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Quantum Computation for Big Information Processing
Published in Neeraj Kumar, N. Gayathri, Md. Arafatur Rahman, B. Balamurugan, Blockchain, Big Data and Machine Learning, 2020
Tawseef Ayoub Shaikh, Rashid Ali
Because of the unavailability of quantum computers and necessary hardware for its implementation, lack of proper tools and simulation environments for carrying out the quantum simulation, quantum computing is still in its infancy stage posing a hot challenge in the information processing. But a lot of progress is going on in this field and in time, it may become the treasure house for big data analytics. Since modern data sets generated from different sources, possessing vast formats like text, image, sensor readings, and streaming data. Likewise, quantum computing has its basic units as quantum (photons), so it can be worth of use to remove this heterogeneity or variety problem in the big data, as the data in it is being analyzed at the electronic level. Once the quantum computer hardware will be ready in the next couple of years, quantum computing will be the hottest topic for tackling down the big data analytics problems.
The Future of Electronics
Published in John D. Cressler, Silicon Earth, 2017
For example, integer factorization (the decomposition of a composite number into a product of smaller integers) is computationally intractable with a classical digital computer for very large integers, if they are the product of prime numbers (e.g., the product of two 300-digit prime numbers). By comparison, a quantum computer could efficiently solve this problem using something called Shor’s algorithm (don’t ask) to find its factors. This ability would allow a quantum computer to quickly decrypt many of the cryptographic systems in use today. Yikes! In particular, most of the popular public key ciphers in use today are based on this difficulty digital computers have of factoring large integers. Such key ciphers are used to protect secure webpages, encrypt e-mail, and essentially lock-down many other types of data from prying eyes. Quantum computers can break those encryptions, with massive ramifications for electronic privacy and security. On a global scale. Read: this is a big deal. Besides factorization, quantum computers offer substantial speed-up over classical digital computers for several other problems, including the simulation of quantum physical processes from chemistry and solid-state physics, and database searching. Since, by definition, chemistry and nanotechnology rely on the detailed understanding of quantum systems, and such systems are impossible to simulate in an efficient manner classically, quantum simulation is likely to be one of the most important applications of quantum computing. Quantum simulation could also be used to simulate the behavior of atoms and particles under unusual conditions, such as the reactions inside a particle collider (think LHC at CERN). Read: quantum computers can do LOTS of cool things. That is, if we can actually build them!
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.