Charge-state transitions of muonium in germanium

1999 ◽  
Vol 60 (3) ◽  
pp. 1734-1745 ◽  
Author(s):  
R. L. Lichti ◽  
S. F. J. Cox ◽  
K. H. Chow ◽  
E. A. Davis ◽  
T. L. Estle ◽  
...  
Keyword(s):  
Charge State ◽  
State Transitions

Charge-state transitions of muonium in 6H silicon carbide

2007 ◽  
Vol 401-402 ◽  
pp. 631-634 ◽  
Author(s):  
H.N. Bani-Salameh ◽  
A.G. Meyer ◽  
B.R. Carroll ◽  
R.L. Lichti ◽  
Y.G. Celebi ◽  
...  
Keyword(s):  
Silicon Carbide ◽  
Charge State ◽  
State Transitions

scholarly journals Miniaturizing neural networks for charge state autotuning in quantum dots

2021 ◽  
Vol 3 (1) ◽  
pp. 015001
Author(s):  
Stefanie Czischek ◽  
Victor Yon ◽  
Marc-Antoine Genest ◽  
Marc-Antoine Roux ◽  
Sophie Rochette ◽  
...  
Keyword(s):  
Neural Networks ◽  
Quantum Dots ◽  
Charge State ◽  
Synthetic Data ◽  
Machine Learning Techniques ◽  
High Dimensional ◽  
State Transitions ◽  
Stability Diagrams ◽  
On Chip ◽  
Memristor Crossbar

Abstract A key challenge in scaling quantum computers is the calibration and control of multiple qubits. In solid-state quantum dots (QDs), the gate voltages required to stabilize quantized charges are unique for each individual qubit, resulting in a high-dimensional control parameter space that must be tuned automatically. Machine learning techniques are capable of processing high-dimensional data—provided that an appropriate training set is available—and have been successfully used for autotuning in the past. In this paper, we develop extremely small feed-forward neural networks that can be used to detect charge-state transitions in QD stability diagrams. We demonstrate that these neural networks can be trained on synthetic data produced by computer simulations, and robustly transferred to the task of tuning an experimental device into a desired charge state. The neural networks required for this task are sufficiently small as to enable an implementation in existing memristor crossbar arrays in the near future. This opens up the possibility of miniaturizing powerful control elements on low-power hardware, a significant step towards on-chip autotuning in future QD computers.

scholarly journals Electrical charge state identification and control for the silicon vacancy in 4H-SiC

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
M. E. Bathen ◽  
A. Galeckas ◽  
J. Müting ◽  
H. M. Ayedh ◽  
U. Grossner ◽  
...  
Keyword(s):  
Charge State ◽  
Electric Fields ◽  
Single Photon ◽  
Photon Emission ◽  
State Transitions ◽  
Single Photon Emission ◽  
Electrical Measurements ◽  
Complete Control ◽  
Silicon Vacancy ◽  
External Bias

AbstractReliable single-photon emission is crucial for realizing efficient spin-photon entanglement and scalable quantum information systems. The silicon vacancy ($${V}_{{\rm{Si}}}$$VSi) in 4H-SiC is a promising single-photon emitter exhibiting millisecond spin coherence times, but suffers from low photon counts, and only one charge state retains the desired spin and optical properties. Here, we demonstrate that emission from $${V}_{{\rm{Si}}}$$VSi defect ensembles can be enhanced by an order of magnitude via fabrication of Schottky barrier diodes, and sequentially modulated by almost $$50 \%$$50% via application of external bias. Furthermore, we identify charge state transitions of $${V}_{{\rm{Si}}}$$VSi by correlating optical and electrical measurements, and realize selective population of the bright state. Finally, we reveal a pronounced Stark shift of 55 GHz for the V1′ emission line state of $${V}_{{\rm{Si}}}$$VSi at larger electric fields, providing a means to modify the single-photon emission. The approach presented herein paves the way towards obtaining complete control of, and drastically enhanced emission from, $${V}_{{\rm{Si}}}$$VSi defect ensembles in 4H-SiC highly suitable for quantum applications.

scholarly journals Non-adiabatic charge state transitions in singlet–triplet qubits

2013 ◽  
Vol 15 (10) ◽  
pp. 103015 ◽  
Author(s):  
Tuukka Hiltunen ◽  
Juha Ritala ◽  
Topi Siro ◽  
Ari Harju
Keyword(s):  
Charge State ◽  
State Transitions

Macroorganisation and flexibility of thylakoid membranes

2019 ◽  
Vol 476 (20) ◽  
pp. 2981-3018 ◽  
Author(s):  
Petar H. Lambrev ◽  
Parveen Akhtar
Keyword(s):  
Light Harvesting ◽  
Current Knowledge ◽  
Three Dimensional ◽  
Photosynthetic Apparatus ◽  
Dynamic Responses ◽  
State Transitions ◽  
Photochemical Quenching ◽  
Light Harvesting Complex ◽  
Complex Ii ◽  
Light Harvesting Complex Ii

Abstract The light reactions of photosynthesis are hosted and regulated by the chloroplast thylakoid membrane (TM) — the central structural component of the photosynthetic apparatus of plants and algae. The two-dimensional and three-dimensional arrangement of the lipid–protein assemblies, aka macroorganisation, and its dynamic responses to the fluctuating physiological environment, aka flexibility, are the subject of this review. An emphasis is given on the information obtainable by spectroscopic approaches, especially circular dichroism (CD). We briefly summarise the current knowledge of the composition and three-dimensional architecture of the granal TMs in plants and the supramolecular organisation of Photosystem II and light-harvesting complex II therein. We next acquaint the non-specialist reader with the fundamentals of CD spectroscopy, recent advances such as anisotropic CD, and applications for studying the structure and macroorganisation of photosynthetic complexes and membranes. Special attention is given to the structural and functional flexibility of light-harvesting complex II in vitro as revealed by CD and fluorescence spectroscopy. We give an account of the dynamic changes in membrane macroorganisation associated with the light-adaptation of the photosynthetic apparatus and the regulation of the excitation energy flow by state transitions and non-photochemical quenching.

Hysteresis in spectral state transitions – a challenge for theoretical modeling

2005 ◽  
Vol 432 (1) ◽  
pp. 181-187 ◽  
Author(s):  
E. Meyer-Hofmeister ◽  
B. F. Liu ◽  
F. Meyer
Keyword(s):  
Theoretical Modeling ◽  
State Transitions ◽  
Spectral State
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