I. Sakamoto · AIP conference proceedings · 2004
Recently, the quantum computer (QC) using the nano‐devices have significantly attracted attention, because a large‐scale extention of the qubits could be easily realized in the nano‐devices. However, some problems for the realization of the QC with nano‐devices arise from the short decoherence time and the interaction of qubits only between nearest‐neighbor qubits. Therefore, we try to design the optimal quantum circuit of the quantum Fourier transform in various network system by means of the genetic algorithm (GA).
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Jae-Hee Kim, Taewan Kim, Kyunghyun Baek et al. · Physica Scripta · 2024
Abstract Efficient encoding of classical data into quantum state—currently referred to as quantum encoding—holds crucial significance in quantum computation. For finite-size databases and qubit registers, a common strategy of the quantum encoding entails establishing a classical mapping that correlates machine-recognizable data addresses with qubit indices that are subsequently superposed. Herein, the most imperative lies in casting an algorithm for generating the superposition of any given number of qubit indices. This algorithm is formally known as quantum superposing algorithm. In this work, we present an efficient quantum superposing algorithm, affirming its effectiveness and superior computational performance in a practical quantum encoding scenario. Our theoretical and numerical analyses demonstrate a substantial enhancement in computational efficiency compared to existing algorithms. Notably, our algorithm has a maximum of 2n − 3 controlled-not (CNOT) counts, representing the most optimized result to date.
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Müssig, Daniel, Lässig, Jörg · Fraunhofer-Publica (Fraunhofer-Gesellschaft) · 2022
Quantum computing is an exciting field, which brought out many competitors, both on the hardware and algorithmic side. Although users are not expecting to outperform classical machines yet, they are still interested in quantum computers, which can demonstrate advantages of their algorithms best. However, for users, especially in the industry, it is not easy to find a quantum device, which fits their needs. This problem arises from an unmanageable amount of meta information about qubits and connections between them such as fidelity, error rates and timings. In this paper we propose a web tool, which helps the user to find quantum computers best suited for their problem or quantum algorithm. Further, it will generate necessary graphs and reports for benchmark studies, which can be directly included in papers of scientists.
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Sean Hallgren · Encyclopedia of Algorithms · 2015
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Moussa, Jonathan E. · ArXiv.org · 2025
I generalize the well-known classical Metropolis-Hastings algorithm into a quantum algorithm that can equilibrate, measure, and mix a quantum thermal state on a quantum computer. It performs non-symmetric transitions on labels of state preparation and measurement operations and rejects transitions using imprecise energies extracted by Gaussian-filtered quantum phase estimation.
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Ullah Khan, Sumsam · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2019
Quantum algorithms are being extensively researched nowadays seeing thepotential of providing exponential speed up when compared to classical algorithmexecution. This speed-up can play a big role in machine learning wheretraining a model is usually very slow. Training a machine learning model requiresmanipulating large vectors and quantum computers inherently are reallyfast at manipulating and computing large vectors and tensor products.However, current quantum computers have certain limitations with respect toqubit’s coherence times and noise. These barriers reduce their effectiveness insolving problems with high accuracy. In pursuit of having better results on currentterm noisy quantum computers, implementations of quantum algorithmswith simpler circuits are desired. To address this problem, three different novelmethods of the quantum version of the K-means clustering algorithm are presentedwith optimized shallow depth quantum circuit design. Experimentalresults on the quantum computer IBMQX2 are demonstrated which show asignificant improvement in the accuracy of the quantum K-means algorithm.
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Sumsam Ullah Khan · 2019
Quantum algorithms are being extensively researched nowadays seeing thepotential of providing exponential speed up when compared to classical algorithmexecution. This speed-up can play a big role i ...
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Guihua Zeng, C. Saavedra, Christoph H. Keitel · ArXiv.org · 2002
A cryptographic algorithm is proposed based on fully quantum mechanical keys and ciphers. Encryption and decryption are carried out via an appropriate measurement process on entangled states as governed by a quantum mechanical, asymmetrical and dynamical public key distribution. The use of public keys leads to a high availability of our scheme, while their quantum nature is shown to ensure unconditional security of the proposed algorithm.
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Yehuda Roth · Journal of Modern Physics · 2013
The collapse phenomenon, the parallelism principle and states correlation are used to define a type of a Grover rapid search engine. In our approach, the observer’s query and the Grover-unsorted-data are stored in different memories where the global state is represented by a tensor product of the associated states. In the proposed formalism, each query-state input activates an adjusted operator that implements the unsorted state in an appropriate 2-D Grover representation. It will be shown that once the representation is set, it takes mainly two operations to complete the whole query search. This seems to be a very efficient search algorithm.
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Giulio Barcaroli · 2024
Function that implements the Quantum Genetic Algorithm, first proposed by Han and Kim in 2000. This is an R implementation of the 'python' application developed by Lahoz-Beltra (<<a href="https://github.com/ResearchCodesHub/QuantumGeneticAlgorithms" target="_top">https://github.com/ResearchCodesHub/QuantumGeneticAlgorithms</a>>). Each optimization problem is represented as a maximization one, where each solution is a sequence of (qu)bits. Following the quantum paradigm, these qubits are in a superposition state: when measuring them, they collapse in a 0 or 1 state. After measurement, the fitness of the solution is calculated as in usual genetic algorithms. The evolution at each iteration is oriented by the application of two quantum gates to the amplitudes of the qubits: (1) a rotation gate (always); (2) a Pauli-X gate (optionally). The rotation is based on the theta angle values: higher values allow a quicker evolution, and lower values avoid local maxima. The Pauli-X gate is equivalent to the classical mutation operator and determines the swap between alfa and beta amplitudes of a given qubit. The package has been developed in such a way as to permit a complete separation between the engine, and the particular problem subject to combinatorial optimization.
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Rafael Pereira da Silva · SSRN Electronic Journal · 2024
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Lahoz-Beltra, Rafael · Figshare · 2016
Quantum Genetic Algorithm (QGA) is a QGA that can be used for the purposes of education and research. QGA is applied in a simple optimization problem:<br><br>Let f(x)=abs(x-5/2+sin(x)) be a function that takes values in the range 0<=x<=15. Within this range f(x) has a maximum value at x=11 (binary value is equal to 1011).
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Samuel J. Lomonaco · Proceedings of symposia in applied mathematics · 2002
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Wong, Renata · Zenodo (CERN European Organization for Nuclear Research) · 2023
Quantum algorithm for the maximum cut problem for arbitrary graphs v1.0.0 as described in paper "Quantum speedup for the maximum cut problem", W-L. Chang, R. Wong, W-Y. Chung, Y-H. Chen, J-C. Chen, and A.V. Vasilaoks (2023). <strong>Full Changelog</strong>: https://github.com/renatawong/quantum_maxcut/compare/v.1.0.0...v.2.0.0
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Perkowski, Marek, Biamonte, Jacob · PDXScholar (Portland State University) · 2005
Current processes validation methods rely on diverse input states and exponential applications of state tomography. Through generalization of classical test theory exceptions to this rule are found. Instead of expanding a complete operator basis to validate a process, the objective is to utilize quantum effects making each gate realized in the process act on a complete set of characteristic states and next extract functional information. Random noise, systematic errors, initialization inaccuracies and measurement faults must also be detected. This concept is applied to the switching class comprising the search oracle. In a first approach, the test set cardinality is held constant to six; both testability and added depth complexity of an additional ”design-for-test” circuit are related to the function realized in the oracle. Oracles realizing affine functions are shown to generate no net entanglement and are thus the easiest to test, where oracles realizing bent functions are the most difficult to test. A second approach replaces extraction complexity with a linear growth in experiment count. An interesting corollary of this study is the success found when addressing the classical test problem quantum mechanically. The validation of all classical degrees of freedom in a quantum switching network were found to necessitate exponentially fewer averaged observables than the number of tests in the classical lower bound.
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Ken Loo · ArXiv.org · 2002
Previously, Bennet and Feynman asked if Heisenberg's uncertainty principle puts a limitation on a quantum computer (Quantum Mechanical Computers, Richard P. Feynman, Foundations of Physics, Vol. 16, No. 6, p597-531, 1986). Feynman's answer was negative. In this paper, we will revisit the same question for the discrete time Fourier transform uncertainty principle. We will show that the discrete time Fourier transform uncertainty principle plays a fundamental role in showing that Shor's type of quantum algorithms has efficient running time and conclude that the discrete time uncertainty principle is an aid in our current formulation and understanding of Shor's type of quantum algorithms. It turns out that for these algorithms, the probability of measuring an element in some set $T$ (at the end of the algorithm) can be written in terms of the time-limiting and band-limiting operators from finite Fourier analysis. Associated with these operators is the finite Fourier transform uncertainty principle. The uncertainty principle provides a lower bound for the above probability. We will derive lower bounds for these types of probabilities in general. We will call these lower bounds quantum algorithm uncertainty principles or QAUP. QAUP are important because they give us some sense of the probability of measuring something desirable. We will use these lower bounds to derive Shor's factoring and discrete log algorithms.
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Sean Hallgren · Encyclopedia of Algorithms · 2016
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Assifuah, Nathaniel · 2024
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Lahoz-Beltra Rafael · Figshare · 2016
Quantum Genetic Algorithm (QGA) is a QGA that can be used for the purposes of education and research. QGA is applied in a simple optimization problem:<br><br>Let f(x)=abs(x-5/2+sin(x)) be a function that takes values in the range 0<=x<=15. Within this range f(x) has a maximum value at x=11 (binary value is equal to 1011).
Cited by 1Open access
Rana Fareed Ghani · Engineering and Technology Journal · 2010
Simulated annealing (SA) has been considered as a good tool for searchand optimization problems which represent the abstraction of obtaining thecrystalline structure through a physical process. This algorithm works sequentiallythat the current state will produce only one next state. That will make the search tobe slower and the important drawback is that the search may fall in local minimumwhich represent the best solution in only part of the solution space. In this workwe present the transformation of Simulated Annealing algorithm into quantumversion which will be called Quantum Simulated Annealing (QSA). Thisalgorithm will overcome the drawbacks of slowness and local minimum falling byproduce as much as possible of the neighbor states and work on in parallel byexploiting the massive parallelism feature in quantum computation. The resultsshow that QSA can find the optimal path in smaller number of iterations than thesequential simulated annealing algorithm and the time complexity of QSA isbetter than any other parallel simulated annealing algorithm.
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