Enrico Prati

TL;DR: The Anderson-Mott transition is observed at low temperatures in silicon transistors containing arrays of two, four or six arsenic dopant atoms that have been deterministically implanted along the channel of the device.

TL;DR: Deep Reinforcement Learning is applied to control coherent transport of quantum states in arrays of quantum dots and it is demonstrated that DRL can solve the control problem in the absence of a known analytical solution even under disturbance conditions.

TL;DR: In this article, the authors propose an infrastructure for read and control of qubits on a field-programmable gate array (FPGA) that supports functionality required by several qubit technologies and can operate physically close to the qubits.

TL;DR: The electronic transport on n-type silicon single electron transistors fabricated in complementary metal oxide semiconductor (CMOS) technology enables massive production of SETs for ultimate nanoelectronic and quantum variable based devices.

TL;DR: A cryogenic reconfigurable platform is proposed as the heart of the control infrastructure implementing the digital error-correction control loop and the stability is finally demonstrated by operating an integrated 1.2 GSa/s analog-to-digital converter (ADC) with a relatively stable performance over temperature.