Quantum Chemistry and Computing for the Curious Explore quantum chemistry with Python and Qiskit, 2nd Edition
Quantum Chemistry and Computing for the Curious Explore quantum chemistry with Python and Qiskit, 2nd Edition | 64.06 MB
Title: Quantum Chemistry and Computing for the Curious Explore quantum chemistry with Python and Qiskit, 2nd Edition
Language: English | 494 Pages | ISBN: 9781807304676
Description:
Learn quantum chemistry and computing with updated Qiskit workflows, noise-aware simulations, and quantum machine learning techniques using Python, with hands-on learning, turning complex concepts into practical skills
Key Features
Explore new chapters on quantum machine learning and advanced algorithms
Apply noise-aware quantum chemistry with error mitigation techniques
Work with updated Qiskit workflows and modern Python examples
Book Description
Build a solid foundation in quantum chemistry and quantum computing using modern tools, updated frameworks, and practical Python examples. In its second edition, this book enhances the original with new chapters and refreshed workflows aligned with the latest advancements.
You begin with core principles of quantum mechanics, quantum information, and molecular Hamiltonians, updated to incorporate the latest Qiskit capabilities. You then implement hybrid algorithms such as VQE using improved Python workflows. New to this edition, you will explore noise‑aware quantum chemistry, including error‑mitigation techniques and optimizer behavior in realistic simulations. The book also introduces quantum machine learning for molecular prediction and a new generation of quantum algorithms for chemistry, including Quantum Phase Estimation (QPE), Quantum Imaginary‑Time Evolution (QITE), quantum Lanczos and subspace methods, and sampling‑based approaches such as Sample‑based Quantum Diagonalization (SQD), Sample‑based Krylov Quantum Diagonalization (SKQD), and SqDRIFT, which combines SKQD with a qDRIFT‑style randomized compilation of the Hamiltonian propagator.
By the end of this book, you will be able to model molecular systems and apply modern quantum techniques with confidence.
What you will learn
Understand quantum mechanics and molecular systems
Build quantum circuits using Qiskit and Python
Implement VQE for molecular energy estimation
Apply error mitigation in noisy quantum systems
Use optimizers for stable hybrid quantum workflows
Develop quantum machine learning models for molecules
Explore advanced algorithms beyond VQE
Who this book is for
Professionals interested in chemistry and computer science at the early stages of learning or interested in a career of quantum computational chemistry and quantum computing, including advanced high school and college students. Helpful to have high school level chemistry, mathematics (algebra), and programming. An introductory level of understanding Python is sufficient to read the code presented to illustrate quantum chemistry and computing.
DOWNLOAD:
https://rapidgator.net/file/c7999be00d2386b04ae517af3953084c/9781807304676.rar
https://nitroflare.com/view/D60D7578CB01E6D/9781807304676.rar
Learn quantum chemistry and computing with updated Qiskit workflows, noise-aware simulations, and quantum machine learning techniques using Python, with hands-on learning, turning complex concepts into practical skills
Key Features
Explore new chapters on quantum machine learning and advanced algorithms
Apply noise-aware quantum chemistry with error mitigation techniques
Work with updated Qiskit workflows and modern Python examples
Book Description
Build a solid foundation in quantum chemistry and quantum computing using modern tools, updated frameworks, and practical Python examples. In its second edition, this book enhances the original with new chapters and refreshed workflows aligned with the latest advancements.
You begin with core principles of quantum mechanics, quantum information, and molecular Hamiltonians, updated to incorporate the latest Qiskit capabilities. You then implement hybrid algorithms such as VQE using improved Python workflows. New to this edition, you will explore noise‑aware quantum chemistry, including error‑mitigation techniques and optimizer behavior in realistic simulations. The book also introduces quantum machine learning for molecular prediction and a new generation of quantum algorithms for chemistry, including Quantum Phase Estimation (QPE), Quantum Imaginary‑Time Evolution (QITE), quantum Lanczos and subspace methods, and sampling‑based approaches such as Sample‑based Quantum Diagonalization (SQD), Sample‑based Krylov Quantum Diagonalization (SKQD), and SqDRIFT, which combines SKQD with a qDRIFT‑style randomized compilation of the Hamiltonian propagator.
By the end of this book, you will be able to model molecular systems and apply modern quantum techniques with confidence.
What you will learn
Understand quantum mechanics and molecular systems
Build quantum circuits using Qiskit and Python
Implement VQE for molecular energy estimation
Apply error mitigation in noisy quantum systems
Use optimizers for stable hybrid quantum workflows
Develop quantum machine learning models for molecules
Explore advanced algorithms beyond VQE
Who this book is for
Professionals interested in chemistry and computer science at the early stages of learning or interested in a career of quantum computational chemistry and quantum computing, including advanced high school and college students. Helpful to have high school level chemistry, mathematics (algebra), and programming. An introductory level of understanding Python is sufficient to read the code presented to illustrate quantum chemistry and computing.
DOWNLOAD:
https://rapidgator.net/file/c7999be00d2386b04ae517af3953084c/9781807304676.rar
https://nitroflare.com/view/D60D7578CB01E6D/9781807304676.rar
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