scholarly journals Quantum Guarded-Command Language (qGCL) for Maximum Value

d'CARTESIAN ◽  
2017 ◽  
Vol 6 (1) ◽  
pp. 8
Author(s):  
Aisya Putri ◽  
Jullia Titaley ◽  
Benny Pinontoan
Keyword(s):  
Quantum Computer ◽  
Command Language ◽  
Quantum Bit ◽  
Maximum Value ◽  
Computer Calculations ◽  
Classical Computer

On a classical computer or a binary computer, calculations are done simultaneously so as to produce the equations and algorithms. The result of this research shows that to determined maximum value specified in the algorithm using quantum Guarded-Command Language (qGCl) in quantum computer. Initially determine of maximum value was construct in Djikstra’s Guarded-Command Language (GCL) which is then implemented on Zuliani’s probability Guarded-Command Language (pGCL) furthermore applying to quantum Guarded-Command Language (qGCL) for last result. Of concern here is the speed in resolving a problem or calculate problem. Due to the Quantum Computer has a Quantum Bit (qubit) and a phenomenon commonly called superposition. Keywords: GCL, pGCL, qGCL, quantum computer.

The Realization of New Computing Model and System Research Related to Quantum Computer

2014 ◽  
Vol 1078 ◽  
pp. 413-416
Author(s):  
Hai Yan Liu
Keyword(s):  
Quantum Computing ◽  
High Performance ◽  
Quantum Physics ◽  
Quantum Computer ◽  
Quantum Computers ◽  
Information Coding ◽  
Computing Model ◽  
Mechanics Model ◽  
Electronic Spin ◽  
Classical Computer

The ultimate goal of quantum calculation is to build high performance practical quantum computers. With quantum mechanics model of computer information coding and computational principle, it is proved in theory to be able to simulate the classical computer is currently completely, and with more classical computer, quantum computation is one of the most popular fields in physics research in recent ten years, has formed a set of quantum physics, mathematics. This paper to electronic spin doped fullerene quantum aided calculation scheme, we through the comprehensive use of logic based network and based on the overall control of the two kinds of quantum computing model, solve the addressing problem of nuclear spin, avoids the technical difficulties of pre-existing. We expect the final realization of the quantum computer will depend on the integrated use of in a variety of quantum computing model and physical realization system, and our primary work shows this feature..

scholarly journals Elucidating reaction mechanisms on quantum computers

2017 ◽  
Vol 114 (29) ◽  
pp. 7555-7560 ◽  
Author(s):  
Markus Reiher ◽  
Nathan Wiebe ◽  
Krysta M. Svore ◽  
Dave Wecker ◽  
Matthias Troyer
Keyword(s):  
Reaction Mechanisms ◽  
Quantum Computer ◽  
Quantum Error Correction ◽  
Quantum Computers ◽  
Chemical Systems ◽  
Small Quantum ◽  
Quantum Technology ◽  
Classical Computer ◽  
Quantum Error ◽  
Biological Nitrogen

With rapid recent advances in quantum technology, we are close to the threshold of quantum devices whose computational powers can exceed those of classical supercomputers. Here, we show that a quantum computer can be used to elucidate reaction mechanisms in complex chemical systems, using the open problem of biological nitrogen fixation in nitrogenase as an example. We discuss how quantum computers can augment classical computer simulations used to probe these reaction mechanisms, to significantly increase their accuracy and enable hitherto intractable simulations. Our resource estimates show that, even when taking into account the substantial overhead of quantum error correction, and the need to compile into discrete gate sets, the necessary computations can be performed in reasonable time on small quantum computers. Our results demonstrate that quantum computers will be able to tackle important problems in chemistry without requiring exorbitant resources.

TOWARD A PRACTICAL ENVIRONMENT FOR QUANTUM PROGRAMMING

2005 ◽  
Vol 03 (supp01) ◽  
pp. 133-141
Author(s):  
S. YAMASHITA ◽  
M. NAKANISHI ◽  
K. WATANABE
Keyword(s):  
Quantum Computer ◽  
Quantum Algorithms ◽  
The Other ◽  
Quantum Programming ◽  
Grover Search ◽  
Classical Computer

This paper proposes a practical framework for quantum programming. In our framework, the parts of a program to be performed on a quantum computer are almost automatically determined, and the other parts are performed on a classical computer. We only consider Grover Search to be performed on a quantum computer in the framework because the other quantum algorithms known so far cannot be applied to general cases. By considering only Grover Search, we have several advantages that show our framework is really practical.

scholarly journals Quantum Lattice-Gas Models for the Many-Body Schrödinger Equation

1997 ◽  
Vol 08 (04) ◽  
pp. 705-716 ◽  
Author(s):  
Bruce M. Boghosian ◽  
Washington Taylor
Keyword(s):  
Schrödinger Equation ◽  
Arbitrary Number ◽  
Schrodinger Equation ◽  
Lattice Gas ◽  
Quantum Computer ◽  
Many Body ◽  
Lattice Gas Models ◽  
Many Body Systems ◽  
The Many ◽  
Classical Computer

A general class of discrete unitary models are described whose behavior in the continuum limit corresponds to a many-body Schrödinger equation. On a quantum computer, these models could be used to simulate quantum many-body systems with an exponential speedup over analogous simulations on classical computers. On a classical computer, these models give an explicitly unitary and local prescription for discretizing the Schrödinger equation. It is shown that models of this type can be constructed for an arbitrary number of particles moving in an arbitrary number of dimensions with an arbitrary interparticle interaction.

scholarly journals Quantum speedup of training radial basis function networks

2019 ◽  
Vol 19 (7&8) ◽  
pp. 609-625
Author(s):  
Changpeng Shao
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Quantum Computer ◽  
Quantum Algorithms ◽  
Rbf Network ◽  
Training Samples ◽  
Radial Basis ◽  
The Matrix ◽  
Hamiltonian Simulation ◽  
Classical Computer

Radial basis function (RBF) network is a simple but useful neural network model that contains wide applications in machine learning. The training of an RBF network reduces to solve a linear system, which is time consuming classically. Based on HHL algorithm, we propose two quantum algorithms to train RBF networks. To apply the HHL algorithm, we choose using the Hamiltonian simulation algorithm proposed in [P. Rebentrost, A. Steffens, I. Marvian and S. Lloyd, Phys. Rev. A 97, 012327, 2018]. However, to use this result, an oracle to query the entries of the matrix of the network should be constructed. We apply the amplitude estimation technique to build this oracle. The final results indicate that if the centers of the RBF network are the training samples, then the quantum computer achieves exponential speedup at the number and the dimension of training samples over the classical computer; if the centers are determined by the K-means algorithm, then the quantum computer achieves quadratic speedup at the number of samples and exponential speedup at the dimension of samples.

What happens when quantum computing re-defines the assessment of investment risk?

2017 ◽  
Vol 57 (2) ◽  
pp. 486
Author(s):  
Mark Laybourn ◽  
John Pascoe
Keyword(s):  
Quantum Computing ◽  
Oil And Gas ◽  
Quantum Computer ◽  
Oil And Gas Industry ◽  
Cash Flows ◽  
Daily Lives ◽  
Gas Industry ◽  
Market Prices ◽  
Investment Evaluation ◽  
Classical Computer

The dawn of quantum computing is upon us and as the world’s smartest minds determine how the technology will change our daily lives, we consider how it could benefit investors in oil and gas projects to make better decisions. The oil and gas industry relies on investment for its survival and investors expect a return commensurate with the risks of a project. The classical approach to investment evaluation relies on mathematics in which estimated project cash flows are assessed against a cost of capital and an upfront investment. The issue with this approach is the key assumptions which underpin the project cash flow calculations such as reserves, production and market prices are themselves estimates which each introduce a degree of risk. If we analysed the financial models of recent oil and gas developments we would find the key assumptions which underpin the projects would be vastly different to reality. The crystal ball of investment evaluation would benefit from a more powerful way to optimise estimates and assess risk. A quantum computer offers the ability to perform optimisation calculations not possible with classical computers. The theoretical ability to run infinite parallel processes (as opposed to sequential processes in classical computers) can fundamentally change the optimisation of estimates. Google and NASA were recently able to solve a highly specialised computing problem with a quantum computer 100 million times faster than a classical computer. The power to significantly improve estimation optimisations and thereby reduce risk will help investors achieve a higher degree of confidence and should see levels of investment increase.

scholarly journals The philosophical foundations of quantum computer modeling

2019 ◽  
Vol 17 (4) ◽  
pp. 85-92
Author(s):  
Anna Yu. Storozhuk
Keyword(s):  
Quantum Mechanics ◽  
Quantum System ◽  
Quantum Computer ◽  
Internal State ◽  
Sufficient Accuracy ◽  
Reduction Wave ◽  
Philosophical Foundations ◽  
Simulated System ◽  
Classical Computer ◽  
Heisenberg's Uncertainty Principle

The source of some problems of the quantum mechanics is the observer’s influence on the system. In particular, such problems include the reduction wave function, which forces physicists to talk about “hidden parameters” and the incompleteness of quantum mechanics. Measurements of a quantum system violate its internal state and make it impossible to obtain information about its other parameters (Heisenberg’s uncertainty principle). In 1980 there appeared the thesis that since modeling the behavior of a quantum system on a classical computer cannot provide sufficient accuracy for reproducing all its parameters, there is a need for a quantum computer. The question arises: to what degree can a quantum computer help to solve traditional epistemological problems of quantum mechanics? Can modelling the behavior of elementary particles on a quantum computer “bypass” the problem of the observer’s influence on the system? In other words, is it possible to obtain information about the behavior of a quantum system without observation? Will the internal state of the simulated system be preserved?

scholarly journals Unstructured search by random and quantum walk

2022 ◽  
Vol 22 (1&2) ◽  
pp. 53-85
Author(s):  
Thomas G. Wong
Keyword(s):  
Random Walks ◽  
Continuous Time ◽  
Quantum Computer ◽  
Success Probability ◽  
Quantum Walk ◽  
Quantum Walks ◽  
Search Problem ◽  
Large N ◽  
Time Quantum ◽  
Classical Computer

The task of finding an entry in an unsorted list of $N$ elements famously takes $O(N)$ queries to an oracle for a classical computer and $O(\sqrt{N})$ queries for a quantum computer using Grover's algorithm. Reformulated as a spatial search problem, this corresponds to searching the complete graph, or all-to-all network, for a marked vertex by querying an oracle. In this tutorial, we derive how discrete- and continuous-time (classical) random walks and quantum walks solve this problem in a thorough and pedagogical manner, providing an accessible introduction to how random and quantum walks can be used to search spatial regions. Some of the results are already known, but many are new. For large $N$, the random walks converge to the same evolution, both taking $N \ln(1/\epsilon)$ time to reach a success probability of $1-\epsilon$. In contrast, the discrete-time quantum walk asymptotically takes $\pi\sqrt{N}/2\sqrt{2}$ timesteps to reach a success probability of $1/2$, while the continuous-time quantum walk takes $\pi\sqrt{N}/2$ time to reach a success probability of $1$.

scholarly journals Circuit for Shor's algorithm using 2n+3 qubits

2003 ◽  
Vol 3 (2) ◽  
pp. 175-185
Author(s):  
S. Beauregard
Keyword(s):  
Polynomial Time ◽  
Quantum Computer ◽  
Quantum Gates ◽  
Factorization Algorithm ◽  
Shor's Algorithm ◽  
Classical Computer

We try to minimize the number of qubits needed to factor an integer of n bits using Shor's algorithm on a quantum computer. We introduce a circuit which uses 2n+3 qubits and O(n^3 lg(n)) elementary quantum gates in a depth of O(n^3) to implement the factorization algorithm. The circuit is computable in polynomial time on a classical computer and is completely general as it does not rely on any property of the number to be factored.

Challenges in Surface Science for a P-in-Si Quantum Computer — Phosphine Adsorption/Incorporation and Epitaxial Si Encapsulation

2003 ◽  
Vol 10 (02n03) ◽  
pp. 415-423 ◽  
Author(s):  
Lars Oberbeck ◽  
Neil J. Curson ◽  
Steven R. Schofield ◽  
Toby Hallam ◽  
Michelle Y. Simmons ◽  
...  
Keyword(s):  
Low Temperature ◽  
Surface Science ◽  
Defect Density ◽  
Quantum Computer ◽  
Scanning Tunneling ◽  
Epitaxial Silicon ◽  
Tunneling Microscopy ◽  
Phosphorus Segregation ◽  
Quantum Bit ◽  
Quantum Bits

We present three important results relating to the fabrication of a quantum computer in silicon: (i) the interaction of the dopant gas phosphine with Si(001), (ii) a comparison of the morphology of epitaxial Si layers grown on clean and on monohydride-terminated Si(001), and (iii) a direct measure of the segregation/diffusion of incorporated P atoms during Si epitaxial growth and annealing. After low phosphine (PH3) dosing of a Si(001) surface dual bias scanning tunneling microscopy was used to identify the PH x(x = 2, 3) species on the surface. Subsequent annealing to 350°C resulted in the P atom from the PH x molecule being incorporated into the surface to form Si–P heterodimers. The threefold coordination that results from incorporation is expected to be advantageous for phosphorus quantum bit fabrication since it will reduce P segregation and diffusion during Si epitaxial overgrowth. One question to be addressed in the encapsulation process for quantum bits is whether the H resist layer needs to be removed or whether we can grow through the hydrogen layer. We demonstrate that five-monolayer-thick epitaxial Si layers deposited at low temperature (250°C) using molecular beam epitaxy have a significantly lower roughness and defect density when grown on a clean Si(001) surface compared to a H-terminated surface. Attempts to encapsulate phosphorus quantum bits at 260°C and to recover the surface quality of the epitaxial layer resulted in P atoms segregating and diffusing to the surface. These results suggest that the hydrogen layer is desorbed first before the P atoms are encapsulated in epitaxial silicon grown at very low temperature (below 250°C) to minimise phosphorus segregation.

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