Learning properties of quantum states without the IID assumption.

We develop a framework for learning properties of quantum states beyond the assumption of independent and identically distributed (i.i.d.

Improved machine learning algorithm for predicting ground state properties.

Finding the ground state of a quantum many-body system is a fundamental problem in quantum physics. In this work, we give a classical machine learning (ML) algorithm for predicting ground state properties with an inductive bias encoding geometric locality. The proposed ML model can efficiently predict ground state properties of an n-qubit gapped local Hamiltonian after learning from only [Formula: see text] data about other Hamiltonians in the same quantum phase of matter.

Development of a new immersive virtual reality (VR) headset-based dexterity training for patients with multiple sclerosis: Clinical and technical aspects.

Background: Impaired manual dexterity is frequent and disabling in patients with multiple sclerosis (MS), affecting activities of daily living and quality of life.

Objective: To develop a new immersive virtual-reality (VR) headset-based dexterity training to improve impaired manual dexterity in persons with MS (pwMS) while being feasible and usable in a home-based setting.

Methods: The training intervention was tailored to the specific group of pwMS by implementing a simple and intuitive application with regard to hardware and software.

Feasibility and usability of a new home-based immersive virtual reality headset-based dexterity training in multiple sclerosis.

Background: Impaired manual dexterity is frequent and disabling in patients with multiple sclerosis (MS), affecting activities of daily living and quality of life.

Objective: The aim of this study was to evaluate the feasibility, usability and patient engagement/satisfaction of a home-based immersive virtual reality (VR) headset-based dexterity training in persons with multiple sclerosis (pwMS). In addition, preliminary efficacy data on the impact of this new training on manual dexterity were collected.