Related papers: In situ Learning-Based Spin Engineering of Pulsed …
A method is advanced allowing for fast regulation of magnetization direction in magnetic nanosystems. The examples of such systems are polarized nanostructures, magnetic nanomolecules, magnetic nanoclusters, magnetic graphene, dipolar and…
We study a mechanism by which nuclear hyperpolarization due to the polarization transfer from a microwave-pulse-controlled electron spin is suppressed. From analytical and numerical calculations of the unitary dynamics of multiple nuclear…
Mechanical resonators operating in the high-frequency regime have become a versatile platform for fundamental and applied quantum research. Their exceptional properties, such as low mass and high quality factor, make them also very…
Nuclear Magnetic Resonance (NMR) forms a natural test-bed to perform quantum information processing (QIP) and has so far proven to be one of the most successful quantum information processors. The nuclear spins in a molecule treated as…
Optically active spin defects in solids offer promising platforms to investigate nuclear spin clusters with high sensitivity and atomic-site resolution. To leverage near-surface defects for molecular structure analysis in chemical and…
Different approaches have improved the sensitivity of either electron or nuclear magnetic resonance to the single spin level. For optical detection it has essentially become routine to observe a single electron spin or nuclear spin.…
The ability to sense and control nuclear spins near solid-state defects might enable a range of quantum technologies. Dynamically Decoupled Radio-Frequency (DDRF) control offers a high degree of design flexibility and long electron-spin…
We propose the Distance-informed Neural Process (DNP), a novel variant of Neural Processes that improves uncertainty estimation by combining global and distance-aware local latent structures. Standard Neural Processes (NPs) often rely on a…
The utilization of Deep Neural Networks (DNNs) in physical science and engineering applications has gained traction due to their capacity to learn intricate functions. While large datasets are crucial for training DNN models in fields like…
We develop an efficient package for the simulation of nuclear magnetic resonance spin echo experiments to study the effects of strong electronic spin correlations on the dynamics of the nuclear spin ensemble. A mean-field model is used to…
A promising platform for quantum information processing is that of silicon impurities, where the quantum states are manipulated by magnetic resonance. Such systems, in abstraction, can be considered as a nucleus of arbitrary spin coupled to…
Efficient polarization of organic molecules is of extraordinary relevance when performing nuclear magnetic resonance (NMR) and imaging. Commercially available routes to dynamical nuclear polarization (DNP) work at extremely…
We present a scheme of surface-sensitive nuclear magnetic resonance in optically pumped semiconductors, where an NMR signal from a part of the surface of a bulk compound semiconductor is detected apart from the bulk signal. It utilizes…
In the framework of nuclear magnetic resonance, we consider the general problem of the coherent control of a spin coupled to a bath by means of composite or continuous pulses of duration $\tau_\mathrm{p}$. We show explicity that it is…
Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental time. We present a proof-of-concept of application of deep learning and neural network for…
The sensitivity of Magnetic Resonance Imaging (MRI) depends strongly on nuclear spin polarisation and, motivated by this observation, dynamical nuclear spin polarisation has recently been applied to enhance MRI protocols (Kurhanewicz, J.,…
Quantum sensors have attracted broad interest in the quest towards sub-micronscale NMR spectroscopy. Such sensors predominantly operate at low magnetic fields. Instead, however, for high resolution spectroscopy, the high-field regime is…
Mixed-signal neuromorphic processors provide extremely low-power operation for edge inference workloads, taking advantage of sparse asynchronous computation within Spiking Neural Networks (SNNs). However, deploying robust applications to…
Achieving atomic resolution is the ultimate limit of magnetic resonance imaging (MRI), and attaining this capability offers enormous technological and scientific opportunities, from drug development to understanding the dynamics in…
Nuclear magnetic resonance (NMR) experiments can reveal local properties in materials, but are often limited by the low signal-to-noise ratio. Spin squeezed states have an improved resolution below the Heisenberg limit in one of the spin…