scholarly journals Quantum Computing for Finance: State-of-the-Art and Future Prospects

2020 ◽  
Vol 1 ◽  
pp. 1-24
Author(s):  
Daniel J. Egger ◽  
Claudio Gambella ◽  
Jakub Marecek ◽  
Scott McFaddin ◽  
Martin Mevissen ◽  
...  
Keyword(s):  
Quantum Computing ◽  
State Of The Art ◽  
Future Prospects

Biological Production of (S)-acetoin: A State-of-the-Art Review

2019 ◽  
Vol 19 (25) ◽  
pp. 2348-2356 ◽  
Author(s):  
Neng-Zhong Xie ◽  
Jian-Xiu Li ◽  
Ri-Bo Huang
Keyword(s):  
Side Effects ◽  
Chemical Synthesis ◽  
State Of The Art ◽  
Optically Active ◽  
Considerable Progress ◽  
Future Prospects ◽  
Chiral Compound ◽  
Biological Production ◽  
Acetoin Production ◽  
Made In

Acetoin is an important four-carbon compound that has many applications in foods, chemical synthesis, cosmetics, cigarettes, soaps, and detergents. Its stereoisomer (S)-acetoin, a high-value chiral compound, can also be used to synthesize optically active drugs, which could enhance targeting properties and reduce side effects. Recently, considerable progress has been made in the development of biotechnological routes for (S)-acetoin production. In this review, various strategies for biological (S)- acetoin production are summarized, and their constraints and possible solutions are described. Furthermore, future prospects of biological production of (S)-acetoin are discussed.

ChemInform Abstract: Hydrothermal Liquefaction (HTL) of Microalgae for Biofuel Production: State of the Art Review and Future Prospects

ChemInform ◽  
2014 ◽  
Vol 45 (16) ◽  
pp. no-no
Author(s):  
Diego Lopez Barreiro ◽  
Wolter Prins ◽  
Frederik Ronsse ◽  
Wim Brilman
Keyword(s):  
State Of The Art ◽  
Hydrothermal Liquefaction ◽  
Biofuel Production ◽  
Future Prospects

Efficient reversible quantum design of sig-magnitude to two's complement converters

2020 ◽  
Vol 20 (9&10) ◽  
pp. 747-765
Author(s):  
F. Orts ◽  
G. Ortega ◽  
E.M. E.M. Garzon
Keyword(s):  
Quantum Computing ◽  
Scientific Community ◽  
Quantum Computer ◽  
Fault Tolerant ◽  
State Of The Art ◽  
The Other ◽  
Quantum Computers ◽  
Binary Number ◽  
Quantum Cost ◽  
The One

Despite the great interest that the scientific community has in quantum computing, the scarcity and high cost of resources prevent to advance in this field. Specifically, qubits are very expensive to build, causing the few available quantum computers are tremendously limited in their number of qubits and delaying their progress. This work presents new reversible circuits that optimize the necessary resources for the conversion of a sign binary number into two's complement of N digits. The benefits of our work are two: on the one hand, the proposed two's complement converters are fault tolerant circuits and also are more efficient in terms of resources (essentially, quantum cost, number of qubits, and T-count) than the described in the literature. On the other hand, valuable information about available converters and, what is more, quantum adders, is summarized in tables for interested researchers. The converters have been measured using robust metrics and have been compared with the state-of-the-art circuits. The code to build them in a real quantum computer is given.

scholarly journals Gaussian Elimination versus Greedy Methods for the Synthesis of Linear Reversible Circuits

2021 ◽  
Vol 2 (3) ◽  
pp. 1-26
Author(s):  
Timothée Goubault De Brugière ◽  
Marc Baboulin ◽  
Benoît Valiron ◽  
Simon Martiel ◽  
Cyril Allouche
Keyword(s):  
Quantum Computing ◽  
State Of The Art ◽  
Gaussian Elimination ◽  
Random Operators ◽  
Lu Factorization ◽  
Large Problem ◽  
Computational Costs ◽  
Elimination Algorithm ◽  
Reversible Circuits ◽  
Greedy Methods

Linear reversible circuits represent a subclass of reversible circuits with many applications in quantum computing. These circuits can be efficiently simulated by classical computers and their size is polynomially bounded by the number of qubits, making them a good candidate to deploy efficient methods to reduce computational costs. We propose a new algorithm for synthesizing any linear reversible operator by using an optimized version of the Gaussian elimination algorithm coupled with a tuned LU factorization. We also improve the scalability of purely greedy methods. Overall, on random operators, our algorithms improve the state-of-the-art methods for specific ranges of problem sizes: The custom Gaussian elimination algorithm provides the best results for large problem sizes (n > 150), while the purely greedy methods provide quasi optimal results when n < 30. On a benchmark of reversible functions, we manage to significantly reduce the CNOT count and the depth of the circuit while keeping other metrics of importance (T-count, T-depth) as low as possible.

scholarly journals Retinopathy of prematurity and retinoblastoma: materials of the scientific and practical conference

2019 ◽  
Vol 12 (3) ◽  
pp. 58-75
Author(s):  
Artiсle Editorial
Keyword(s):  
Retinopathy Of Prematurity ◽  
State Of The Art ◽  
Eye Diseases ◽  
Great Success ◽  
Controversial Issues ◽  
Pediatric Ophthalmology ◽  
Future Prospects ◽  
National Medical ◽  
International Participation ◽  
Practical Conference

On April 4–5, 2019, the Russian Helmholtz National Medical Research Center of Eye Diseases held a scientific and practical conference with international participation «Retinopathy of prematurity and retinoblastoma 2019». The conference, which became a platform for discussion of the most acute and burning issues of pediatric ophthalmology, was a great success. The proceedings reflect the state-of-the-art in the treatment of infants and children with retinopathy of prematurity and retinoblastoma, new technical and methodological solutions, controversial issues, and future prospects.

scholarly journals Demand Prediction with Machine Learning Models; State of the Art and a Systematic Review of Advances

2019 ◽  
Author(s):  
Amir Mosavi ◽  
Sina Faizollahzadeh Ardabili ◽  
Shahabodin Shamshirband
Keyword(s):  
Machine Learning ◽  
Energy Production ◽  
Production Management ◽  
Prediction Models ◽  
State Of The Art ◽  
Electricity Demand ◽  
Original Research ◽  
Review Of The Literature ◽  
Future Prospects ◽  
Demand Prediction

Electricity demand prediction is vital for energy production management and proper exploitation of the present resources. Recently, several novel machine learning (ML) models have been employed for electricity demand prediction to estimate the future prospects of the energy requirements. The main objective of this study is to review the various ML models applied for electricity demand prediction. Through a novel search and taxonomy, the most relevant original research articles in the field are identified and further classified according to the ML modeling technique, perdition type, and the application area. A comprehensive review of the literature identifies the major ML models, their applications and a discussion on the evaluation of their performance. This paper further makes a discussion on the trend and the performance of the ML models. As the result, this research reports an outstanding rise in the accuracy, robustness, precision and the generalization ability of the prediction models using the hybrid and ensemble ML algorithms.

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