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An Overview of Future Applications of Quantum Computing
Published in Thiruselvan Subramanian, Archana Dhyani, Adarsh Kumar, Sukhpal Singh Gill, Artificial Intelligence, Machine Learning and Blockchain in Quantum Satellite, Drone and Network, 2023
Taskeen Zaidi, Bijjahalli Sadanandamurthy Sushma
A quantum computer works on a quantum algorithm. The algorithm contains at least one unique quantum either superposition or entanglement for execution on quantum computers. The quantum circuit and quantum gate are also required. The quantum gate can be proposed on the number of qubits. There may be single qubit or multiqubit gate. The quantum algorithms are reversible and undo all the operations by forward traversing. A few examples of quantum algorithms are mainly Shor’s and Grover’s algorithms. Shor’s algorithm factorized large numbers faster than the classical algorithm, whereas Grover’s algorithm is used for searching unordered list faster than the classical algorithm. Various quantum algorithms like algebraic and number theoretic algorithms, oracular algorithms, simulation algorithms and optimization algorithms are well discussed [19].
The Longer Term: Quantum Information Processing and Communication
Published in Simon Deleonibus, Electronic Device Architectures for the Nano-CMOS Era, 2019
Grover’s algorithm relies upon a very subtle use of interference, now known as amplitude amplification, which performs a stepwise increase of the probability of obtaining the relevant item in the database by means of a measurement, and which brings this probability as close to 1 as possible after N1/2 steps: the quantum query complexity of unordered database search is Θ(N1/2) (Θ(h(N)) denotes the fact that the exact complexity of a problem is of the order of h(N), if N is the size of the input, whereas O(h(N)) tells that h(N) is an upper bound of the complexity). In the case of our telephone directory, Grover’s algorithm finds the correct answer after exactly 103 queries to the quantum oracle Uf, instead of up to 106 queries to the classical oracle f when classical means are used, which represents a quadratic speedup.
The Future of Security
Published in Marcus K. Weldon, The Future X Network, 2018
However, quantum computing presents a direct threat to RSA and other cryptosystems. It makes direct use of quantum-mechanical phenomena, such as superposition and entanglement, to perform operations on data, through the use of quantum bits or qubits. In theory, a quantum computer would be able to perform some computational functions far faster than conventional digital computers, namely sorting using Grover’s algorithm (Grover 1996) and factorization using Shor’s algorithm (Shor 1994). In particular, based on Shor’s algorithm, a quantum computer’s ability to factor in polynomial time (much faster) gives it the potential to crack any encryption scheme based on prime numbers in a fraction of the time of conventional computers. However, it depends on the ability to scale a quantum computer with multiple qubits and combine multiple qubits into a large-scale quantum computer. This continues to be elusive despite significant ongoing research on the topic. Bell Labs has made significant progress toward the creation of a new type of topological qubit device (figure 7). It promises to be immune to the decoherence phenomena — and the unacceptably short lifetimes of quantum states that result — which have plagued quantum computing for many decades.
Quantum algorithm for solving the test suite minimization problem
Published in Cogent Engineering, 2021
Hager Hussein, Ahmed Younes, Walid Abdelmoez
Grover’s algorithm is effective when the initial amplitude distribution of dataset is uniform, which means that is equal to the number of stored data, but it is not always effective in the non-uniform cases where is not equal to the number of stored data. Arima’s algorithm was proposed to solve the search in an incomplete superposition.
Brain Tumour Classification Using Quantum Support Vector Machine Learning Algorithm
Published in IETE Journal of Research, 2023
Tarun Kumar, Dilip Kumar, Gurmohan Singh
Many researchers have investigated the efficacy of Quantum Support Vector Machines based on their theoretical and practical implementations for quantum machine learning problems. J. Biamonte et al. [8] explained how the crossover of quantum computing and machine learning benefitted the development of quantum machine learning algorithms. Machine learning enhanced the benchmarking and control of the quantum systems by following the principles of quantum mechanics. It leads to the performance improvement of the quantum systems by reducing the computational complexity. They have discussed various quantum techniques to process the big data which include Grover’s algorithm for amplitude amplification, QSVM for classification tasks, k-means clustering for clustering tasks, etc. The authors highlighted that these algorithms offer quantum speedup. J. C. Adcock et al. [15] presented an overview of classical machine learning and quantum machine learning, compared both classical and quantum machine learning and principal component analysis. The authors also discussed the quantum algorithm which includes the HHL algorithm for solving a linear system of equations, the k-Nearest Neighbour (KNN) algorithm, the SVM algorithm, etc. and implemented a QSVM on a four-qubit quantum simulator to check whether a hand-written number is 6 or 9. Nimish Mishra et al. [9] explained the influence of quantum computers on normal processing and machine learning. The authors discussed algorithms such as quantum HHL, SVM, QSVM, etc. The implementation techniques, applications of quantum algorithms, and the challenges such as data handling and data visualization in QML are discussed. S. Saini et al. [23] presented a classification model based on QSVM and implemented it on the breast cancer dataset. They revealed that due to the complex computations performed on quantum computer/simulator, QSVM lagged in accuracy compared to SVM but the computational speed offered by the quantum simulator is 234 folds quicker than its classical equivalent. R.D.M. Simoes et al. [33] explored the application of quantum machine learning in solving practical problems, focusing on kernel-based quantum support vector machines and quantum neural networks. By evaluating these algorithms on five different datasets with various quantum feature maps, the experiments show that quantum support vector machines have an accuracy improvement of 3%–4% over classical solutions on average. Although the experiments were conducted on relatively small datasets, the results demonstrate the potential of quantum computing in solving small-scale machine learning problems with better accuracy and less complexity.