Embedded Files
Main research directions
Main research directions
Bose-Einstein condensation in light-matter systems
Quantum information processing and neural networks
Transport properties of driven-dissipative systems
Interplay between topology, nonlinearity and quantum mechanics
Quantum size effects in strongly interacting systems
Bose-Einstein condensation in light-matter systems
Bose-Einstein condensation in light-matter systems
Bose-Einstein condensation (BEC) in light-matter systems represents a fascinating frontier in quantum physics, where bosonic particles—such as excitons, photons, or polaritons—condense into a single macroscopic quantum state at low temperatures. In conventional BEC, atoms in a dilute gas form a condensate, but in light-matter systems, this phenomenon is realized through the interaction between photons and excitons, resulting in exciton-polaritons. These quasiparticles exhibit unique properties due to their mixed light-matter nature, enabling the study of quantum phase transitions and coherence effects at higher temperatures and reduced dimensions. Exciton-polariton BEC has been observed in semiconductor microcavities, where cavity photons strongly couple to excitons in quantum wells, leading to collective behavior similar to that seen in atomic BECs. The ability to control these systems through external parameters like detuning, cavity geometry, and pump conditions has opened up new avenues for investigating novel quantum phenomena, including superfluidity, quantum coherence, and the exploration of non-Hermitian and topological phases in hybrid light-matter systems.
Quantum transport in non-Hermitian systems
Quantum transport is the phenomenon of particle transport in quantum systems. Examples of quantum transport are electron conduction in matter, light propagation in various media, or cold atom transport in artificial landscape etc. So far transport properties are overwhelmingly studied in the context of hermitian systems where particle number or energy is conserved. We recently explored the impact of non-hermitian properties in quantum transport. For instance, we have observed an anomalous feature in the localization phenomenon of exciton polaritons with finite loss, see Science Bulletin .
Moreover, non-Hermitian systems show many other intriguing properties such as parity-time symmetry-induced exceptional points, and nonreciprocal transport. Among the many fascinating features of non-Hermitian systems, one particularly intriguing phenomenon is known as the non-Hermitian skin effect (NHSE) . In this system, all states are localized at the edge, with a complex energy spectrum that is highly sensitive to the boundary conditions. While translation symmetry is crucial in the Bloch theorem for typical condensed matter systems, it dramatically breaks down in non-Hermitian systems (see for example Phys. Rev. B 111, L121301 (2025)).
We have shown that by utilizing longitudinal-transverse spin splitting and spin-momentum-locked gain, a critical non-Hermitian skin effect can be achieved in a continuous system without the need for an underlying lattice. We find that a phase transition can be induced by changing the cavity detuning with respect to the exciton energy. We identify a measurable order parameter associated with this phase transition and demonstrate the corresponding critical behavior.
Quantum bits, or qubits, are the fundamental building blocks of quantum computing, capable of encoding and processing information in ways that surpass classical systems. While several physical platforms have been explored for qubit implementation—such as superconducting circuits, trapped ions, and semiconductor quantum dots—exciton polaritons have emerged as a promising alternative due to their unique properties. The appeal of exciton polaritons for quantum computing lies in their ultra-fast coherence times, efficient nonlinear interactions, and potential scalability in semiconductor-based architectures. Their bosonic nature allows for Bose-Einstein condensation at relatively high temperatures, facilitating robust qubit manipulation and readout. Furthermore, advancements in microcavity fabrication and polariton engineering have paved the way for developing polariton-based quantum gates and networks.
Quantum information processing and neural networks are converging fields that have the potential to revolutionize computing by combining the strengths of quantum mechanics and machine learning. Quantum information processing leverages quantum phenomena such as superposition, entanglement, and quantum interference to perform computations that would be infeasible for classical computers. Neural networks, inspired by the structure and function of biological brains, are a cornerstone of modern artificial intelligence (AI). They enable machines to learn from data, recognize patterns, and make decisions in complex environments. When integrated with quantum computing, neural networks can potentially achieve exponential speedups, enabling AI models to solve problems with immense computational complexity, such as simulating molecular interactions or optimizing large-scale systems.
Traditionally, training neural networks involves adjusting the connections between neurons to establish the correct relationships between input and output data. This process, though effective, is time-consuming and resource-intensive, often requiring significant computational power. In the quantum domain, training neural networks can be particularly challenging due to the complexity of quantum systems. To address this, a novel concept known as quantum reservoir processing (QRP) has emerged. In QRP, quantum systems are used as dynamic reservoirs to perform computations, greatly simplifying the training process. Unlike classical neural networks, where training involves optimizing many parameters, QRP requires minimal adjustments. This makes training in QRP computationally easier and more efficient. Recent developments have demonstrated that QRP can perform a variety of quantum tasks, such as quantum state tomography, quantum state preparation, and even aspects of quantum computing, all with a streamlined training process. By combining the computational power of quantum systems with the efficiency of neural network processing, QRP represents a promising direction for the future of quantum machine learning, offering a powerful and resource-efficient method for solving quantum tasks.
Nonlinearity, topology and quantum
Nonlinearity, topology and quantum
Page updated
Google Sites
Report abuse