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)
Magnetic DIsks as Transducer of Angular Momentum
The proposed topic is a collaborative project to exploit suspended magnetic disks as novel microwave transducers of orbital angular momentum. Our goal is to develop ultra-high fidelity opto-mechanical modulators operating at GHz frequencies by integrating magnetic materials into optical components. This innovative concept arises from recent progress in the study of angular momentum conservation laws by magnon modes in axi-symmetric cavities, leading to new opportunities to develop a more frugal, agile, and sustainable communications technology. Our proposed design has the potential to achieve coherent interconversion between the microwave frequency range in which wireless networks or quantum computers operate and optical network frequencies, which is the optimal frequency range for long-distance communications. In this regard, our proposal not only proposes new applications of magnonics to the field of optics not previously envisioned, but also builds a bridge between the spintronics and the electronic and quantum communities.
In this proposal, the elastic deformations are generated by the magnetization dynamics through the magneto-elastic tensor and its contactless coupling to a microwave circuit. We have shown that coherent coupling between magnons and phonons can be achieved by precisely tuning the magnetic resonance degenerate with a selected elastic mode via the application of an external magnetic field. We expect to achieve ultra-high fidelity conversion by focusing our study on micron-sized single crystal magnetic garnet structures integrated with GaAs photonic waveguides or cavities. In addition, we propose the fabrication of suspended cavities as a means to minimize further energy leakage (elastic or optical) through the substrate.
The first challenge is to produce hybrid materials that integrate high quality garnet films with semiconductors. We propose a radically new approach based on micron-thick magnetic garnet films grown by liquid phase epitaxy (LPE) on a gadolinium-gallium-garnet (GGG) substrate. The originality is to bond the flipped film to a semiconductor wafer and then remove most of the the GGG substrate by mechanical polishing. The resulting multi-layer is then processed using standard lithography techniques, taking advantage of the relative robustness of garnet materials to chemical, thermal or milling processes.
The second challenge is to go beyond the excitation of uniform modes and target modes with orbital angular momentum as encoders of arbitrarily large quanta of nJ? for mode multiplexed communication channels or multi-level quantum state registers. The project will take advantage of recent advances in spin-orbit coupling between azimuthal spin waves as well as elastic scattering of magnons on anisotropic magneto-crystalline tensors. In this project, we also want to go beyond uniformly magnetized state and exploit the ability to continuously morph the equilibrium magnetic texture in the azimuthal direction as a means of engineering the selection rules and thus coherently access otherwise hidden mode symmetries.
Brines for metal recycling
Critical metals are essential for a range of technologies that are vital to reduce our carbon dioxide emissions. However, the global recycling rate for metals contained in electronic waste is below 20%, indicating that electronic waste is a relatively untapped source of metals. Additionally, it is increasingly urgent to develop effective processes for recycling waste from products like solar panels, as the volume of waste solar pannels generated is set to rise significantly in the coming years. Currently, conventional hydrometallurgical methods often rely on toxic aqueous solutions to dissolve metals, which presents substantial environmental challenges.
This project proposes an innovative alternative by using concentrated brines (aqueous salt solutions) to oxidize and dissolve metals. This thesis will investigate the fundamental properties of brines and their ability to dissolve metals through various techniques, particularly electrochemical methods. Artificial intelligence methods developed within the lab will be employed to screen a wide range of brines that would allow metal dissolution. Subsequently, brine-based recycling processes will be developed to recover metals from printed circuit boards and solar panels. Finally, metal separation and the treatment of used brines will be explored using membrane and electrochemical processes.
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.
Understanding the mechanisms of direct CO2 hydrogenation using (Na,K)FeOx catalysts via theoretical-experimental coupling
In the context of climate change, we need to reduce our CO2 emissions by using less energy. Another approach is to capture, store and use CO2, with the aim of moving towards a circular carbon economy and, ultimately, defossilization. With this in mind, the direct hydrogenation of CO2 enables it to be transformed into molecules of interest such as hydrocarbons, via the coupling of the reverse water gas shift (RWGS) reaction and Fischer-Tropsch synthesis (FTS).
Computational operando catalysis has recently emerged as a reasoned alternative to the development of new catalysts, thanks to a multi-scale approach from the atom down to the active particle, to model catalyst selectivity and activity. New tools combining ab initio simulations (DFT) and molecular dynamics (MD) via machine learning algorithms bridge the gap between the precision of DFT calculations and the power of atomistic simulations. Current bifunctional catalysts (active for RWGS, and FTS) for direct CO2 hydrogenation are based on doped iron oxides (metal promoters).
The aim of this project is the theoretical study of Na-FeOx and K-FeOx catalysts doped with Cu, Mn, Zn and Co, in 4 stages: DFT simulations (adsorption energies, density of states, energy barriers, transition states), microkinetic modeling (reaction constants, TOF), construction of interatomic potentials by DFT/machine learning coupling, simulation of whole particles (selectivity, activity, microscopic quantities).
This theoretical study will go hand in hand with the synthesis and experimental measurements of the studied catalysts, and optimized catalysts emerging from the computational results. All the accumulated data (DFT, MD, catalytic properties) will be fed into a database, which can eventually be exploited to identify descriptors of interest for CO2 hydrogenation.
Magneto-ionic gating of magnetic tunnel junctions for neuromorphic applications
Magneto-ionics is an emerging field that offers great potential for reducing power consumption in spintronics memory applications through non-volatile control of magnetic properties through gating. By combining the concept of voltage-controlled ionic motion from memristor technologies, typically used in neuromorphic applications, with spintronics, this field also provides a unique opportunity to create a new generation of neuromorphic functionalities based on spintronics devices.
The PhD will be an experimental research project focused on the implementation of magneto-ionic gating schemes in magnetic tunnel junction’s spintronics devices. The ultimate goal of the project is to obtain reliable and non-volatile gate-control over magnetisation switching in three-terminal magnetic tunnel junctions.
One major challenge remains ahead for the use of magneto-ionics in practical applications, its integration into magnetic tunnel junctions (MTJ), the building blocks of magnetic memory architectures. This will not only unlock the dynamic control of switching fields/currents in magnetic tunnel junctions to reduce power consumption, but also allow for the control of stochasticity, which has important implications in probabilistic computing.
Towards a better understanding of membrane proteins through AI
Despite the remarkable advances in artificial intelligence (AI), particularly with tools like AlphaFold, the prediction of membrane protein structures remains a major challenge in structural biology. These proteins, which represent 30% of the proteome and 60% of therapeutic targets, are still significantly underrepresented in the Protein Data Bank (PDB), with only 3% of their structures resolved. This rarity is due to the difficulty in maintaining their native state in an amphiphilic environment, which complicates their study, especially with classical structural techniques.
This PhD project aims to overcome these challenges by combining the predictive capabilities of AlphaFold with experimental small-angle scattering (SAXS/SANS) data obtained under physiological conditions. The study will focus on the translocator protein TSPO, a key marker in neuroimaging of several serious pathologies (cancers, neurodegenerative diseases) due to its strong affinity for various pharmacological ligands.
The work will involve predicting the structure of TSPO, both in the presence and absence of ligands, acquiring SAXS/SANS data of the TSPO/amphiphile complex, and refining the models using advanced modeling tools (MolPlay, Chai-1) and molecular dynamics simulations. By deepening the understanding of TSPO’s structure and function, this project could contribute to the design of new ligands for diagnostic and therapeutic purposes.
Design and fabrication of neuromorphic circuit based on lithium-iontronics devices
Neural Networks (NNs) are inspired by the brain’s computational and communication processes to efficiently address tasks such as data analytics, real time adaptive signal processing, and biological system modelling. However, hardware limitations are currently the primary obstacle to widespread adoption. To address this, a new type of circuit architecture called "neuromorphic circuit" is emerging. These circuits mimic neuron behaviour by incorporating high parallelism, adaptable connectivity, and in memory computation. Ion gated transistors have been extensively studied for their potential to function as artificial neurons and synapses. Even if these emerging devices exhibit excellent properties due to their ultra low power consumption and analog switching capabilities, they still need to be validated into larger systems.
At the RF and Energy Components Laboratory of CEA-Leti, we are developing new lithium-gated transistors as building blocks for deploying low-power artificial neural networks. After an initial optimization phase focused on materials and design, we are ready to accelerate the pace of development. These devices now need to be integrated into a real system to assess their actual performance and potential. In particular, both bio-inspired circuits and crossbar architectures for accelerated computation will be targeted.
During this 3-year PhD thesis, your (main) objective will be to design, implement, and test neural networks based on lithium-gated transistor crossbars (5x5, 10x10, 20x20) and neuromorphic circuits , along with the CMOS read and write logic to control them. The networks might be implemented using different algorithms and architectures, including Artificial Neural Network, Spiking Neural Networks and Recurrent Neural Networks, which will be then tested by solving spatial and/or temporal pattern recognition problems and reproduce biological functions such as pavlovian conditioning.
Embedded local blockchain on secure physical devices
The blockchain is based on a consensus protocol, the aim of which is to share and replicate ordered data between peers in a distributed network. The protocol stack, embedded in the network's peer devices, relies on a proof mechanism that certifies the timestamp and ensures a degree of fairness within the network.
The consensus protocols used in the blockchains deployed today are not suitable for embedded systems, as they require too many communication and/or computing resources for the proof. A number of research projects, such as IOTA and HashGraph, deal with this subject and will be analysed in the state of the art.
The aim of this thesis is to build a consensus protocol that is frugal in terms of communications and computing resources, and whose protocol stack will be implemented in a secure embedded device. This protocol must be based on the proof of elapsed time from our laboratory's work, which is also frugal, called Proof-of-Hardware-Time (PoHT), and must satisfy the properties of finality and fairness. The complete architecture of a peer node in the network will be designed and embedded on an electronic board including a microprocessor and several hardware security components, in such a way that the proof resource cannot be parallelized. Communication between peers will be established in a distributed manner.
Advancing Semantic Representation, Alignment, and Reasoning in Multi-Agent 6G Communication Systems
Semantic communications is an emerging and transformative research area, where the focus shifts from transmitting raw data to conveying meaningful information. While initial models and design solutions have laid foundational principles, they often rest on strong assumptions regarding the extraction, representation, and interpretation of semantic content. The advent of 6G networks introduces new challenges, particularly with the growing need for multi-agent systems where multiple AI-driven agents interact seamlessly.
In this context, the challenge of semantic alignment becomes critical. Existing literature on multi-agent semantic communications frequently assumes that all agents share a common understanding and interpretation framework, a condition rarely met in practical scenarios. Misaligned representations can lead to communication inefficiencies, loss of critical information, and misinterpretations.
This PhD research aims to advance the state-of-the-art by investigating the principles of semantic representation, alignment, and reasoning in multi-AI agent environments within 6G communication networks. The study will explore how agents can dynamically align their semantic models, ensuring consistent interpretation of messages while accounting for differences in context, objectives, and prior knowledge. By leveraging techniques from artificial intelligence, such as machine learning, ontology alignment, and multi-agent reasoning, the goal is to propose novel frameworks that enhance communication efficiency and effectiveness in multi-agent settings. This work will contribute to more adaptive, intelligent, and context-aware communication systems that are key to the evolution of 6G networks.