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Paperback. Condizione: New. 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 FeaturesExplore new chapters on quantum machine learning and advanced algorithmsApply noise-aware quantum chemistry with error mitigation techniquesWork with updated Qiskit workflows and modern Python examplesBook DescriptionBuild 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 learnUnderstand quantum mechanics and molecular systemsBuild quantum circuits using Qiskit and PythonImplement VQE for molecular energy estimationApply error mitigation in noisy quantum systemsUse optimizers for stable hybrid quantum workflowsDevelop quantum machine learning models for moleculesExplore advanced algorithms beyond VQEWho this book is forProfessionals 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.…

Paperback. Condizione: New. 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 FeaturesExplore new chapters on quantum machine learning and advanced algorithmsApply noise-aware quantum chemistry with error mitigation techniquesWork with updated Qiskit workflows and modern Python examplesBook DescriptionBuild 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 learnUnderstand quantum mechanics and molecular systemsBuild quantum circuits using Qiskit and PythonImplement VQE for molecular energy estimationApply error mitigation in noisy quantum systemsUse optimizers for stable hybrid quantum workflowsDevelop quantum machine learning models for moleculesExplore advanced algorithms beyond VQEWho this book is forProfessionals 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.…

Paperback. Condizione: new. Paperback. 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 FeaturesExplore new chapters on quantum machine learning and advanced algorithmsApply noise-aware quantum chemistry with error mitigation techniquesWork with updated Qiskit workflows and modern Python examplesBook DescriptionBuild 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 learnUnderstand quantum mechanics and molecular systemsBuild quantum circuits using Qiskit and PythonImplement VQE for molecular energy estimationApply error mitigation in noisy quantum systemsUse optimizers for stable hybrid quantum workflowsDevelop quantum machine learning models for moleculesExplore advanced algorithms beyond VQEWho this book is forProfessionals 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condizione: new. Paperback. 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 FeaturesExplore new chapters on quantum machine learning and advanced algorithmsApply noise-aware quantum chemistry with error mitigation techniquesWork with updated Qiskit workflows and modern Python examplesBook DescriptionBuild 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 learnUnderstand quantum mechanics and molecular systemsBuild quantum circuits using Qiskit and PythonImplement VQE for molecular energy estimationApply error mitigation in noisy quantum systemsUse optimizers for stable hybrid quantum workflowsDevelop quantum machine learning models for moleculesExplore advanced algorithms beyond VQEWho this book is forProfessionals 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condizione: new. Paperback. 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 FeaturesExplore new chapters on quantum machine learning and advanced algorithmsApply noise-aware quantum chemistry with error mitigation techniquesWork with updated Qiskit workflows and modern Python examplesBook DescriptionBuild 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 learnUnderstand quantum mechanics and molecular systemsBuild quantum circuits using Qiskit and PythonImplement VQE for molecular energy estimationApply error mitigation in noisy quantum systemsUse optimizers for stable hybrid quantum workflowsDevelop quantum machine learning models for moleculesExplore advanced algorithms beyond VQEWho this book is forProfessionals 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. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…