Quantum Devices tagged posts

New Study uses Machine Learning to Bridge the Reality Gap in Quantum Devices

New study uses machine learning to bridge the reality gap in quantum devices
(a) Device geometry including the gate electrodes (labeled G1–G8), donor ion plane, and an example disorder potential experienced by confined electrons. Typical flow of current from source to drain is indicated by the white arrow. (b) Schematic of the disorder inference process. Colors indicate the following: red for experimentally controllable variables, green for quantities relevant to the electrostatic model, blue for experimental device, and yellow for machine learning methods. Dashed arrows represent the process of generating training data for the deep learning approximation and are not part of the disorder inference process. Credit: Physical Review X (2024). DOI: 10.1103/PhysRevX.14.011001

A study led by the University of Oxford has used the power of machine learning to ove...

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Research reveals Rare Metal could offer Revolutionary Switch for Future Quantum Devices

Image shows a representation of emergent symmetry, showing a perfectly symmetric water droplet emerging from a layering of snow. The ice crystals in the snow, by contrast, have a complex shape and therefore a lower symmetry than the water droplet. The purple colour denotes the purple bronze material in which this phenomenon was discovered.University of Bristol

Quantum scientists have discovered a rare phenomenon that could hold the key to creating a ‘perfect switch’ in quantum devices which flips between being an insulator and superconductor.

The research, led by the University of Bristol and published in Science, found these two opposing electronic states exist within purple bronze, a unique one-dimensional metal composed of individual conducting chains of atoms.

Tiny changes in...

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Machine Learning Models Quantum Devices

Quantum reservoir computing. B and F represent the input and output states, respectively, of a quantum system. E is an auxiliary system necessary to pass the sequence of input states B to the quantum reservoir S. S can then be read to emulate F without disrupting the system. ©2021 Tran et al.

A novel algorithm allows for efficient and accurate verification of quantum devices. Technologies that take advantage of novel quantum mechanical behaviors are likely to become commonplace in the near future. These may include devices that use quantum information as input and output data, which require careful verification due to inherent uncertainties. The verification is more challenging if the device is time dependent when the output depends on past inputs...

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