Physics (Internal)

Machine learning and glassy optimization of the quantum control problem

This event is part of the Departmental Seminars.

We study the problem of preparing a quantum many-body system from an initial to a target state by optimizing the fidelity over the family of the so-called bang-bang protocols. We present compelling numerical evidence for a universal spin-glass-like transition controlled by the protocol time duration. The glassy critical point is marked by the occurrence of an extensive number of protocols with close-to-optimal fidelity and with a true optimum that appears exponentially difficult to locate. Using a machine learning (ML) inspired framework based on the manifold learning algorithm t-SNE, we are able to visualize the geometry of the high-dimensional control landscape in an effective low-dimensional representation. Across the glassy transition, the control landscape features a proliferation of an exponential number of attractors separated by extensive barriers, which bears a strong resemblance with replica symmetry breaking in spin glasses and random satisfiability problems. We further show that the quantum control landscape maps onto a disorder-free classical Ising model with frustrated nonlocal, multibody interactions. Our work highlights an intricate but unexpected connection between optimal quantum control and spin glass physics, and how tools from ML can be used to visualize and understand glassy optimization landscapes.