Quantum Solutions for Training a Single Layer Binary Neural Network - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Quantum Solutions for Training a Single Layer Binary Neural Network write by Sabrina Ly. This book was released on 2022. Quantum Solutions for Training a Single Layer Binary Neural Network available in PDF, EPUB and Kindle. "Quantum computing is a relatively new field starting in the early 1980s when a physicist named Paul Benioff proposed a quantum mechanical model of the Turing machine, introducing quantum computers. Previously, the focus of most quantum computers was in the study of quantum applications instead of broad applications due to the fact that quantum technology is a newer field with many technology constraints, such as limited qubits and noisy environments. However, quantum computers are still capable of using quantum mechanics to solve specific algorithms with an exponential speed-up in comparison to their classical counterparts. One key algorithm is the HHL algorithm proposed by Harrow, Hassidim and Lloyd in 2009 [1]. This algorithm outlines a quantum approach to solve a linear systems of equations with a best case time complexity of O(poly(log N )), in comparison to the best case time complexity for classical algorithms of O(N^3 ). The HHL algorithm outlines a use for quantum circuits outside of quantum applications. One such application is in machine learning, as many networks use linear regression in their training algorithm. Currently it is not feasible to solve for weight vectors of floating point precision on a quantum computer, but if the weight vector is constrained to binary values 0 or 1 then the problem becomes small enough to implement even on current noisy quantum computers. This work outlines two different circuit designs to solve for 2 × 2 and 4 × 4 systems of equations, so long as the matrices follow the eigenvalue constraint of having eigenvalues be powers of 2. In addition, the problem of reading data from the quantum state to classical data is addressed through the use of a swap test between the solution state |x> and an test state |test>. By using a swap test vector of all 1s, it is shown it is possible to find how many ones lay in the solution vector; thus reducing the number of possible solution states without performing quantum tomography. While it is not possible to beat classical algorithms with the noise on current quantum circuits, this work shows it is possible to implement quantum algorithms for non-quantum applications establishing potential for future hybrid approaches."--Abstract.
Training Binary Neural Networks with a Quantum Computer
Training Binary Neural Networks with a Quantum Computer - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Training Binary Neural Networks with a Quantum Computer write by . This book was released on 2019. Training Binary Neural Networks with a Quantum Computer available in PDF, EPUB and Kindle.
Supervised Learning with Quantum Computers
Supervised Learning with Quantum Computers - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Supervised Learning with Quantum Computers write by Maria Schuld. This book was released on 2018-08-30. Supervised Learning with Quantum Computers available in PDF, EPUB and Kindle. Quantum machine learning investigates how quantum computers can be used for data-driven prediction and decision making. The books summarises and conceptualises ideas of this relatively young discipline for an audience of computer scientists and physicists from a graduate level upwards. It aims at providing a starting point for those new to the field, showcasing a toy example of a quantum machine learning algorithm and providing a detailed introduction of the two parent disciplines. For more advanced readers, the book discusses topics such as data encoding into quantum states, quantum algorithms and routines for inference and optimisation, as well as the construction and analysis of genuine ``quantum learning models''. A special focus lies on supervised learning, and applications for near-term quantum devices.
Machine Learning with Quantum Computers
Machine Learning with Quantum Computers - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning with Quantum Computers write by Maria Schuld. This book was released on 2021-10-17. Machine Learning with Quantum Computers available in PDF, EPUB and Kindle. This book offers an introduction into quantum machine learning research, covering approaches that range from "near-term" to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data. Among the topics discussed are parameterized quantum circuits, hybrid optimization, data encoding, quantum feature maps and kernel methods, quantum learning theory, as well as quantum neural networks. The book aims at an audience of computer scientists and physicists at the graduate level onwards. The second edition extends the material beyond supervised learning and puts a special focus on the developments in near-term quantum machine learning seen over the past few years.
Classical and Quantum Computation
Classical and Quantum Computation - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Classical and Quantum Computation write by Alexei Yu. Kitaev. This book was released on 2002. Classical and Quantum Computation available in PDF, EPUB and Kindle. An introduction to a rapidly developing topic: the theory of quantum computing. Following the basics of classical theory of computation, the book provides an exposition of quantum computation theory. In concluding sections, related topics, including parallel quantum computation, are discussed.
