Integrated superconducting nanowire single photon detecors on SiN waveguides with photon number resolution capability for photonic quantum computing
Among the various qubit technologies, photonic qubits represent a highly promising approach to the development of quantum computers capable of solving problems beyond the reach of the most powerful supercomputers. The core of photonic quantum computing processors relies on the generation, manipulation, and detection of single photons. In this context, this PhD project at CEA-LETI aims to develop a new generation of high-performance single-photon detectors, offering not only high detection efficiency and low noise, but also photon-number-resolving capabilities, i.e., the ability to discriminate between 0, 1, 2, or more incident photons. This functionality is essential for implementing quantum error correction codes and ultimately enabling the transition from physical qubits to logical qubits. The research will focus on the design of innovative detector circuit architectures, the fabrication of integrated prototypes in CEA-LETI's 200 mm cleanroom, and their experimental characterization at cryogenic temperatures. This work will contribute to the development of a strategic technology for next-generation photonic quantum computers.
Backpropagation-Free Langevin Sampling for Bayesian Learning on ReRAM Architectures
Bayesian neural networks allow artificial intelligence systems to account for uncertainty in their predictions, but training them requires substantial computational resources. Resistive memories (ReRAM), which can both store data and perform computations directly within electronic circuits, offer a promising solution to reduce this cost.
This PhD project aims to develop a new Bayesian learning method on ReRAM hardware, based on Langevin sampling. This approach efficiently explores neural network parameters by combining random perturbations with gradient estimates. The objective is to exploit the intrinsic physical variability of ReRAM devices to estimate these gradients without relying on conventional backpropagation. The possibility of reusing memory programming attempts will also be investigated to reduce energy consumption and device wear.
The work will combine algorithm development, simulations, and experiments on existing ReRAM circuits at CEA-Leti. Expected outcomes include a demonstration of Bayesian learning on real hardware and an assessment of its performance, accuracy, and energy efficiency compared with existing approaches.
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.