Development of multiplexed photon sources for quantum technologies
Quantum information technologies offers several promises in domains such as computation or secured communications. There is a wide variety of technologies available, including photonic qubits. The latter are robust against decoherence and are particularly interesting for quantum communications applications, even at room temperature. They also offers an alternative to other qubits technologies for quantum computing. For the large-scale deployment of those applications, it is necessary to have cheap, compact and scalable devices. To reach this goal, silicon photonics platform is attractive. It allows implementing key components such as generation, manipulation and detection of photonic qubits.
Solid-state photon generation may occur with different physical processes. Among those, the non-linear photon pair generation has several benefits, such as working at room temperature, the ability to generate heralded single photon, or entangled photon pairs…
You will work on multiplexed parametric photon pair sources in order to surpass the inherent limits of the physical process for generating photon pairs. This will include the development, the fabrication monitoring, and the characterization in the laboratory. In the goal of a full integration on chip, it is necessary to be able to filter effectively unwanted light, in order to keep only photons of interest.
Combined Software and Hardware Approaches for Large Scale Sparse Matrix Acceleration
Computational physics, artificial intelligence and graph analytics are important compute problems which depend on processing sparse matrices of huge dimensions. This PhD thesis focuses on the challenges related to efficiently processing such sparse matrices, by applying a systematic software are hardware approach.
Although the processing of sparse matrices has been studied from a purely software perspective for decades, in recent years many dedicated, and very specific hardware, accelerators for sparse data have been proposed. What is missing is a vision of how to properly exploit these accelerators, as well as standard hardware such as GPUs, to efficiently solve a full problem. Prior to solving a matrix problem, it is common to perform pre-processing of the matrix. This can include techniques to improve the numerical stability, to adjust the form of the matrix, and techniques to divide it into smaller sub-matrices (tiling) which can be distributed to processing cores. In the past, this pre-processing has assumed homogenous compute cores. New approaches are needed, to take advantage of heterogeneous cores which can include dedicated accelerators and GPUs. For example, it may make sense to dispatch the sparsest regions to specialized accelerators and to use GPUs for the denser regions, although this has yet to be shown. The purpose of this PhD thesis is to take a broad overview of the processing of sparse matrices and to analyze what software techniques are required to exploit existing and future accelerators. The candidate will build on an existing multi-core platform based on RISC-V cores and an open-source GPU to develop a full framework and will study which strategies are able to best exploit the available hardware.
CORTEX: Container Orchestration for Real-Time, Embedded/edge, miXed-critical applications
This PhD proposal will develop a container orchestration scheme for real-time applications, deployed on a continuum of heterogeneous computing resources in the embedded-edge-cloud space, with a specific focus on applications that require real-time guarantees.
Applications, from autonomous vehicles, environment monitoring, or industrial automation, applications traditionally require high predictability with real-time guarantees, but they increasingly ask for more runtime flexibility as well as a minimization of their overall environmental footprint.
For these applications, a novel adaptive runtime strategy is required that can optimize dynamically at runtime the deployment of software payloads on hardware nodes, with a mixed-critical objective that combines real-time guarantees with the minimization of the environmental footprint.
Study of the stability of Si-CMOS Structures for the implementation of Spin Qubits
Silicon-based spin qubits in CMOS structures stand out for their compatibility with semiconductor technologies and their scalability potential. However, impurities and defects introduced during fabrication lead to noise and instability, which affect their performance.
The objective is to characterize devices fabricated at CEA-Leti, from room temperature to cryogenic temperatures, to evaluate their quality and understand the physical mechanisms responsible for their instability. The goal is to improve the design of the devices and ideally establish a method to identify the most promising devices without requiring measurements at very low temperatures.
The candidate should have skills in the following areas:
- Experimental physics and semiconductors.
- Algorithm programming and data analysis.
- Knowledge in nanofabrication, low-temperature physics, and quantum physics (desirable).
Theoretical design of quasi-atomic systems in the band gap of semiconductors/insulators for quantum application
The rise of room-temperature applications like single photon emission of the negatively charged nitrogen-vacancy NV center in diamond has renewed the interest in the search for materials having a quasi-atomic system QAS analogous to that of NV, mainly characterized by the presence of well localized in-gap defect levels generate occupied by electrons and leading to high spin states. In this Ph.D. work, theoretical methods will be used to design new QASs analogous to the NV center as well as, in selected QAS, to predict charge states and explore the effect of the proximity of the surface on the thermodynamic stability and on the spin state structure. The objectives are to design new QASs; To predict charge states of selected QASs in the bulk of the host material; To study changes in the charge state brought by the proximity of the surface; To extend the Hubbard model used to compute the excited states and to account for the electron-lattice interaction in the calculation of the excited states; To study the effect of the presence of deep level states in the band gap on the transport of electrons and phonons. The methodology developed at LSI to design new QASs with high spin states will be exploited and new systems analogous to the NV center will be looked for. Density functional theory (DFT) and a Hubbard model developed at LSI will be the main tools of this PhD.
In-Sensor Computing for MEMS Sensors: Toward an Electromechanical Neural Network
The rise of machine learning models for processing sensor data has led to the development of Edge-AI, which aims to perform these data processing tasks locally, directly at the sensor level. This approach reduces the amount of data transmitted and eases the load on centralized computing centers, providing a solution to decrease the overall energy consumption of systems. In this context, the concept of in-sensor computing has emerged, integrating data acquisition and processing within the sensor itself. By leveraging the physical properties of sensors and alternative computing paradigms, such as reservoir computing and neuromorphic computing, in-sensor computing eliminates the energy-intensive steps of signal conversion and processing.
Applying this concept to MEMS sensors enables the processing of signals such as acceleration, strain, or acoustic signals, with a significant reduction, or even elimination, of traditional electronic components. This has rekindled interest in mechanical computing devices and their integration into MEMS sensors like microphones and accelerometers. Recent research explores innovative MEMS devices integrating recurrent neural networks or reservoir computing, showing promising potential for energy efficiency. However, these advancements are still limited to proof-of-concept demonstrations for simple classification tasks with a very low number of neurons.
Building on our expertise in MEMS-based computing, this doctoral work aims to push these concepts further by developing a MEMS device that integrates a reprogrammable neural network with learning capabilities. The objective is to design an intelligent sensor that combines detection and preprocessing on a single chip, optimized to operate with extremely low energy consumption, in the femtoJoule range per activation. This thesis will focus on the design, fabrication, and validation of this new device, targeting low-frequency signal processing applications in high-temperature environments, paving the way for a new generation of intelligent and autonomous sensors.
Quantum computing with nuclear spins
Nuclear spins in solids are amongst the quantum systems with the longest coherence times, up to minutes or even hours, and as such are attractive qubit candidates; however, controlling and reading out individual nuclear spins is highly challenging. In our laboratory, we have developed a new way to do so. The nuclear spin qubits are interfaced by an electron spin ancilla to which they are coupled by the hyperfine interaction. The electron spin is then measured by microwave photon counting at millikelvin temperatures [1,2]. Nuclear-spin single-shot readout is performed via the electron spin [3], and coherent control is achieved through the use of microwave Raman transitions [4]. The electron spins are Er3+ ions in a CaWO4 crystal, and the nuclear spins are 183W atoms in the matrix, which have a spin 1/2.
[1] E. Albertinale et al., Nature 600, 434 (2021)
[2] Z. Wang et al., Nature 619, 276 (2023)
[3] J. Travesedo et al., arxiv (2024)
[4] J. O'Sullivan et al., arxiv (2024)
Modelling spin shuttling in Si and Ge spin qubits
Silicon and Germanium spin qubits have made outstanding progress in the past few years. In these devices, the elementary information is stored as a coherent superposition of the spin states of an electron or hole confined in a quantum dot embedded in a Si/SiO2 or SiGe heterostructure. These spins can be manipulated electrically and are entangled through exchange interactions, allowing for a variety of one- and two-qubit gates required for quantum computing and simulation. Grenoble is promoting original spin qubit platforms based on Si and Ge, and holds various records in spin lifetimes and spin-photon interactions. At CEA/IRIG, we support the progress of these quantum technologies with state-of-the-art modelling. We are, in particular, developing the TB_Sim code, able to describe very realistic qubit structures down to the atomic scale if needed.
Spin shuttling has emerged recently as a resource for spin manipulation and transport. A carrier and its spin can indeed be moved (shuttled) coherently between quantum dots, allowing for the transport of quantum information on long ranges and for the coupling between distant spins. The shuttling dynamics is however complex owing to the spin-orbit interactions that couple the motion of the carrier to its spin. This calls for a comprehensive understanding of these interactions and of their effects on the evolution and coherence of the spin. The aim of this PhD is to model shuttling between Si/Ge spin qubits using a combination of analytical and numerical (TB_Sim) techniques. The project will address spin manipulation, transport and entanglement in arrays of spin qubits, as well as the response to noise and disorder (decoherence). The PhD candidate will have the opportunity to interact with a lively community of experimentalists working on spin qubits at CEA and CNRS.