| 1. |
- Bangar, H., et al.
(author)
-
Large Spin Hall Conductivity in Epitaxial Thin Films of Kagome Antiferromagnet Mn3Sn at Room Temperature
- 2022
-
In: Advanced Quantum Technologies. - : Wiley. - 2511-9044. ; 6:1
-
Journal article (peer-reviewed)abstract
- Mn3Sn is a non-collinear antiferromagnetic quantum material that exhibits a magnetic Weyl semimetallic state and has great potential for efficient memory devices. High-quality epitaxial c-plane Mn3Sn thin films have been grown on a sapphire substrate using a Ru seed layer. Using spin pumping induced inverse spin Hall effect measurements on c-plane epitaxial Mn3Sn/Ni80Fe20, spin-diffusion length (lambda(Mn3Sn)), and spin Hall conductivity (sigma(SH)) of Mn3Sn thin films are measured: lambda(Mn3Sn) = 0.42 +/- 0.04 nm and sigma(SH) = -702 h/e Omega(-1)cm(-1). While lambda(Mn3Sn) is consistent with earlier studies, sigma(SH) is an order of magnitude higher and of the opposite sign. The behavior is explained on the basis of excess Mn, which shifts the Fermi level in these films, leading to the observed behavior. These findings demonstrate a technique for engineering sigma(SH) of Mn3Sn films by employing Mn composition for functional spintronic devices.
|
|
| 2. |
- Elshaari, Ali W., et al.
(author)
-
Deterministic Integration of hBN Emitter in Silicon Nitride Photonic Waveguide
- 2021
-
In: Advanced Quantum Technologies. - : Wiley. - 2511-9044. ; 4:6, s. 2100032-
-
Journal article (peer-reviewed)abstract
- Hybrid integration provides an important avenue for incorporating atom-like solid-state single-photon emitters into photonic platforms that possess no optically-active transitions. Hexagonal boron nitride (hBN) is particularly interesting quantum emitter for hybrid integration, as it provides a route for room-temperature quantum photonic technologies, coupled with its robustness and straightforward activation. Despite the recent progress of integrating hBN emitters in photonic waveguides, a deterministic, site-controlled process remains elusive. Here, the integration of selected hBN emitter in silicon nitride waveguide is demonstrated. A small misalignment angle of 4° is shown between the emission-dipole orientation and the waveguide propagation direction. The integrated emitter maintains high single-photon purity despite subsequent encapsulation and nanofabrication steps, delivering quantum light with zero delay second order correlation function (Formula presented.). The results provide an important step toward deterministic, large scale, quantum photonic circuits at room temperature using atom-like single-photon emitters.
|
|
| 3. |
- Kudyshev, Z. A., et al.
(author)
-
Rapid Classification of Quantum Sources Enabled by Machine Learning
- 2020
-
In: Advanced Quantum Technologies. - : Wiley-VCH Verlag. - 2511-9044. ; 3:10
-
Journal article (peer-reviewed)abstract
- Deterministic nanoassembly may enable unique integrated on-chip quantum photonic devices. Such integration requires a careful large-scale selection of nanoscale building blocks such as solid-state single-photon emitters by means of optical characterization. Second-order autocorrelation is a cornerstone measurement that is particularly time-consuming to realize on a large scale. Supervised machine learning-based classification of quantum emitters as “single” or “not-single” is implemented based on their sparse autocorrelation data. The method yields a classification accuracy of 95% within an integration time of less than a second, realizing roughly a 100-fold speedup compared to the conventional Levenberg–Marquardt fitting approach. It is anticipated that machine learning-based classification will provide a unique route to enable rapid and scalable assembly of quantum nanophotonic devices.
|
|
| 4. |
- Kumar, Akash, et al.
(author)
-
Interfacial Origin of Unconventional Spin-Orbit Torque in Py/r-IrMn3
- 2023
-
In: Advanced Quantum Technologies. - 2511-9044. ; 6:7
-
Journal article (peer-reviewed)abstract
- Angle-resolved spin-torque ferromagnetic resonance measurements are carried out in heterostructures consisting of Py (Ni81Fe19) and a noncollinear antiferromagnetic quantum material r-IrMn3. The structural characterization reveals that r-IrMn3 is polycrystalline in nature. A large exchange bias of 158 Oe is found in Py/r-IrMn3 at room temperature, while r-IrMn3/Py and Py/Cu/r-IrMn3 exhibit no exchange bias. Regardless of the exchange bias and stacking sequence, a substantial unconventional out-of-plane anti-damping torque is observed when r-IrMn3 is in direct contact with Py. The magnitude of the out-of-plane spin-orbit torque efficiency is found to be twice as large as the in-plane spin-orbit torque efficiency. The unconventional spin-orbit torque vanishes when a Cu spacer is introduced between Py and r-IrMn3, indicating that the unconventional spin-orbit torque in this system originates at the interface. These findings are important for realizing efficient antiferromagnet-based spintronic devices via interfacial engineering.
|
|
| 5. |
- Olsthoorn, Bart, et al.
(author)
-
Band Gap Prediction for Large Organic Crystal Structures with Machine Learning
- 2019
-
In: Advanced Quantum Technologies. - : Wiley. - 2511-9044. ; 2:7-8
-
Journal article (peer-reviewed)abstract
- Machine‐learning models are capable of capturing the structure–property relationship from a dataset of computationally demanding ab initio calculations. Over the past two years, the Organic Materials Database (OMDB) has hosted a growing number of calculated electronic properties of previously synthesized organic crystal structures. The complexity of the organic crystals contained within the OMDB, which have on average 82 atoms per unit cell, makes this database a challenging platform for machine learning applications. In this paper, the focus is on predicting the band gap which represents one of the basic properties of a crystalline material. With this aim, a consistent dataset of 12 500 crystal structures and their corresponding DFT band gap are released, freely available for download at https://omdb.mathub.io/dataset. An ensemble of two state‐of‐the‐art models reach a mean absolute error (MAE) of 0.388 eV, which corresponds to a percentage error of 13% for an average band gap of 3.05 eV. Finally, the trained models are employed to predict the band gap for 260 092 materials contained within the Crystallography Open Database (COD) and made available online so that the predictions can be obtained for any arbitrary crystal structure uploaded by a user.
|
|
| 6. |
- Wendin, Göran, 1942
(author)
-
Can Biological Quantum Networks Solve NP-Hard Problems?
- 2019
-
In: Advanced Quantum Technologies. - : Wiley. - 2511-9044. ; 2:7-8
-
Journal article (peer-reviewed)abstract
- There is a widespread view that the human brain is so complex that it cannot be efficiently simulated by universal Turing machines, let alone ordinary classical computers. During the last decades the question has therefore been raised whether it is needed to consider quantum effects to explain the imagined cognitive power of a conscious mind. Not surprisingly, the conclusion is that quantum-enhanced cognition and intelligence are very unlikely to be found in biological brains. Quantum effects may certainly influence signaling pathways at the molecular level in the brain network, like ion ports, synapses, sensors, and enzymes. This might evidently influence the functionality of some nodes and perhaps even the overall intelligence of the brain network, but hardly give it any dramatically enhanced functionality. The conclusion is that biological quantum networks can only approximately solve small instances of nonpolynomial (NP)-hard problems. On the other hand, artificial intelligence and machine learning implemented in complex dynamical systems based on genuine quantum networks can certainly be expected to show enhanced performance and quantum advantage compared with classical networks. Nevertheless, even quantum networks can only be expected to solve NP-hard problems approximately. In the end it is a question of precision-Nature is approximate.
|
|
