A Future with Quantum Machine Learning

DeBenedictis, Erik P.

doi:10.1109/mc.2018.1451646

Cited by 38 publications

(30 citation statements)

References 3 publications

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“…Complementing this analysis, Shaydulin et al (2018) have conducted a first performance comparison of these two leading paradigms, showing that quantum local search approach with both frameworks can achieve results comparable to state-of-the-art local search using classical computing architectures, with a potential for the quantum approaches to outperform the classical systems as hardware evolves. However, the time frame for realizing such potential has been estimated by some analysts to lie 10 or more years in the future (Reedy, 2017;Debenedictis, 2019).…”

Section: Y = 2588mentioning

confidence: 99%

Quantum Bridge Analytics I: a tutorial on formulating and using QUBO models

Glover

Kochenberger²,

Du³

2019

4OR-Q J Oper Res

201

139

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The Quadratic Unconstrained Binary Optimization (QUBO) model has gained prominence in recent years with the discovery that it unifies a rich variety of combinatorial optimization problems. By its association with the Ising problem in physics, the QUBO model has emerged as an underpinning of the quantum computing area known as quantum annealing and Fujitsu's digital annealing, and has become a subject of study in neuromorphic computing. Through these connections, QUBO models lie at the heart of experimentation carried out with quantum computers developed by D-Wave Systems and neuromorphic computers developed by IBM. The consequences of these new computational technologies and their links to QUBO models are being explored in initiatives by organizations such as Google, Amazon and Lockheed Martin in the commercial realm and Los Alamos National Laboratory, Oak Ridge National Laboratory, Lawrence Livermore National Laboratory and NASA's Ames Research Center in the public sector. Computational experience is being amassed by both the classical and the quantum computing communities that highlights not only the potential of the QUBO model but also its effectiveness as an alternative to traditional modeling and solution methodologies.

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Section: Y = 2588mentioning

confidence: 99%

Quantum Bridge Analytics I: a tutorial on formulating and using QUBO models

Glover

Kochenberger²,

Du³

2019

4OR-Q J Oper Res

201

139

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“…Therefore we hereafter, call this standard approach "multiple-preparation QIP." 4 These copies may, e.g., be obtained successively from the same system, by using the "repeated write/read" procedure [40]- [42]. The above type of measurement is first performed for each of these copies.…”

Section: Appendix B Multiple-preparation Qipmentioning

confidence: 99%

New Single-Preparation Methods for Unsupervised Quantum Machine Learning Problems

Deville

2021

IEEE Trans. Quantum Eng.

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The term "machine learning" especially refers to algorithms that derive mappings, i.e., inputoutput transforms, by using numerical data that provide information about considered transforms. These transforms appear in many problems related to classification/clustering, regression, system identification, system inversion, and input signal restoration/separation. We here analyze the connections between all these problems in the classical and quantum frameworks. We then focus on their most challenging versions, involving quantum data and/or quantum processing means, and unsupervised, i.e., blind, learning. We consider the general single-preparation quantum information processing (SIPQIP) framework that we have recently proposed. It involves methods that can work with a single instance of each (unknown) state, whereas usual methods are quite cumbersome because they have to very accurately create many copies of each (known) state. In our previous papers, we applied the SIPQIP approach to only one task [blind quantum process tomography (BQPT)], but it opens the way to a large range of other types of methods. In this article, we, therefore, propose various new SIPQIP methods that efficiently perform tasks related to system identification (blind Hamiltonian parameter estimation (BHPE), blind quantum channel identification/estimation, and blind phase estimation), system inversion and state estimation (blind quantum source separation (BQSS), blind quantum entangled state restoration (BQSR), and blind quantum channel equalization), and classification. Numerical tests show that our SIPQIP framework, moreover, yields much more accurate estimation than the usual multiple-preparation approach. Our methods are especially useful in a quantum computer, which we propose to more briefly call a "quamputer": BQPT and BHPE simplify the characterization of quamputer gates; BQSS and BQSR yield quantum gates that may be used to compensate for the nonidealities that alter states stored in quantum registers and open the way to very general self-adaptive quantum gates.INDEX TERMS Blind Hamiltonian parameter estimation (BHPE), blind quantum process tomography (BQPT), blind quantum source separation (BQSS), blind quantum state restoration (BQSR), blind/unsupervised quantum machine learning, Heisenberg exchange coupling, quantum classification, quantum computer (quamputer), single-preparation quantum information processing (SIPQIP), spin-based qubits. Engineering uantum Transactions on IEEEDeville and Deville: NEW SINGLE-PREPARATION METHODS FOR UNSUPERVISED (QM) LEARNING PROBLEMS

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“…This new technology faces challenges and difficulties that have not yet been solved. In fact, the main problems that limit QC are overheating due to the amount of operations and processes used in the code, and the problem of error tolerance, [7], [8]. This becomes critical because is needed to iterate an exponential number of times the factorization of a number of n bits.…”

Section: Theme Developmentmentioning

confidence: 99%

State of art in quantum computing (QC) with open source platforms

Cárdenas-Martínez¹,

Laverde-Forero²,

Castang-Montiel³

2018

Vis. Electron.

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The idea behind the QC paradigm lies in the information storage capacity in amplitude values that use systems based on Qubits or quantum bits -not in bits-, and in the processing of it when transformations are required to change these amplitudes in a precise and controlled way. Therefore, the description of the states in a computer evolves obeying different algorithms than the known ones: correcting errors and digitizing arbitrarily precise calculations through limited resources. The present documentary research, carried out by the Orion research group in 2018, establishes a state of the art of knowledge in QC oriented to education as a baseline for future research whose development platforms are open source kind. A methodology based on indexes is established to categorize and subcategorize the QC, as are: fundamentals and background, QC history, concept and operation, companies that implement the QC field, applications, platforms that are managed in QC, architecture, and finally the programming languages. Source databases such as IEEE-Xplore, EBSCO, and websites, were used to illustrate the fundamental concepts and developments that companies have specified in applications using this new paradigm.

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A Future with Quantum Machine Learning

Cited by 38 publications

References 3 publications

Quantum Bridge Analytics I: a tutorial on formulating and using QUBO models

Quantum Bridge Analytics I: a tutorial on formulating and using QUBO models

New Single-Preparation Methods for Unsupervised Quantum Machine Learning Problems

State of art in quantum computing (QC) with open source platforms

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