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.

Identification, Prediction and Data Analysis of Noncoding RNAs: A Review

2019 ◽  
Vol 15 (3) ◽  
pp. 216-230 ◽  
Author(s):  
Abbasali Emamjomeh ◽  
Javad Zahiri ◽  
Mehrdad Asadian ◽  
Mehrdad Behmanesh ◽  
Barat A. Fakheri ◽  
...  
Keyword(s):  
Computational Methods ◽  
State Of The Art ◽  
Noncoding Rnas ◽  
Research Field ◽  
Design Strategies ◽  
Structural Prediction ◽  
Computational Costs ◽  
Cellular Processes ◽  
Laboratory Techniques ◽  
Fast Prediction

Background:Noncoding RNAs (ncRNAs) which play an important role in various cellular processes are important in medicine as well as in drug design strategies. Different studies have shown that ncRNAs are dis-regulated in cancer cells and play an important role in human tumorigenesis. Therefore, it is important to identify and predict such molecules by experimental and computational methods, respectively. However, to avoid expensive experimental methods, computational algorithms have been developed for accurately and fast prediction of ncRNAs.Objective:The aim of this review was to introduce the experimental and computational methods to identify and predict ncRNAs structure. Also, we explained the ncRNA’s roles in cellular processes and drugs design, briefly.Method:In this survey, we will introduce ncRNAs and their roles in biological and medicinal processes. Then, some important laboratory techniques will be studied to identify ncRNAs. Finally, the state-of-the-art models and algorithms will be introduced along with important tools and databases.Results:The results showed that the integration of experimental and computational approaches improves to identify ncRNAs. Moreover, the high accurate databases, algorithms and tools were compared to predict the ncRNAs.Conclusion:ncRNAs prediction is an exciting research field, but there are different difficulties. It requires accurate and reliable algorithms and tools. Also, it should be mentioned that computational costs of such algorithm including running time and usage memory are very important. Finally, some suggestions were presented to improve computational methods of ncRNAs gene and structural prediction.

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

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 On Residual Stress Development, Prevention, and Compensation in Metal Additive Manufacturing

Materials ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 255 ◽  
Author(s):  
Kevin Carpenter ◽  
Ali Tabei
Keyword(s):  
Additive Manufacturing ◽  
Process Parameters ◽  
Mechanical Performance ◽  
State Of The Art ◽  
Measurement Techniques ◽  
Complex Geometries ◽  
Quality Inspection ◽  
Metal Additive Manufacturing ◽  
Computational Costs ◽  
Metal Additive

One of the most appealing qualities of additive manufacturing (AM) is the ability to produce complex geometries faster than most traditional methods. The trade-off for this advantage is that AM parts are extremely vulnerable to residual stresses (RSs), which may lead to geometrical distortions and quality inspection failures. Additionally, tensile RSs negatively impact the fatigue life and other mechanical performance characteristics of the parts in service. Therefore, in order for AM to cross the borders of prototyping toward a viable manufacturing process, the major challenge of RS development must be addressed. Different AM technologies contain many unique features and parameters, which influence the temperature gradients in the part and lead to development of RSs. The stresses formed in AM parts are typically observed to be compressive in the center of the part and tensile on the top layers. To mitigate these stresses, process parameters must be optimized, which requires exhaustive and costly experimentations. Alternative to experiments, holistic computational frameworks which can capture much of the physics while balancing computational costs are introduced for rapid and inexpensive investigation into development and prevention of RSs in AM. In this review, the focus is on metal additive manufacturing, referred to simply as “AM”, and, after a brief introduction to various AM technologies and thermoelastic mechanics, prior works on sources of RSs in AM are discussed. Furthermore, the state-of-the-art knowledge on RS measurement techniques, the influence of AM process parameters, current modeling approaches, and distortion prevention approaches are reported.

Collaborative correlation filters for real-time tracking with spatial constraint

2019 ◽  
Vol 17 (03) ◽  
pp. 1950012
Author(s):  
Lifang Zhou ◽  
Hongmei Li ◽  
Weisheng Li ◽  
Bangjun Lei ◽  
Lu Wang
Keyword(s):  
Adaptive Sampling ◽  
Search Strategy ◽  
State Of The Art ◽  
Computational Cost ◽  
Background Information ◽  
Spatial Constraint ◽  
Correlation Filters ◽  
Computational Costs ◽  
Real Time Tracking ◽  
Selection Factor

Accurate scale estimation of the target plays an important role in object tracking. Most state-of-the-art methods estimate the target size by employing an exhaustive scale search. These methods can achieve high accuracy but suffer significantly from large computational cost. In this paper, we first propose an adaptive scale search strategy with the scale selection factor instead of an exhaustive scale search. This proposed strategy contributes to reducing computational costs by adaptive sampling. Furthermore, the boundary effects of correlation filters are suppressed by investigating background information so that the accuracy of the proposed tracker can be boosted. Experiments’ empirical evaluations of 61 challenging benchmark sequences demonstrate that the overall tracking performance of the proposed tracker is very successfully improved. Moreover, our method obtains the top rank in performance by outperforming 17 state-of-the-art trackers on OTB2013.

A Quantum Approach Using Stochastic Simulation and Schrodinger with Multi-Layer Learning for Finding Features from Dataset

2020 ◽  
Vol 17 (6) ◽  
pp. 2514-2518
Author(s):  
Kapil Prashar ◽  
Rajneesh Talwar ◽  
Chander Kant
Keyword(s):  
Quantum Computing ◽  
Mathematical Models ◽  
Stochastic Simulation ◽  
Statistical Models ◽  
State Of The Art ◽  
Research Work ◽  
Current Approach ◽  
Suggested Model ◽  
Quantum Approach ◽  
Multiple State

Quantum computing relies on the quantity of the mechanical phenomenon, such as interference and overlap. It aims to solve issues which are not realistically possible on computers. The research work introduces the new quantum-based model from a provided dataset for forecasting the infection. This technique is beneficial in describing the association among different statistical models. Our study has resulted in highest precision than ever applied technique, which was differentiated and calculated from the defined dataset and results. Such suggested strategies were evaluated and reviewed against multiple state-of-the-art methods to demonstrate efficacy. The qualitative and graphical results are provided for the verification of the current approach. The suggested model is more robust than existing mathematical models due to the findings.

scholarly journals Non-clausal Redundancy Properties

2021 ◽  
pp. 252-272
Author(s):  
Lee A. Barnett ◽  
Armin Biere
Keyword(s):  
State Of The Art ◽  
Binary Decision Diagrams ◽  
Gaussian Elimination ◽  
Decision Diagrams ◽  
Binary Decision ◽  
Refutation Systems ◽  
Unit Propagation

AbstractState-of-the-art refutation systems for SAT are largely based on the derivation of clauses meeting some redundancy criteria, ensuring their addition to a formula does not alter its satisfiability. However, there are strong propositional reasoning techniques whose inferences are not easily expressed in such systems. This paper extends the redundancy framework beyond clauses to characterize redundancy for Boolean constraints in general. We show this characterization can be instantiated to develop efficiently checkable refutation systems using redundancy properties for Binary Decision Diagrams (BDDs). Using a form of reverse unit propagation over conjunctions of BDDs, these systems capture, for instance, Gaussian elimination reasoning over XOR constraints encoded in a formula, without the need for clausal translations or extension variables. Notably, these systems generalize those based on the strong Propagation Redundancy (PR) property, without an increase in complexity.

scholarly journals TSCDA: A novel greedy approach for community discovery in networks

2021 ◽  
Author(s):  
Arman Ferdowsi ◽  
Alireza Khanteymoori ◽  
Maryam Dehghan Chenary
Keyword(s):  
Detection Method ◽  
State Of The Art ◽  
The Other ◽  
Community Discovery ◽  
New Approach ◽  
Influential Node ◽  
New Community ◽  
Greedy Methods ◽  
Two Stages ◽  
The Impact

In this paper, we introduce a new approach for detecting community structures in networks. The approach is subject to modifying one of the connectivity-based community quality functions based on considering the impact that each community's most influential node has on the other vertices. Utilizing the proposed quality measure, we devise an algorithm that aims to detect high-quality communities of a given network based on two stages: finding a promising initial solution using greedy methods and then refining the solutions in a local search manner. The performance of our algorithm has been evaluated on some standard real-world networks as well as on some artificial networks. The experimental results of the algorithm are reported and compared with several state-of-the-art algorithms. The experiments show that our approach is competitive with the other well-known techniques in the literature and even outperforms them. This approach can be used as a new community detection method in network analysis.

Sign in / Sign up

Export Citation Format

Share Document