With the fast development of quantum technology, the size of quantum systems we can digitally manipulate and analogly probe increase drastically. In order to have a better control and understanding of the quantum hardware, an important task is to characterize the interaction, i.e., to learn the Hamiltonian, which determines both static or dynamic properties of the system. Conventional Hamiltonian learning methods either require costly process tomography or adopt impractical assumptions, such as prior information of the Hamiltonian structure and the ground or thermal states of the system. In this work, we present a practical and efficient Hamiltonian learning method that circumvents these limitations. The proposed method can efficiently learn any Hamiltonian that is sparse on the Pauli basis using only short time dynamics and local operations without any information of the Hamiltonian or preparing any eigenstates or thermal states. The method has scalable complexity and vanishing failure probability regarding the qubit number.

As machine learning continues to surpass human performance in a growing number of tasks, scientists at Skoltech have applied deep learning to reconstruct quantum properties of optical systems. Through a collaboration between the quantum optics research laboratories at Moscow State University, led by Sergey Kulik, and members of Skoltech's Deep Quantum Laboratory of CPQM, led by Jacob Biamonte, the scientists have successfully applied machine learning to the state reconstruction problem. Their findings have been reported in npj Quantum Information, and are the first to show that machine learning can reconstruct quantum states from experimental data in the presence of noise and detector errors. The MSU team generated data with an experimental platform based on spatial states of photons to prepare and measure high-dimensional quantum states. Experimental errors in state preparation and measurements inevitably plague the results and the situation becomes worse with increasing dimensionality.

IMAGE: The theoretical beam is the goal scientists wished to achieve. As machine learning continues to surpass human performance in a growing number of tasks, scientists at Skoltech have applied deep learning to reconstruct quantum properties of optical systems. Through a collaboration between the quantum optics research laboratories at Moscow State University, led by Sergey Kulik, and members of Skoltech's Deep Quantum Laboratory of CPQM, led by Jacob Biamonte, the scientists have successfully applied machine learning to the state reconstruction problem. Their findings have been reported in the Nature Partner Journal, npj Quantum Information, and are the first to show that machine learning can reconstruct quantum states from experimental data in the presence of noise and detector errors. Skoltech PhD student Adriano Macarone Palmieri, lead author of the study, described the findings as " a new open door towards deeper insights ." Adriano has a Master's Degree in Physics from Bologna and joined Skoltech from Italy, where he worked as a data scientist.