Quantum technologies, such as quantum computers and sensors, rely on the ability to steer the behaviour of quantum particles precisely. However, the process of finding the best way to control a quantum system can be like navigating a vast and rugged surface, called the control landscape. Moreover, control landscapes are not static, and can undergo sudden and dramatic changes as experimental parameters are varied, similar to how water abruptly freezes into ice. These changes, known as control landscape phase transitions, mark critical points where new optimal control strategies suddenly emerge. In a pair of papers, Nicolò Beato, Pranay Patil, and Marín Bukov of the Max Planck Institute for the Physics of Complex Systems have now proposed analytical and numerical techniques to describe these transitions, borrowing tools from statistical physics usually applied to complex systems such as magnets and glasses.
Their work sheds new light on these transitions, which are linked to physical properties of the quantum system, such as the minimum time needed to drive a system from one state to another. By applying their methods to prototypical quantum systems, the researchers characterised their fundamental properties and were able to understand why these transitions arise. Their theory suggests a deep interconnection with the physics of disordered media, such as spin and structural glasses. This research opens up a path toward systematically understanding—and ultimately harnessing—the complexity of quantum control, which is essential for building reliable quantum technologies.
Niccolò Beato, Pranay Patil, and Marín Bukov,
Phys. Rev. Lett. 135, 110803 (2025)
Niccolò Beato, Pranay Patil, and Marín Bukov,
Phys. Rev. X 15, 041014 (2025)