scholarly journals Efficient Construction of Functional Representations for Quantum Algorithms

2021 ◽  
pp. 227-241
Author(s):  
Lukas Burgholzer ◽  
Rudy Raymond ◽  
Indranil Sengupta ◽  
Robert Wille
Keyword(s):  
Quantum Algorithms ◽  
Efficient Construction ◽  
Functional Representations

Efficient Construction of Order-Preserving Encryption Using Pseudo Random Function

2015 ◽  
Vol E98.B (7) ◽  
pp. 1276-1283 ◽  
Author(s):  
Nam-Su JHO ◽  
Ku-Young CHANG ◽  
Do-Won HONG
Keyword(s):  
Random Function ◽  
Efficient Construction

The Essence of Quantum Computing

2018 ◽  
Author(s):  
Rajendra K. Bera
Keyword(s):  
Hilbert Space ◽  
Quantum Computing ◽  
Quantum Physics ◽  
Quantum Algorithms ◽  
Quantum Computers ◽  
Turing Machines ◽  
Classical Physics ◽  
Core Set ◽  
The World ◽  
Subordinate Role

It now appears that quantum computers are poised to enter the world of computing and establish its dominance, especially, in the cloud. Turing machines (classical computers) tied to the laws of classical physics will not vanish from our lives but begin to play a subordinate role to quantum computers tied to the enigmatic laws of quantum physics that deal with such non-intuitive phenomena as superposition, entanglement, collapse of the wave function, and teleportation, all occurring in Hilbert space. The aim of this 3-part paper is to introduce the readers to a core set of quantum algorithms based on the postulates of quantum mechanics, and reveal the amazing power of quantum computing.

Problem of energy efficiency in the model of sustainable development in Ukraine: EU experience

2019 ◽  
pp. 18-23
Author(s):  
Vasyl Zelenko ◽  
Yaryna Ferenchak ◽  
Nataliya Zelenko
Keyword(s):  
Energy Efficiency ◽  
Sustainable Development ◽  
Energy Saving ◽  
Association Agreement ◽  
Climate Conditions ◽  
The Sustainable Development ◽  
Non Profit ◽  
Efficiency Measures ◽  
Efficient Construction

The paper outlines major preconditions of development of energy efficiency programs and the process of their introduction from the perspective of sustainable development concept. Energy saving measures of European Union are examined, its experience and results, like green books and Thermie programs, etc are analyzed. The paper also specifies relevant activities and responsibilities undertaken by Member States by 2030 and the rates planned to be achieved. The experience of neighboring Poland in energy efficiency promotion is analyzed, as far as the country most closely resembles Ukraine by the initial rates in 1990. We calculated conditional losses of Ukraine in 2017 as the gap between the rates of energy efficiency of Ukraine and Poland (similarity of climate conditions and the state of housing funds allowed us to assume that the comparison will be the most correct regarding Ukraine). The results of calculations make us conclude that the funds are used most efficiently if the money designated, for example, for subsidizing of population are directed at strengthening of energy efficiency (at least up to the level of neighboring country). It will result in saving of about 40 billion. This is the amount defined as Ukrainian capacity in terms of improvement of energy saving and possible results of its realization. The paper determines the stage Ukraine is at in introduction of energy efficiency measures and programs: “warm loans” program; conditions regarding energy saving in Ukraine enshrined in the EU-Ukraine Association Agreement; creation of legal basis, namely the Law of Ukraine “On Energy Efficiency Fund” as of 8 June 2017. The role of newly created Energy Efficiency Fund is analyzed. Special attention is paid to the fact that energy efficient construction is one of international liabilities undertaken by Ukraine. The presence of The Active House Alliance at Ukrainian market is an important achievement in this sphere as it is the non-profit association aiming at creation of housing concept corresponding to the sustainable development principles.

SchemEX: Efficient Construction of a Data Catalogue by Stream-Based Indexing of Linked Data

2012 ◽  
Author(s):  
Mathias Konrath ◽  
Thomas Gottron ◽  
Steffen Staab ◽  
Ansgar Scherp
Keyword(s):  
Linked Data ◽  
Efficient Construction

scholarly journals Quantum Algorithms for Mixed Binary Optimization applied to Transaction Settlement

2021 ◽  
pp. 1-1
Author(s):  
Lee Braine ◽  
Daniel Egger ◽  
Jennifer Glick ◽  
Stefan Woerner
Keyword(s):  
Quantum Algorithms ◽  
Binary Optimization

Learning adiabatic quantum algorithms over optimization problems

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Davide Pastorello ◽  
Enrico Blanzieri ◽  
Valter Cavecchia
Keyword(s):  
Optimization Problems ◽  
Quantum Algorithms

scholarly journals Smooth input preparation for quantum and quantum-inspired machine learning

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Zhikuan Zhao ◽  
Jack K. Fitzsimons ◽  
Patrick Rebentrost ◽  
Vedran Dunjko ◽  
Joseph F. Fitzsimons
Keyword(s):  
Machine Learning ◽  
Quantum Algorithms ◽  
Machine Learning Algorithms ◽  
Low Rank ◽  
Smoothed Analysis ◽  
Machine Learning Applications ◽  
Data Points ◽  
Input Model ◽  
The Cost ◽  
Input Perturbation

AbstractMachine learning has recently emerged as a fruitful area for finding potential quantum computational advantage. Many of the quantum-enhanced machine learning algorithms critically hinge upon the ability to efficiently produce states proportional to high-dimensional data points stored in a quantum accessible memory. Even given query access to exponentially many entries stored in a database, the construction of which is considered a one-off overhead, it has been argued that the cost of preparing such amplitude-encoded states may offset any exponential quantum advantage. Here we prove using smoothed analysis that if the data analysis algorithm is robust against small entry-wise input perturbation, state preparation can always be achieved with constant queries. This criterion is typically satisfied in realistic machine learning applications, where input data is subjective to moderate noise. Our results are equally applicable to the recent seminal progress in quantum-inspired algorithms, where specially constructed databases suffice for polylogarithmic classical algorithm in low-rank cases. The consequence of our finding is that for the purpose of practical machine learning, polylogarithmic processing time is possible under a general and flexible input model with quantum algorithms or quantum-inspired classical algorithms in the low-rank cases.

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