Giacomo Nannicini (Photo/Courtesy of Giacomo Nannicini)
Imagine a world where computers could solve complex problems that currently take days or weeks in just hours or minutes. That’s the aspirational goal of quantum optimization, a cutting-edge field studying how quantum computers could tackle mathematical challenges that affect everything from manufacturing efficiency to logistics to finance.
Giacomo Nannicini, associate professor at the Daniel J. Epstein Department of Industrial & Systems Engineering, is helping bring that world closer to reality. His new book, “Quantum Algorithms for Optimizers,” which came out in December 2025, makes this highly technical field accessible to engineers and applied mathematicians without requiring any background in quantum physics. His work, which earned him some of the field’s highest honors, including the 2021 Beale-Orchard-Hays Prize and a best paper award at the IEEE Quantum Computing and Engineering conference, has the potential to transform industries from chemical engineering to finance, making systems more efficient and decisions smarter.
Nannicini spoke with USC Viterbi writer Marc Ballon about his new book and the field of quantum computing.
For someone who’s never heard of quantum optimization, how would you explain what it is in everyday terms? Why is it important?
There are multiple areas of research that could rightfully be called “quantum optimization.” I am particularly interested in the use of quantum computers to solve mathematical optimization problems. Mathematical optimization is a fundamental instrument in the toolbox of industrial and systems engineers: It lets you design and operate systems efficiently and according to specifications. Optimal decision making, analytics and machine learning all rely on mathematical optimization. The aim of some of my work is to try to understand if quantum computers let us design algorithms that have a computational advantage over existing methodologies. For example, algorithms that can solve larger or more difficult problems, or similar problems but faster. This is an important research question because mathematical optimization has real, tangible business impact.
What industries or fields do you think will be most transformed by quantum optimization?
This is a great and difficult question. I think that one important field, machine learning with large, “non-quantum” datasets, will not be transformed, at least in the short term. Luckily, optimization has plenty of applications besides machine learning. I think the most promising direction is the solution of difficult problems that optimizers call “nonconvex,” for which existing software can struggle. Nonconvex problems are found in many areas of engineering, such as chemical engineering, financial engineering, and mechanical engineering.
How close are we to seeing practical, commercially viable quantum optimization solutions?
I am skeptical of the practical viability for now, so I think we are still far from commercially viable quantum optimization solutions. There are quantum algorithms that hold promise in theory, and some of them have preliminary implementations, but non-quantum algorithms have been refined for 50 years and will be difficult to beat. More importantly, existing quantum computers are quite primitive, and they are not capable of executing many of the quantum algorithms that are helpful in theory.
We have two large obstacles: On the one hand, we need to design better algorithms and make them practical, something that often requires years of refining and careful experimental testing. On the other hand, we need more capable hardware, so that these algorithms can be tested.
What inspired you to write this book now?
In a nutshell, I want more researchers that are able to think about promising research directions for quantum optimization, and that are able to scrutinize existing results and assess their impact on industrial practice. Given the rapid increase in public interest, media attention and funding, both private and government, I think that it is the right time to make a push for accessibility and help train a new generation of scientists. I hope that this book will move some steps in that direction.
What is your goal in writing your book?
I want this book to be accessible to students and researchers with a background in optimization or applied mathematics. Most engineers fit this description. Knowledge of quantum mechanics is not necessary at all to read this book! The book is meant to demystify quantum algorithms and quantum computing. I always find that the barrier of entry to this field is unnecessarily high for many engineers, simply due to the fact that there are no good resources that provide a physics-free treatment of the topic. But in fact, one can understand the field very well using only linear algebra and computational complexity.
What are your most important findings?
I’m not sure if I can point to specific important findings. This book is a comprehensive treatment of a new subject, one that so far could only be learned by reading books and papers for which industrial engineers typically lacked the background. It highlights certain connections between optimization and quantum algorithms, and the presentation of most topics is tailored to readers with a background in applied mathematics. One could say that the most important finding is that quantum optimization algorithms can be rigorously studied without knowing quantum mechanics.
What’s next for you?
I’ve been writing exercises for the book, which will be published as an online companion. On the research side, I’ve been working on designing and understanding quantum and non-quantum algorithms for linear and nonlinear optimization. On the teaching side, I will teach an undergraduate optimization class in the fall, my first undergraduate class at USC. I also plan to teach a graduate class on quantum optimization algorithms, of course.
Published on February 11th, 2026
Last updated on February 11th, 2026