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.

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.

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 Quantum Factoring Algorithm using ProjectQ Framework

2019 ◽  
Vol 8 (6S3) ◽  
pp. 52-56
Keyword(s):  
Quantum Computing ◽  
Computer Science ◽  
Polynomial Time ◽  
Quantum Computer ◽  
Quantum Algorithm ◽  
Prime Factorization ◽  
Shor's Algorithm ◽  
Computer Simulators ◽  
Testing And Evaluation ◽  
Factoring Algorithm

Prime number factorization is a problem in computer science where the solution to that problem takes super-polynomial time classically. Shor’s quantum factoring algorithm is able to solve the problem in polynomial time by harnessing the power of quantum computing. The implementation of the quantum algorithm itself is not detailed by Shor in his paper. In this paper, an approach and experiment to implement Shor’s quantum factoring algorithm are proposed. The implementation is done using Python and a quantum computer simulator from ProjectQ. The testing and evaluation are completed in two computers with different hardware specifications. User time of the implementation is measured in comparison with other quantum computer simulators: ProjectQ and Quantum Computing Playground. This comparison was done to show the performance of Shor’s algorithm when simulated using different hardware. There is a 33% improvement in the execution time (user time) between the two computers with the accuracy of prime factorization in this implementation is inversely proportional to the number of qubits used. Further improvements upon the program that has been developed for this paper is its accuracy in terms of finding the factors of a number and the number of qubits used, as previously mentioned.

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.

scholarly journals Quantum-Resistant Network for Classical Client Compatibility

2021 ◽  
Vol 50 (2) ◽  
pp. 224-235
Author(s):  
Te-Yuan Lin ◽  
Chiou-Shann Fuh
Keyword(s):  
Quantum Computing ◽  
Polynomial Time ◽  
Time Complexity ◽  
Quantum Computer ◽  
Quantum Network ◽  
Network Infrastructure ◽  
Zero Knowledge ◽  
Shor's Algorithm ◽  
Computational Security ◽  
Computational Difficulty

Quantum computing is no longer a thing of the future. Shor’s algorithm proved that a quantum computer couldtraverse key of factoring problems in polynomial time. Because the time-complexity of the exhaustive keysearch for quantum computing has not reliably exceeded the reasonable expiry of crypto key validity, it is believedthat current cryptography systems built on top of computational security are not quantum-safe. Quantumkey distribution fundamentally solves the problem of eavesdropping; nevertheless, it requires quantumpreparatory work and quantum-network infrastructure, and these remain unrealistic with classical computers.In transitioning to a mature quantum world, developing a quantum-resistant mechanism becomes a stringentproblem. In this research, we innovatively tackled this challenge using a non-computational difficulty schemewith zero-knowledge proof in order to achieve repellency against quantum computing cryptanalysis attacks foruniversal classical clients.

scholarly journals Quantum machine learning for particle physics using a variational quantum classifier

2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
Andrew Blance ◽  
Michael Spannowsky
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Quantum Computing ◽  
Particle Physics ◽  
Gradient Descent ◽  
Computing Methods ◽  
Descent Method ◽  
Gradient Descent Method ◽  
Classification Problems ◽  
Quantum Machine Learning

Abstract Quantum machine learning aims to release the prowess of quantum computing to improve machine learning methods. By combining quantum computing methods with classical neural network techniques we aim to foster an increase of performance in solving classification problems. Our algorithm is designed for existing and near-term quantum devices. We propose a novel hybrid variational quantum classifier that combines the quantum gradient descent method with steepest gradient descent to optimise the parameters of the network. By applying this algorithm to a resonance search in di-top final states, we find that this method has a better learning outcome than a classical neural network or a quantum machine learning method trained with a non-quantum optimisation method. The classifiers ability to be trained on small amounts of data indicates its benefits in data-driven classification problems.

scholarly journals Robust in practice: Adversarial attacks on quantum machine learning

2021 ◽  
Vol 103 (4) ◽  
Author(s):  
Haoran Liao ◽  
Ian Convy ◽  
William J. Huggins ◽  
K. Birgitta Whaley
Keyword(s):  
Machine Learning ◽  
Quantum Machine Learning
Sign in / Sign up

Export Citation Format

Share Document