Fundamentals of Quantum Computing, Quantum Supremacy, and Quantum Machine Learning

2021 ◽  
pp. 21-46
Author(s):  
Kamaljit I. Lakhtaria ◽  
Vrunda Gadesha
Keyword(s):  
Machine Learning ◽  
Quantum Computing ◽  
Quantum Computer ◽  
Error Mitigation ◽  
Fundamental Goal ◽  
Quantum Device ◽  
Quantum Machine Learning ◽  
Simulation Error ◽  
The Difference ◽  
Quantum Optimization

When we aim to demonstrate that a programmable quantum device can solve complex problems which cannot be addressed by classic computers, this fundamental goal is known as quantum supremacy. This concept has changed every fundamental rule of computation. In this chapter, the detailed concept of quantum computing and quantum supremacy is explained along with various open source tools and real-time applications of this technology. The major base concepts, quantum computing, the difference between classical and quantum computer on physical level, programing quantum device, and the experiment-quantum supremacy are explained conceptually. This chapter also includes an introduction of the tools Cirq and OpenFermion plus the applications like quantum simulation, error mitigation technique, quantum machine learning, and quantum optimization, which are explained with illustrations.

scholarly journals Option Pricing using Quantum Computers

Quantum ◽  
2020 ◽  
Vol 4 ◽  
pp. 291 ◽  
Author(s):  
Nikitas Stamatopoulos ◽  
Daniel J. Egger ◽  
Yue Sun ◽  
Christa Zoufal ◽  
Raban Iten ◽  
...  
Keyword(s):  
Option Pricing ◽  
Quantum Computer ◽  
Quantum Circuits ◽  
Quantum Computers ◽  
Barrier Options ◽  
Error Mitigation ◽  
Quantum Device ◽  
Amplitude Estimation ◽  
Simulation Results ◽  
Path Dependent

We present a methodology to price options and portfolios of options on a gate-based quantum computer using amplitude estimation, an algorithm which provides a quadratic speedup compared to classical Monte Carlo methods. The options that we cover include vanilla options, multi-asset options and path-dependent options such as barrier options. We put an emphasis on the implementation of the quantum circuits required to build the input states and operators needed by amplitude estimation to price the different option types. Additionally, we show simulation results to highlight how the circuits that we implement price the different option contracts. Finally, we examine the performance of option pricing circuits on quantum hardware using the IBM Q Tokyo quantum device. We employ a simple, yet effective, error mitigation scheme that allows us to significantly reduce the errors arising from noisy two-qubit gates.

Recent Progress in Quantum Machine Learning

2021 ◽  
pp. 232-256
Author(s):  
Amandeep Singh Bhatia ◽  
Renata Wong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Quantum Computing ◽  
Recent Progress ◽  
Machine Learning Algorithms ◽  
Learning Models ◽  
Exciting Field ◽  
Quantum Machine Learning ◽  
The Subject ◽  
Machine Learning Models

Quantum computing is a new exciting field which can be exploited to great speed and innovation in machine learning and artificial intelligence. Quantum machine learning at crossroads explores the interaction between quantum computing and machine learning, supplementing each other to create models and also to accelerate existing machine learning models predicting better and accurate classifications. The main purpose is to explore methods, concepts, theories, and algorithms that focus and utilize quantum computing features such as superposition and entanglement to enhance the abilities of machine learning computations enormously faster. It is a natural goal to study the present and future quantum technologies with machine learning that can enhance the existing classical algorithms. The objective of this chapter is to facilitate the reader to grasp the key components involved in the field to be able to understand the essentialities of the subject and thus can compare computations of quantum computing with its counterpart classical machine learning algorithms.

scholarly journals Implementation of Shor’s Algorithm in QISKIT by Quantum Computing using IBMQ

2021 ◽  
Vol 10 (2) ◽  
pp. 1166-1170
Keyword(s):  
Machine Learning ◽  
Quantum Computing ◽  
Polynomial Time ◽  
Classical Algorithm ◽  
Shor's Algorithm ◽  
Quantum Machine Learning

Quantum machine learning is the combination of quantum computing and classical machine learning. It helps in solving the problems of one field to another field. Shor’s algorithm is used for factoring the integers in polynomial time. Since the bestknown classical algorithm requires super polynomial time to factor the product of two primes, the widely used cryptosystem, RSA, relies on factoring being impossible for large enough integers. In this paper we will focus on the quantum part of Shor’s algorithm, which actually solves the problem of period finding. In polynomial time factoring problem can be turned into a period finding problem so an efficient period finding algorithm can be used to factor integers efficiently.

scholarly journals Modeling of Supervised Machine Learning using Mechanism of Quantum Computing.

2022 ◽  
Vol 2161 (1) ◽  
pp. 012023
Author(s):  
Mukta Nivelkar ◽  
S. G. Bhirud
Keyword(s):  
Machine Learning ◽  
Quantum Computing ◽  
High Volume ◽  
Supervised Machine Learning ◽  
Volume Data ◽  
Quantum Mechanism ◽  
Quantum Machine Learning ◽  
Classical Models ◽  
Quantum Technology ◽  
Classical Computer

Abstract Mechanism of quantum computing helps to propose several task of machine learning in quantum technology. Quantum computing is enriched with quantum mechanics such as superposition and entanglement for making new standard of computation which will be far different than classical computer. Qubit is sole of quantum technology and help to use quantum mechanism for several tasks. Tasks which are non-computable by classical machine can be solved by quantum technology and these tasks are classically hard to compute and categorised as complex computations. Machine learning on classical models is very well set but it has more computational requirements based on complex and high-volume data processing. Supervised machine learning modelling using quantum computing deals with feature selection, parameter encoding and parameterized circuit formation. This paper highlights on integration of quantum computation and machine learning which will make sense on quantum machine learning modeling. Modelling of quantum parameterized circuit, Quantum feature set design and implementation for sample data is discussed. Supervised machine learning using quantum mechanism such as superposition and entanglement are articulated. Quantum machine learning helps to enhance the various classical machine learning methods for better analysis and prediction using complex measurement.

Quantum Machine Learning for Drug Discovery

2020 ◽  
Author(s):  
Kushal Batra ◽  
Kimberley M. Zorn ◽  
Daniel H. Foil ◽  
Eni Minerali ◽  
Victor O. Gawriljuk ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Molecular Descriptors ◽  
Quantum Computer ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Scoring Functions ◽  
Biological Targets ◽  
Hybrid Approaches ◽  
Quantum Machine Learning

<p>The growing public and private datasets focused on small molecules screened against biological targets or whole organisms <sup>1</sup> provides a wealth of drug discovery relevant data. Increasingly this is used to create machine learning models which can be used for enabling target-based design <sup>2-4</sup>, predict on- or off-target effects and create scoring functions <sup>5,6</sup>. This is matched by the availability of machine learning algorithms such as Support Vector Machines (SVM) and Deep Neural Networks (DNN) that are computationally expensive to perform on very large datasets and thousands of molecular descriptors. Quantum computer (QC) algorithms have been proposed to offer an approach to accelerate quantum machine learning over classical computer (CC) algorithms, however with significant limitations. In the case of cheminformatics, one of the challenges to overcome is the need for compression of large numbers of molecular descriptors for use on QC. Here we show how to achieve compression with datasets using hundreds of molecules (SARS-CoV-2) to hundreds of thousands (whole cell screening datasets for plague and <i>M. tuberculosis</i>) with SVM and data re-uploading classifier (a DNN equivalent algorithm) on a QC benchmarked against CC and hybrid approaches. This illustrates a quantum advantage for drug discovery to build upon in future.</p>

scholarly journals Variational Quantum Circuits for Machine Learning. An Application for the Detection of Weak Signals

2021 ◽  
Vol 11 (14) ◽  
pp. 6427
Author(s):  
Israel Griol-Barres ◽  
Sergio Milla ◽  
Antonio Cebrián ◽  
Yashar Mansoori ◽  
José Millet
Keyword(s):  
Machine Learning ◽  
Quantum Computing ◽  
Quantum Computer ◽  
Technological Development ◽  
Quantum Circuits ◽  
Support Vector ◽  
Classification Problems ◽  
New Paradigm ◽  
Weak Signals ◽  
Advantages And Disadvantages

Quantum computing is a new paradigm for a multitude of computing applications. This study presents the technologies that are currently available for the physical implementation of qubits and quantum gates, establishing their main advantages and disadvantages and the available frameworks for programming and implementing quantum circuits. One of the main applications for quantum computing is the development of new algorithms for machine learning. In this study, an implementation of a quantum circuit based on support vector machines (SVMs) is described for the resolution of classification problems. This circuit is specially designed for the noisy intermediate-scale quantum (NISQ) computers that are currently available. As an experiment, the circuit is tested on a real quantum computer based on superconducting qubits for an application to detect weak signals of the future. Weak signals are indicators of incipient changes that will have a future impact. Even for experts, the detection of these events is complicated since it is too early to predict this impact. The data obtained with the experiment shows promising results but also confirms that ongoing technological development is still required to take full advantage of quantum computing.

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