Superconducting qubit readout using a multi-stage quantum amplifier readout chain
The current available superconducting quantum processors (IBM, Google,…), operated at temperatures down to 20 mK (-273,13 °C), have a limited amount of quantum bits (qubits) because of the difficulty to have compact readout chains allowing to measure several qubits simultaneously. The current cryogenic amplifiers used for superconducting qubit readout are based on semiconductor technologies, which limits their performances because of their electrical resistance. Moreover, they are powered with DC-current and have to be operated at elevated temperature (4 K) to dissipate the heat they produce.
The topic of this PhD is to design a new kind of cryogenic amplifiers, called parametric amplifiers, based on superconducting materials in order to replace semiconductor-based ones. The designed amplifier will be fabricated in the industrial-grade cleanroom of CEA-Leti, and further used to measure our superconducting qubits.
Model-Based Systems Engineering approach for the automation and optimization of qubit control architecture exploration
Designing the system architecture to control millions of qubits in a quantum computer is a complex challenge, driven by strict constraints spanning heterogeneous technical domains (microelectronics, quantum information, thermics). Addressing this complexity requires dedicated formalisms and tools to enable automated design and optimization. Where traditional approaches remain siloed, Model-Based Systems Engineering (MBSE) and the SysML v2 standard modeling language provide a unique integration framework for analyzing and exploring complex multi-physics design spaces.
To capture these architectures within SysML v2 models, this research aims to adapt this modeling language to the quantum domain by developing dedicated extensions (libraries and metadata).
Based on these models, this work will explore how reference quantum or hybrid algorithms impact these architectures, particularly through resource estimation for these applications and the applied error-correction mechanisms.
The PhD thesis intends to develop a methodology for automating and optimizing architectural exploration. To this end, it will investigate the adaptation of operational research algorithms for design space exploration, incorporating sensitivity analyses, constraint relaxation heuristics, and physical uncertainties.
This research aims to identify the most influential parameters and provide new insights capable of guiding efforts toward the most promising R&D avenues.
The thesis will be carried out at CEA Grenoble.