasca Navale 84, 00146, Rome, Italy
| | - Robert I Booth
- Dipartimento di Scienze, Università degli Studi Roma Tre, Via della Vasca Navale 84, 00146, Rome, Italy
- Institut de Physique, Sorbonne Université, 4 Place Jussieu, 75005, Paris, France
| | - Emanuele Roccia
- Dipartimento di Scienze, Università degli Studi Roma Tre, Via della Vasca Navale 84, 00146, Rome, Italy
| | - Ilaria Gianani
- Dipartimento di Scienze, Università degli Studi Roma Tre, Via della Vasca Navale 84, 00146, Rome, Italy
| | - Vittorio Giovannetti
- NEST, Scuola Normale Superiore and Istituto Nanoscienze-CNR, Piazza dei Cavalieri 7, I-56126, Pisa, Italy
| | - Marco Barbieri
- Dipartimento di Scienze, Università degli Studi Roma Tre, Via della Vasca Navale 84, 00146, Rome, Italy
- Istituto Nazionale di Ottica-CNR, Largo Enrico Fermi 6, 50125, Florence, Italy
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8
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Binder FC, Thompson J, Gu M. Practical Unitary Simulator for Non-Markovian Complex Processes. PHYSICAL REVIEW LETTERS 2018; 120:240502. [PMID: 29956996 DOI: 10.1103/physrevlett.120.240502] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2017] [Revised: 02/09/2018] [Indexed: 06/08/2023]
Abstract
Stochastic processes are as ubiquitous throughout the quantitative sciences as they are notorious for being difficult to simulate and predict. In this Letter, we propose a unitary quantum simulator for discrete-time stochastic processes which requires less internal memory than any classical analogue throughout the simulation. The simulator's internal memory requirements equal those of the best previous quantum models. However, in contrast to previous models, it only requires a (small) finite-dimensional Hilbert space. Moreover, since the simulator operates unitarily throughout, it avoids any unnecessary information loss. We provide a stepwise construction for simulators for a large class of stochastic processes hence directly opening the possibility for experimental implementations with current platforms for quantum computation. The results are illustrated for an example process.
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Affiliation(s)
- Felix C Binder
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371 Singapore, Singapore
- Complexity Institute, Nanyang Technological University, 639673 Singapore, Singapore
| | - Jayne Thompson
- Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, 117543 Singapore, Singapore
| | - Mile Gu
- School of Physical and Mathematical Sciences, Nanyang Technological University, 637371 Singapore, Singapore
- Complexity Institute, Nanyang Technological University, 639673 Singapore, Singapore
- Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, 117543 Singapore, Singapore
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9
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Cabello A, Gu M, Gühne O, Xu ZP. Optimal Classical Simulation of State-Independent Quantum Contextuality. PHYSICAL REVIEW LETTERS 2018; 120:130401. [PMID: 29694184 DOI: 10.1103/physrevlett.120.130401] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 02/06/2018] [Indexed: 06/08/2023]
Abstract
Simulating quantum contextuality with classical systems requires memory. A fundamental yet open question is what is the minimum memory needed and, therefore, the precise sense in which quantum systems outperform classical ones. Here, we make rigorous the notion of classically simulating quantum state-independent contextuality (QSIC) in the case of a single quantum system submitted to an infinite sequence of measurements randomly chosen from a finite QSIC set. We obtain the minimum memory needed to simulate arbitrary QSIC sets via classical systems under the assumption that the simulation should not contain any oracular information. In particular, we show that, while classically simulating two qubits tested with the Peres-Mermin set requires log_{2}24≈4.585 bits, simulating a single qutrit tested with the Yu-Oh set requires, at least, 5.740 bits.
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Affiliation(s)
- Adán Cabello
- Departamento de Física Aplicada II, Universidad de Sevilla, E-41012 Sevilla, Spain
| | - Mile Gu
- School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Singapore
- Complexity Institute, Nanyang Technological University, 18 Nanyang Drive, Singapore 637723, Singapore
- Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, Singapore 117543, Singapore
| | - Otfried Gühne
- Naturwissenschaftlich-Technische Fakultät, Universität Siegen, Walter-Flex-Straße 3, D-57068 Siegen, Germany
| | - Zhen-Peng Xu
- Departamento de Física Aplicada II, Universidad de Sevilla, E-41012 Sevilla, Spain
- Theoretical Physics Division, Chern Institute of Mathematics, Nankai University, Tianjin 300071, People's Republic of China
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10
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Aghamohammadi C, Mahoney JR, Crutchfield JP. Extreme Quantum Advantage when Simulating Classical Systems with Long-Range Interaction. Sci Rep 2017; 7:6735. [PMID: 28751746 PMCID: PMC5532296 DOI: 10.1038/s41598-017-04928-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Accepted: 05/23/2017] [Indexed: 11/12/2022] Open
Abstract
Classical stochastic processes can be generated by quantum simulators instead of the more standard classical ones, such as hidden Markov models. One reason for using quantum simulators has recently come to the fore: they generally require less memory than their classical counterparts. Here, we examine this quantum advantage for strongly coupled spin systems-in particular, the Dyson one-dimensional Ising spin chain with variable interaction length. We find that the advantage scales with both interaction range and temperature, growing without bound as interaction range increases. In particular, simulating Dyson's original spin chain with the most memory-efficient classical algorithm known requires infinite memory, while a quantum simulator requires only finite memory. Thus, quantum systems can very efficiently simulate strongly coupled one-dimensional classical spin systems.
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Affiliation(s)
- Cina Aghamohammadi
- Complexity Sciences Center and Physics Department, University of California at Davis, One Shields Avenue, Davis, CA, 95616, USA.
| | - John R Mahoney
- Complexity Sciences Center and Physics Department, University of California at Davis, One Shields Avenue, Davis, CA, 95616, USA.
| | - James P Crutchfield
- Complexity Sciences Center and Physics Department, University of California at Davis, One Shields Avenue, Davis, CA, 95616, USA.
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