Machine learning for quantum information and computing

Doctoral thesis, 2023

Quantum physics has revolutionized our understanding of the natural world, stretching the boundaries of classical physics to encompass the mysterious behaviors of particles at the smallest scales. Quantum computers promise a leap in information processing capabilities by harnessing quantum-mechanical effects that can overcome the limitations of classical digital computing as they scale to a size where quantum effects start to dominate. However, to build such quantum computers, we must overcome many challenges that demand precise characterization and enhanced control over quantum devices. Meeting these challenges calls for creating sophisticated models for quantum systems that are fitted with large amounts of noisy experimental data.

Machine learning is a way to push the boundaries of computer programming with data-driven learning, obviating the need for explicit programming. Modern machine learning algorithms have achieved remarkable feats, from identifying objects in images to generating entirely new images from text descriptions and emulating intelligence through conversations, all accomplished by processing large amounts of data.

The convergence of machine learning, quantum information, and computing presents an exciting frontier. We can construct a bridge between these domains by framing the challenges of modeling and characterization in quantum systems as machine-learning problems. At the same time, machine-learning-inspired concepts can lead to new ideas for quantum algorithms. We explore the two aspects of this merger between machine learning, quantum information, and computing.