Related papers: In situ Learning-Based Spin Engineering of Pulsed …
A sensitivity increase of two orders of magnitude in proton (1H) and carbon (13C) spins via dynamic nuclear polarization (DNP) has been accomplished recently using a compact benchtop DNP polarizer operating at 1 T and 77 K. However the DNP…
This paper presents a deep learning strategy to simultaneously solve Partial Differential Equations (PDEs) and back-calculate their parameters in the context of deep tunnel excavation. A Physics-Informed Neural Network (PINN) model is…
Identifying weakly coupled nuclear spins around single electron spins is a key step of implementing quantum information processing using coupled electron-nuclei spin systems or sensing like single spin nuclear magnetic resonance detection…
One of solution-state Nuclear Magnetic Resonance (NMR)'s main weaknesses, is its relative insensitivity. J-driven Dynamic Nuclear Polarization (JDNP) was recently proposed for enhancing the sensitivity of solution-state NMR, by bypassing…
The theoretical shapes of nuclear spin-noise spectra in NMR are derived by considering a receiver circuit with finite, preamplifier input impedance and a transmission line between the preamplifier and the probe. Using this model, it becomes…
We report spin-selective tunneling of electrons along natural and artificial double-stranded DNA (dsDNA) sandwiched by nonmagnetic leads. The results reveal that the spin polarization strongly depends on the dsDNA sequence and is dominated…
We propose a new approach to the measurement of a single spin state, based on nuclear magnetic resonance (NMR) techniques and inspired by the coherent control over many-body systems envisaged by Quantum Information Processing (QIP). A…
Nuclear magnetic resonance (NMR) schemes can be applied to micron-, and nanometer-sized samples by the aid of quantum sensors such as nitrogen-vacancy (NV) color centers in diamond. These minute devices allow for magnetometry of nuclear…
We investigate analytically and numerically the Multiple Quantum (MQ) NMR dynamics in dipolar ordered spin systems of nuclear spins 1/2 at a low temperatures. We suggest two different methods of MQ NMR. One of them is based on the…
Learning-based methods have made significant progress in physics simulation, typically approximating dynamics with a monolithic end-to-end optimized neural network. Although these models offer an effective way to simulation, they may lose…
The detection of nuclear spins using individual electron spins has enabled new opportunities in quantum sensing and quantum information processing. Proof-of-principle experiments have demonstrated atomic-scale imaging of nuclear-spin…
We investigate the dynamic nuclear polarization from the hyperfine interaction between nonequilibrium electronic spins and nuclear spins coupled to them in semiconductor nanostructures. We derive the time and position dependence of the…
We study the mechanism of dynamical nuclear spin polarization by hyperfine interaction in spin-blocked double quantum dot system. We calculate the hyperfine transition rates and solve the master equations for the nuclear spins.…
Neural network potentials (NNPs) enable large-scale molecular dynamics (MD) simulations of systems containing >10,000 atoms with the accuracy comparable to ab initio methods and play a crucial role in material studies. Although NNPs are…
Recent progress in the application of color centers to nanoscale spin sensing makes the combined use of noise spectroscopy and scanning probe imaging an attractive route for the characterization of arbitrary material systems. Unfortunately,…
Magnetic noise of atomic nuclear spins is a major problem for solid state spin qubits. Highly-polarized nuclei would not only overcome this obstacle, but also make nuclear spins a useful quantum information resource. However, achieving…
Amplification of a single spin state using nuclear magnetic resonance (NMR) techniques in a rotating frame is considered. The main aim is to investigate the efficient of various schemes for quantum detection. Results of numerical simulation…
Soon after the first measurements of nuclear magnetic resonance (NMR) in a condensed matter system, Bloch predicted the presence of statistical fluctuations proportional to $1/\sqrt{N}$ in the polarization of an ensemble of $N$ spins. First…
Localized spins in the solid state are attracting widespread attention as highly sensitive quantum sensors with nanoscale spatial resolution and fascinating applications. Recently, adaptive measurements were used to improve the dynamic…
As a result of the growing size of Deep Neural Networks (DNNs), the gap to hardware capabilities in terms of memory and compute increases. To effectively compress DNNs, quantization and connection pruning are usually considered. However,…