7 papers
Heisenberg-limited Hamiltonian learning without short-time control
Myeongjin Shin, Junseo Lee, Changhun Oh
Characterizing quantum systems by learning their underlying Hamiltonians is a central task in quantum information science. While recent algorithmic advances have achieved near-opti…
Complexity phase transition for continuous-variable cluster state
Byeongseon Go, Hyunseok Jeong, Changhun Oh
Continuous-variable (CV) cluster states offer a promising platform for large-scale measurement-based quantum computations (MBQC). However, finite squeezing inevitably introduces Ga…
Generative modeling with Gaussian Boson Sampling: classically trainable Bosonic Born Machines
Zoltán Kolarovszki, Bence Bakó, Michał Oszmaniec +2
Quantum generative modeling has emerged as a promising application of quantum computers, aiming to model complex probability distributions beyond the reach of classical methods. In…
On the Fundamental Resource for Exponential Advantage in Quantum Channel Learning
Minsoo Kim, Changhun Oh
Quantum resources enable us to achieve an exponential advantage in learning the properties of unknown physical systems by employing quantum memory. While entanglement with quantum…
Quantum learning advantage on a scalable photonic platform
Zheng-Hao Liu, Romain Brunel, Emil E. B. Østergaard +12
Recent advancements in quantum technologies have opened new horizons for exploring the physical world in ways once deemed impossible. Central to these breakthroughs is the concept…
Exponential advantage in continuous-variable quantum state learning
Eugen Coroi, Changhun Oh
We consider the task of learning quantum states in bosonic continuous-variable (CV) systems. We present an experimentally feasible protocol that uses entangled measurements and ref…