Report on point defect quantum systems in the presence of input-output signal and intrinsic decoherence and ambient noise
(öffnet in neuem Fenster)
A range of point defect-based structures will be fabricated. A comprehensive set of experiments will be conducted to determine their behaviour in a wide range of conditions. Results will be systematized, analysed and reported to improve the quantitative defect-based QR model (D1.4).
Project logo and website and social media accounts
(öffnet in neuem Fenster)
A project logo and website will be prepared and made available on the internet. This short report will describe the logo and website structure and main characteristics. Also, social media accounts (Twitter, LinkedIn) will be created to disseminate project news, which will be described in the short report.
Quantitative model of defect-based QR
(öffnet in neuem Fenster)
A mathematical description of a quantum reservoir (QR) structure comprising several defect-based quantum bits will be developed, under realistic assumptions about its design and its environment. Based on this description, a numerical model of the system will be built, which will allow to make quantitative predictions of the behaviour of the QR in the presence of the external signal, controls, and ambient noise. The model will be used to simulate the QR in a wide range of parameters in order to find the optimal design parameters and the regime of its operation (D3.1-3), and to train the software-implemented neural network (D4.1).
Quantitative model of superconducting QR
(öffnet in neuem Fenster)
A mathematical description of a quantum reservoir (QR) structure comprising several superconducting quantum bits will be developed, under realistic assumptions about its design and its environment. Based on this description, a numerical model of the system will be built, which will allow to make quantitative predictions of the behaviour of the QR in the presence of the external signal, controls, and ambient noise. The model will be used to simulate the QR in a wide range of parameters in order to find the optimal design parameters and the regime of its operation (D2.1-4), and to train the software-implemented neural network (D4.1).
Dissemination and communication plan - initial version
(öffnet in neuem Fenster)
A report documenting the Dissemination and Communication Plan over the course of the project.
