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.
Table of Contents
- Introducing Quantum Concepts
- Postulates of Quantum Mechanics
- Quantum Circuit Model of Computation
- Molecular Hamiltonians
- Variational Quantum Eigensolver (VQE) Algorithm
- Noise in Quantum Computation
- QML for Molecular Prediction
- Advanced Algorithms for Chemistry
- Beyond Born-Oppenheimer
- Conclusion
- Glossary
Chancé, Alain: - "Alain Chancé is Business advisor ODE, L3C and founder & CEO Quantalain SASU, a management consulting startup. He has over 30 years of experience in major enterprise transformation projects with a focus on data management and governance gained in major management consulting firms. He has a diploma ingénieur civil des Mines from École des Mines de Saint-Étienne (1981). Alain is co-speaker with Dr. Keeper Sharkey of the event: Next-Generation accuracy for quantum computational chemistry: introducing QLEAN(TM), Quantum Business Europe 2022. Alain is a Qiskit(R) Advocate and is an IBM Certified Associate Developer - Quantum Computation using Qiskit(R) v0.2X since 2021. He has completed a number of hackathons pertaining to quantum computing since 2018."
Sharkey, Keeper Layne: - Keeper L. Sharkey, PhD is the founder and CEO of ODE, L3C, a social enterprise that serves through Quantum Science, Technology and Research, qSTAR. She is Chair of Quantum Applied Chemistry at Quantum Security Alliance. She obtained a PhD in Chemical Physics from the University of Arizona as a US National Science Foundation graduate research fellow, May 2015, and a Bachelor's of Science in both Mathematics and Chemistry, May 2010. She remains a Designated Scientific Research Campus Colleague at the University of Arizona. She published over 30 manuscripts in top peer-reviewed journals regarding non-Born-Oppenheimer quantum mechanical finite-nuclear mass variational algorithms and has been cited over 400 times; H-index and i10-index of 10
