Project description
Shining light on new physics phenomena of quantum matter out of equilibrium
Quantum matter refers to states of matter where quantum physics operates on microscopic scales to produce exotic phenomena that bear no similarity to the macroscopic world. Realising novel phases of matter and building reliable quantum computers hinge on the ability to manipulate such quantum matter with high precision. The EU-funded PHASEQUANTROL project will shed new light on newly observed physics phenomena that are also common to macroscopic systems: glassy phases of matter and spontaneous symmetry breaking. The proposed research calls for an unprecedented level of synergy between different disciplines – reinforcement learning, optimal control, condensed matter physics and statistical mechanics. The research will address issues that may seem unrelated to advance our understanding of the dynamics of quantum many-body systems out of equilibrium.
Objective
A particularly useful application of out-of-equilibrium quantum dynamics is the control of quantum matter. High-precision quantum control defines a cutting edge frontier in developing quantum technology: high-fidelity preparation of target states with prescribed properties, experimental studies of novel phases of matter, and reliable quantum computing depend upon our ability to manipulate quantum systems. Recently, it was discovered that optimal quantum control exhibits continuous and discontinuous phase transitions familiar from macroscopic systems: correlated/glassy phases and spontaneous symmetry breaking appear also in the optimization landscape. Despite this progress, much of the physics of this new phenomenon remains unknown.
The objectives of this project are to:
(1) develop theoretical understanding for phase transitions in the optimization landscapes of nonequilibrium quantum control problems, such as quantum state preparation and entanglement reduction;
(2) create a new perspective on quantum many-body control and longstanding optimization problems in many-body physics by investigating the correlations between local minima of the control landscape;
(3) reveal limitations of reinforcement learning and optimal control algorithms in correlated optimization landscapes.
This unconventional approach to quantum control will allow us to quantify the complexity of nonequilibrium control tasks in terms of properties of the underlying phases of control. We will identify common traits in the space of almost-optimal solutions, and use them to investigate features of the controlled physical systems.
The proposed research opens up a new frontier which can point to a mutually beneficial synergy between reinforcement learning, optimal control, condensed matter physics and statistical mechanics. It combines hitherto unrelated concepts to advance our understanding of nonequilibrium quantum many-body dynamics, with applications in ultracold atoms and trapped ions.
Fields of science
- natural sciencesphysical sciencescondensed matter physics
- natural sciencesphysical sciencesquantum physics
- natural sciencescomputer and information sciencesartificial intelligencemachine learningreinforcement learning
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringcomputer hardwarequantum computers
- natural sciencesphysical sciencesclassical mechanicsstatistical mechanics
Keywords
Programme(s)
Funding Scheme
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinator
1504 Sofia
Bulgaria