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Quantum Machine Learning with Python - Using Cirq from Google Research and IBM Qiskit (Paperback, 1st ed.)
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Quantum Machine Learning with Python - Using Cirq from Google Research and IBM Qiskit (Paperback, 1st ed.)
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Quickly scale up to Quantum computing and Quantum machine learning
foundations and related mathematics and expose them to different
use cases that can be solved through Quantum based algorithms.This
book explains Quantum Computing, which leverages the Quantum
mechanical properties sub-atomic particles. It also examines
Quantum machine learning, which can help solve some of the most
challenging problems in forecasting, financial modeling, genomics,
cybersecurity, supply chain logistics, cryptography among others.
You'll start by reviewing the fundamental concepts of Quantum
Computing, such as Dirac Notations, Qubits, and Bell state,
followed by postulates and mathematical foundations of Quantum
Computing. Once the foundation base is set, you'll delve deep into
Quantum based algorithms including Quantum Fourier transform, phase
estimation, and HHL (Harrow-Hassidim-Lloyd) among others. You'll
then be introduced to Quantum machine learning and Quantum deep
learning-based algorithms, along with advanced topics of Quantum
adiabatic processes and Quantum based optimization. Throughout the
book, there are Python implementations of different Quantum machine
learning and Quantum computing algorithms using the Qiskit toolkit
from IBM and Cirq from Google Research. What You'll Learn
Understand Quantum computing and Quantum machine learning Explore
varied domains and the scenarios where Quantum machine learning
solutions can be applied Develop expertise in algorithm development
in varied Quantum computing frameworks Review the major challenges
of building large scale Quantum computers and applying its various
techniques Who This Book Is For Machine Learning enthusiasts and
engineers who want to quickly scale up to Quantum Machine Learning
General
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