arXiv subjects

Elif Çetiner

Publications and source records attributed to Elif Çetiner.

4 recordsLinked to original sources

arXiv2025

Ten-valley excitonic complexes in charge-tunable monolayer WSe$_2$

Excitons dominate the optical response of two-dimensional (2D) semiconductors. Strong interactions produce peculiar excitonic complexes, which provide a testing ground for exciton and quantum many-body theories. Here, we report a hitherto unobserved many-body exciton that emerges upon filling both the K and Q valleys of WSe$_2$. We optically probe the exciton landscape using charge-tunable devices with unusually thin dielectrics that facilitate doping up to several $10^{13}$ cm$^{-2}$. We observe the emergence of the thermodynamically stable complex when 10 valleys are electrostatically filled. We gain insight into its physics using magneto-optical measurements. Our results are well-described by a model where the number of distinguishable Fermi seas interacting with the photoexcited electron-hole pair defines the complex's behavior. In addition to expanding the repertoire of excitons in 2D semiconductors, this complex could probe the limit of exciton models and answer open questions about screened Coulomb interactions in 2D semiconductors.

cond-mat.mes-hall↗

arXiv2025

Engineering strong correlations in a perfectly aligned dual moiré system

Exotic collective phenomena emerge when bosons strongly interact within a lattice. However, creating a robust and tunable solid-state platform to explore such phenomena has been elusive. Dual moiré systems$-$compromising two Coulomb-coupled moiré lattices$-$offer a promising system for investigating strongly correlated dipolar excitons (composite bosons) with electrical control. Thus far, their implementation has been hindered by the relative misalignment and incommensurability of the two moiré patterns. Here we report a dual moiré system with perfect translational and rotational alignment, achieved by utilizing twisted hexagonal boron nitride (hBN) bilayer to both generate an electrostatic moiré potential and separate MoSe$_{2}$ and WSe$_{2}$ monolayers. We observe strongly correlated electron phases driven by intralayer interactions and identify interlayer Rydberg trions, which become trapped in the presence of the Mott insulating state. Importantly, our platform is electrostatically programmable, allowing the realization of different lattice symmetries with either repulsive or attractive interlayer interactions. In particular, we implement the latter scenario by optically injecting charges, which form a dipolar excitonic phase. Our results establish a versatile platform for the exploration and manipulation of exotic and topological bosonic quantum many-body phases.

cond-mat.mes-hall↗

arXiv2024

CUAOA: A Novel CUDA-Accelerated Simulation Framework for the QAOA

The Quantum Approximate Optimization Algorithm (QAOA) is a prominent quantum algorithm designed to find approximate solutions to combinatorial optimization problems, which are challenging for classical computers. In the current era, where quantum hardware is constrained by noise and limited qubit availability, simulating the QAOA remains essential for research. However, existing state-of-the-art simulation frameworks suffer from long execution times or lack comprehensive functionality, usability, and versatility, often requiring users to implement essential features themselves. Additionally, these frameworks are primarily restricted to Python, limiting their use in safer and faster languages like Rust, which offer, e.g., advanced parallelization capabilities. In this paper, we develop a GPU accelerated QAOA simulation framework utilizing the NVIDIA CUDA toolkit. This framework offers a complete interface for QAOA simulations, enabling the calculation of (exact) expectation values, direct access to the statevector, fast sampling, and high-performance optimization methods using an advanced state-of-the-art gradient calculation technique. The framework is designed for use in Python and Rust, providing flexibility for integration into a wide range of applications, including those requiring fast algorithm implementations leveraging QAOA at its core. The new framework's performance is rigorously benchmarked on the MaxCut problem and compared against the current state-of-the-art general-purpose quantum circuit simulation frameworks Qiskit and Pennylane as well as the specialized QAOA simulation tool QOKit. Our evaluation shows that our approach outperforms the existing state-of-the-art solutions in terms of runtime up to multiple orders of magnitude. Our implementation is publicly available at https://github.com/JFLXB/cuaoa and Zenodo.

quant-ph↗

arXiv2024

Efficient Quantum One-Class Support Vector Machines for Anomaly Detection Using Randomized Measurements and Variable Subsampling

Quantum one-class support vector machines leverage the advantage of quantum kernel methods for semi-supervised anomaly detection. However, their quadratic time complexity with respect to data size poses challenges when dealing with large datasets. In recent work, quantum randomized measurements kernels and variable subsampling were proposed, as two independent methods to address this problem. The former achieves higher average precision, but suffers from variance, while the latter achieves linear complexity to data size and has lower variance. The current work focuses instead on combining these two methods, along with rotated feature bagging, to achieve linear time complexity both to data size and to number of features. Despite their instability, the resulting models exhibit considerably higher performance and faster training and testing times.

cs.LG↗