Heisenberg-Limited Hamiltonian Learning for Continuous Variable Systems via Engineered Dissipation

Learn about Heisenberg-limited Hamiltonian learning for continuous variable quantum systems through engineered dissipation in this 43-minute conference talk. Explore how discrete and continuous variables require different treatments in quantum learning tasks, with a specific focus on identifying Hamiltonians governing bosonic quantum system evolution. Discover an analytic framework for studying strong dissipation effects in continuous-variable systems, enabling the development of Heisenberg-limited algorithms for learning general bosonic Hamiltonians with higher-order terms of creation and annihilation operators. Examine a new quantitative adiabatic approximation estimate for general Lindbladian evolutions with unbounded generators, addressing a gap in quantum learning theory where previous work primarily focused on quantum spin systems rather than continuous-variable quantum systems. Gain insights into advanced quantum algorithms for open quantum systems and their applications in bosonic system characterization through this research presentation from INRIA delivered at IPAM's New Frontiers in Quantum Algorithms for Open Quantum Systems Workshop.