Applied Quantum Computing for Engineers: Linear Solvers and Optimization

Engineers repeatedly solve two problems: linear systems and optimization. This book shows how to cast both on a quantum computer, run them, and read out an answer. It also shows where that approach works, where it fails, and why.

Quantum computing is a real shift, and unlike earlier transitions such as multi-core or GPU programming, every stage of the quantum pipeline is an open problem: encoding the data, designing the algorithm, running on noisy hardware, and extracting an answer. A bottleneck in any one erodes the speedup promised by the others. Written for graduate students, researchers, and practicing engineers with no prior background in quantum computing, the book covers both tasks from the ground up. The strategies span quantum annealing, Grover's search, the HHL and QSVT linear solvers, and hybrid variational methods such as QAOA and VQLS, along with the data-encoding techniques that decide whether any of them is practical.

Every method is grounded in problems you will recognize: truss analysis and topology optimization, steady-state heat conduction, plane stress. Each comes with runnable Python code, executed on quantum simulators and, where appropriate, on real quantum hardware. Across 21 chapters, the book works through 190+ examples, 240+ exercises, and 100+ code listings.

Throughout, the emphasis is on honest assessment. When a quantum method isn't the right tool, the book says so and shows why. The goal is to prepare you not just to use quantum computers, but to understand, clearly and critically, what practical quantum computing will require.

Prerequisites: a working knowledge of linear algebra and some experience programming in Python. Companion code for every chapter is at github.com/UW-ERSL/AppliedQC.

About the author: Krishnan Suresh is the Mead Witter Foundation Professor of Mechanical Engineering at the University of Wisconsin–Madison.