scholarly journals QUANTUM COMPUTING

2021 ◽  
Vol 28 (1) ◽  
Author(s):  
Titu-Marius I. Băjenescu ◽  
Keyword(s):  
Quantum Mechanics ◽  
Quantum Computing ◽  
Computer Science ◽  
Quantum Computer ◽  
Quantum Bits ◽  
Data Object ◽  
Quantum Phenomena ◽  
Classical Computing ◽  
Scientific Fields ◽  
Conventional Computer

The quantum computer, is a "supercomputer" that relies on the phenomena of quantum mechanics to perform operations on data. Object of suppositions, sometimes farfetched, quantum mechanics gave birth to the quantum computer, a machine capable of processing data tens of millions of times faster than a conventional computer. A quantum computer doesn't use the same memory as a conventional computer. Rather than a sequence of 0 and 1, it works with qubits or quantum bits. The quantum computer is a combination of two major scientific fields: quantum mechanics and computer science. Quantum mechanics, on which this computer is based, governs the movement of bodies in the atomic, molecular and corpuscular domains, is a theory whose logic is totally contrary to intuition and it is essential to use mathematics to fully grasp it. Quantum computing is the sub-domain of computer science that deals with quantum computers using quantum mechanical phenomena, as opposed to those of electricity exclusively, for so-called "classical" computing. The quantum phenomena used are quantum entanglement and superposition. The article examines some aspects related to the development, operation, advantages and difficulties, applications and future of the quantum computer.

Quantum Computing Memoir: Quantum Mechanics, Architectural Developments and its Future

2021 ◽  
Vol 9 (8) ◽  
pp. 2198-2200
Author(s):  
Abhay Patil
Keyword(s):  
Quantum Mechanics ◽  
Quantum Computing ◽  
Quantum Theory ◽  
Computer Science ◽  
Physical Science ◽  
Quantum Computer ◽  
Energy Utilization ◽  
Quantum Computers ◽  
Edge Method ◽  
Limited Scope

Abstract: Quantum computing is a cutting edge method of computing that depends on the study of quantum mechanics and its staggering marvels. It is an excellent blend of physical science, arithmetic, computer science and data hypothesis. It gives high computational force, less energy utilization and remarkable speed over old-style computers by controlling the conduct of little actual articles for example minuscule particles like iotas, electrons, photons, and so forth Here, we present a prologue to the crucial ideas and a few thoughts of quantum computing. To comprehend the true abilities and difficulties of a pragmatic quantum computer that can be dispatched financially, the paper covers the engineering, equipment, programming, plan, types and calculations that are explicitly needed by quantum computers. It reveals the ability of quantum computers that can affect our lives in different perspectives like network safety, traffic enhancement, medications, man-made reasoning and some more. Limited scope quantum computers are being grown as of late. This improvement is going towards an incredible future because of their high possible abilities and headways in continuous exploration. Prior to zeroing in on the meanings of a broadly useful quantum computer and investigating the force of the new emerging innovation, it is smarter to survey the beginning, possibilities, and restrictions of the current conventional computing. This data helps us in understanding the potential difficulties in creating outlandish and serious innovation. It will likewise give us an understanding of the continuous advancement in this field. Keywords: Realtime Systems, Programming Processors, Quantum Theory, Quantum Computing

scholarly journals Machine Learning: A Quantum Perspective

2021 ◽  
Author(s):  
Aishwarya Jhanwar ◽  
Manisha J. Nene
Keyword(s):  
Machine Learning ◽  
Quantum Mechanics ◽  
Quantum Computing ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Quantum Computers ◽  
Learning Tasks ◽  
Current Scenario ◽  
Chip Fabrication ◽  
Classical Computing

Recently, increased availability of the data has led to advances in the field of machine learning. Despite of the growth in the domain of machine learning, the proximity to the physical limits of chip fabrication in classical computing is motivating researchers to explore the properties of quantum computing. Since quantum computers leverages the properties of quantum mechanics, it carries the ability to surpass classical computers in machine learning tasks. The study in this paper contributes in enabling researchers to understand how quantum computers can bring a paradigm shift in the field of machine learning. This paper addresses the concepts of quantum computing which influences machine learning in a quantum world. It also states the speedup observed in different machine learning algorithms when executed on quantum computers. The paper towards the end advocates the use of quantum application software and throw light on the existing challenges faced by quantum computers in the current scenario.

scholarly journals Quantum Information: A Brief Overview and Some Mathematical Aspects

Mathematics ◽  
2018 ◽  
Vol 6 (12) ◽  
pp. 273
Author(s):  
Maurice Kibler
Keyword(s):  
Quantum Mechanics ◽  
Quantum Computing ◽  
Quantum Information ◽  
Quantum Information Processing ◽  
Short Review ◽  
Mutually Unbiased Bases ◽  
Quantum Bits ◽  
Mathematical Problems ◽  
Classical Information Theory ◽  
Main Ideas

The aim of the present paper is twofold. First, to give the main ideas behind quantum computing and quantum information, a field based on quantum-mechanical phenomena. Therefore, a short review is devoted to (i) quantum bits or qubits (and more generally qudits), the analogues of the usual bits 0 and 1 of the classical information theory, and to (ii) two characteristics of quantum mechanics, namely, linearity, which manifests itself through the superposition of qubits and the action of unitary operators on qubits, and entanglement of certain multi-qubit states, a resource that is specific to quantum mechanics. A, second, focus is on some mathematical problems related to the so-called mutually unbiased bases used in quantum computing and quantum information processing. In this direction, the construction of mutually unbiased bases is presented via two distinct approaches: one based on the group SU(2) and the other on Galois fields and Galois rings.

scholarly journals Quantum computing, postselection, and probabilistic polynomial-time

2005 ◽  
Vol 461 (2063) ◽  
pp. 3473-3482 ◽  
Author(s):  
Scott Aaronson
Keyword(s):  
Quantum Mechanics ◽  
Quantum Computing ◽  
Polynomial Time ◽  
Quantum Computer ◽  
Complexity Class ◽  
Complete Problems ◽  
Probabilistic Polynomial Time

I study the class of problems efficiently solvable by a quantum computer, given the ability to ‘postselect’ on the outcomes of measurements. I prove that this class coincides with a classical complexity class called PP, or probabilistic polynomial-time. Using this result, I show that several simple changes to the axioms of quantum mechanics would let us solve PP-complete problems efficiently. The result also implies, as an easy corollary, a celebrated theorem of Beigel, Reingold and Spielman that PP is closed under intersection, as well as a generalization of that theorem due to Fortnow and Reingold. This illustrates that quantum computing can yield new and simpler proofs of major results about classical computation.

scholarly journals Implementation of Shor’s Quantum Factoring Algorithm using ProjectQ Framework

2019 ◽  
Vol 8 (6S3) ◽  
pp. 52-56
Keyword(s):  
Quantum Computing ◽  
Computer Science ◽  
Polynomial Time ◽  
Quantum Computer ◽  
Quantum Algorithm ◽  
Prime Factorization ◽  
Shor's Algorithm ◽  
Computer Simulators ◽  
Testing And Evaluation ◽  
Factoring Algorithm

Prime number factorization is a problem in computer science where the solution to that problem takes super-polynomial time classically. Shor’s quantum factoring algorithm is able to solve the problem in polynomial time by harnessing the power of quantum computing. The implementation of the quantum algorithm itself is not detailed by Shor in his paper. In this paper, an approach and experiment to implement Shor’s quantum factoring algorithm are proposed. The implementation is done using Python and a quantum computer simulator from ProjectQ. The testing and evaluation are completed in two computers with different hardware specifications. User time of the implementation is measured in comparison with other quantum computer simulators: ProjectQ and Quantum Computing Playground. This comparison was done to show the performance of Shor’s algorithm when simulated using different hardware. There is a 33% improvement in the execution time (user time) between the two computers with the accuracy of prime factorization in this implementation is inversely proportional to the number of qubits used. Further improvements upon the program that has been developed for this paper is its accuracy in terms of finding the factors of a number and the number of qubits used, as previously mentioned.

scholarly journals Quantum Computing in the Biomedical Sciences; A Brief Introduction into Concepts and Applications

2019 ◽  
Vol 12 (3) ◽  
pp. 104
Author(s):  
Casper van der Kerk ◽  
Attila Csala ◽  
Aeilko H. Zwinderman
Keyword(s):  
Image Processing ◽  
Quantum Computing ◽  
Quantum Computer ◽  
Quantum Algorithms ◽  
Quantum Computers ◽  
Biomedical Sciences ◽  
Quantum Bits ◽  
Early Stages ◽  
Made In

Quantum computing is a field that aims to exploit the principles of superposition and entanglement to perform computations. By using quantum bits (qubits) a quantum computer is able to perform certain tasks more efficiently when compared to classical computers. While applied quantum computing is still in its early stages, quantum algorithms on simulated quantum computers have already been applied to certain problems in epidemics modeling and image processing. Furthermore, companies like Google and IBM continue to develop new quantum computers with an increasing number of qubits. While much progress has been made in the recent years, the so called ”quantum supremacy”has not yet been achieved, and quantum computing appears to be still unsuitable for most applications in biomedical sciences.

scholarly journals Programming gate-based hardware quantum computers for music

Muzikologija ◽  
2018 ◽  
pp. 21-37
Author(s):  
Alexis Kirke
Keyword(s):  
Quantum Mechanics ◽  
Quantum Computing ◽  
Quantum Computation ◽  
Computer Music ◽  
Quantum Computer ◽  
Quantum Computers ◽  
Future Contribution ◽  
Computer Algorithms ◽  
Quantum Processes ◽  
D Wave

There have been significant attempts previously to use the equations of quantum mechanics for generating sound, and to sonify simulated quantum processes. For new forms of computation to be utilized in computer music, eventually hardware must be utilized. This has rarely happened with quantum computer music. One reason for this is that it is currently not easy to get access to such hardware. A second is that the hardware available requires some understanding of quantum computing theory. This paper moves forward the process by utilizing two hardware quantum computation systems: IBMQASM v1.1 and a D-Wave 2X. It also introduces the ideas behind the gate-based IBM system, in a way hopefully more accessible to computerliterate readers. This is a presentation of the first hybrid quantum computer algorithm, involving two hardware machines. Although neither of these algorithms explicitly utilize the promised quantum speed-ups, they are a vital first step in introducing QC to the musical field. The article also introduces some key quantum computer algorithms and discusses their possible future contribution to computer music.

scholarly journals Quantum Image Processing: Opportunities and Challenges

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yue Ruan ◽  
Xiling Xue ◽  
Yuanxia Shen
Keyword(s):  
Image Processing ◽  
Quantum Mechanics ◽  
Quantum Computing ◽  
Quantum Computer ◽  
Quantum Image Processing ◽  
Quantum Image ◽  
Intermediate Scale ◽  
Classical Image ◽  
Image Format ◽  
Significant Opportunity

Quantum image processing (QIP) is a research branch of quantum information and quantum computing. It studies how to take advantage of quantum mechanics’ properties to represent images in a quantum computer and then, based on that image format, implement various image operations. Due to the quantum parallel computing derived from quantum state superposition and entanglement, QIP has natural advantages over classical image processing. But some related works misuse the notion of quantum superiority and mislead the research of QIP, which leads to a big controversy. In this paper, after describing this field’s research status, we list and analyze the doubts about QIP and argue “quantum image classification and recognition” would be the most significant opportunity to exhibit the real quantum superiority. We present the reasons for this judgment and dwell on the challenges for this opportunity in the era of NISQ (Noisy Intermediate-Scale Quantum).

scholarly journals QUANTUM-INSPIRED ARTIFICIL NEURAL NETWORKS AND EVOLUTIONARY ALGORITHMS METHODS APPLIED TO MODELING OF THE POLISH ELECTRIC POWER EXCHANGE USING THE DAY-AHEAD MARKET DATA

2018 ◽  
Vol 7 (3) ◽  
pp. 201-212
Author(s):  
Jerzy Tchórzewski ◽  
Dariusz Ruciński
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Quantum Computing ◽  
Evolutionary Algorithms ◽  
Evolutionary Algorithm ◽  
Computer Science ◽  
Electric Power ◽  
Quantum Computer ◽  
Neural Model ◽  
Power Exchange

The paper presents selected results of research on the use of artificial intelligence methods, which are inspired by quantum computing solutions for modelling of electric power exchange systems. Methods used in the modelling of quantum data acquisition, quantization and dequantization of information as well as the methods of performing quantum computations were emphasized. Furthermore, we have analysed the results obtained for the neural model and for the evolutionary algorithm inspired by the quantum computer science. Eventually, the model was verified on the example of the neural model of the Electric Power Exchange (EPE).

scholarly journals Past, Present and Future of Quantum Computing: A Systematic Study

2021 ◽  
pp. 351-363
Author(s):  
Palash Dutta Banik ◽  
Asoke Nath
Keyword(s):  
Quantum Computing ◽  
Low Temperature ◽  
Systematic Study ◽  
Quantum Physics ◽  
Quantum Computer ◽  
Quantum Computers ◽  
Huge Number ◽  
Classical Computing ◽  
Probabilistic Nature ◽  
Near Future

Quantum Computing is relatively new and it's kind of a special type of computing that uses the laws of quantum physics. In classical computing, we have some limitations and we can overcome those with the help of Quantum Computing as it uses qubits, but we need to keep those qubits at low temperature. Quantum Computing uses the probabilistic nature of electrons. The power of a quantum computer increases exponentially with the number of qubits linked tougher. Quantum computers are very difficult to make but there are a huge number of calculations that can be done easily with the use of a quantum computer. Quantum computers are way better and faster than classical computers. So, we can say that quantum computers rather than quantum computing will be used in the near future to replace classical computing.

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