Research Interests

With burgeoning interest in quantum technology, our research focuses on utilizing graphene as a candidate for quantum computing via strain engineering. Elastic strain in graphene generates pseudo-magnetic fields, allowing Dirac fermions to be strongly localized or guided by designed strain patterns. We are highly concentrated on creating controllable qubits within these systems.

A nanobubble formed in graphene sheets can host a strong localization of Dirac fermions, acting as a quantum dot. These strain-induced single/double quantum dots are implemented to create spin or charge qubits, where quantum states are controllable via gate voltages and strain parameters. Since initialization, modulation, and operation are all electrically enabled, graphene nanobubble qubits offer a pathway to purely electrical quantum computing. Furthermore, we explore quantum interferometry (e.g., Mach-Zehnder type at p-n junctions) where pseudo-magnetic fields mimic 'real' fields, enabling ultrasensitive mechanical sensing by detecting minute phase changes induced by strain.

This theme bridges the fundamental physics of mesoscopic transport with the practical realization of nano-electronic devices. We investigate the wave-like nature of electrons in regimes where quantum interference and non-equilibrium dynamics dominate. Our work spans a wide range of platforms, from van der Waals heterostructures to complex p-n junction networks.

A key aspect of this research is providing a theoretical bridge for experimental collaborations. We specialize in simulating the current-voltage (I-V) characteristics and carrier dynamics of real-world devices, incorporating non-ideal effects such as interfacial scattering, contact resistance, and moiré potentials. By employing rigorous numerical methods like tight-binding and Green’s function formalisms, we help experimental partners interpret measurements that deviate from idealized theory. Our goal is to identify the fundamental limits of spin and valley manipulation, ensuring that new device concepts are grounded in sound physical principles.

Machine Learning (ML) serves as a cross-cutting methodology that permeates all our research areas, from graphene and topology to device modeling. However, we do not treat ML as a mere "black box" for pattern recognition; instead, we focus on interpretable AI that can provide physical insights.

We develop and apply deep learning models (e.g., CNNs, GNNs) to classify complex quantum states and automate the detection of nanostructures. A major thrust of our current work is inverse design, where we utilize ML algorithms to find the optimal strain patterns or device geometries required to achieve specific quantum functionalities. We constantly question the physical validity of AI-driven predictions, ensuring they align with established laws of physics. By bridging the gap between data-driven approaches and rigorous physical modeling, we aim to accelerate the discovery of new physical phenomena while maintaining high theoretical integrity.

As theoretical physicists, we maintain a deep-seated interest in the fundamental topological properties of electronic systems. This line of inquiry is driven by academic curiosity and the search for mathematical rigor in condensed matter physics, often independent of specific applied projects.

We investigate the origin and stability of topological invariants (such as Chern numbers and Berry curvature) in lattice systems. Our work explores how topological phase transitions are influenced by external perturbations and many-body interactions. By scrutinizing the protection of edge states and the breakdown of conventional symmetries, we seek to uncover new quantum states of matter. This fundamental research ensures that our applied work is always grounded in the most advanced theoretical frameworks of modern physics.