arXiv subjects

Payman Kazemikhah

Publications and source records attributed to Payman Kazemikhah.

4 recordsLinked to original sources

arXiv2026

Quantized Photocurrents in Gapless Topological Matter

The quantum Hall effect establishes that topology can fix a material response to integer multiples of fundamental constants when an energy gap isolates the relevant symmetry-protected electronic states. Whether such universal quantization can also emerge in gapless matter, where topological bands coexist with a continuum of metallic excitations, has remained a fundamental question in the field of quantum materials. Chiral topological semimetals provide a unique setting in which to explore this principle; when optical transitions are confined to a single chiral node, the resulting circular photogalvanic effect is predicted to be quantized by the topological charge of the node. In real materials, however, this nonlinear optical phenomenon has remained experimentally elusive, obscured by trivial band transitions, insufficient energy separation between node pairs, and their relative positions with respect to the Fermi level. Here we observe a quantized circular photogalvanic effect in the chiral topological semimetal Rh0.95Ni0.05Si. Band engineering via Ni substitution opens a photon-energy window dominated by interband optical transitions at the Γ-point multifold node. This allows circularly polarized near- to mid-infrared pulses to drive a helicity-odd terahertz response that manifests three hallmarks of quantization: a sharp onset, a photon-energy-independent plateau governed by the magnitude of the monopole charge, and an abrupt long-wavelength cutoff imposed by Pauli blocking. Our work thus establishes an all-optical analogue of the quantum Hall effect and a new paradigm to realize topological quantization in gapless matter.

cond-mat.mtrl-sci↗

arXiv2025

Using 1-Factorization from Graph Theory for Quantum Speedups on Clique Problems

The clique problems, including $k$-CLIQUE and Triangle Finding, form an important class of computational problems; the former is an NP-complete problem, while the latter directly gives lower bounds for Matrix Multiplication. A number of previous efforts have approached these problems with Quantum Computing methods, such as Amplitude Amplification. In this paper, we provide new Quantum oracle designs based on the 1-factorization of complete graphs, all of which have depth $O(n)$ instead of the $O(n^2)$ presented in previous studies. Also, we discuss the usage of one of these oracles in bringing the Triangle Finding time complexity down to $O(n^{2.25} poly(log n))$, compared to the $O(n^{2.38})$ classical record. Finally, we benchmark the number of required Amplitude Amplification iterations for another presented oracle, for solving $k$-CLIQUE.

quant-ph↗

arXiv2025

Quantum-Efficient Convolution through Sparse Matrix Encoding and Low-Depth Inner Product Circuits

Convolution operations are foundational to classical image processing and modern deep learning architectures, yet their extension into the quantum domain has remained algorithmically and physically costly due to inefficient data encoding and prohibitive circuit complexity. In this work, we present a resource-efficient quantum algorithm that reformulates the convolution product as a structured matrix multiplication via a novel sparse reshaping formalism. Leveraging the observation that localized convolutions can be encoded as doubly block-Toeplitz matrix multiplications, we construct a quantum framework wherein sparse input patches are prepared using optimized key-value QRAM state encoding, while convolutional filters are represented as quantum states in superposition. The convolution outputs are computed through inner product estimation using a low-depth SWAP test circuit, which yields probabilistic amplitude information with reduced sampling overhead. Our architecture supports batched convolution across multiple filters using a generalized SWAP circuit. Compared to prior quantum convolutional approaches, our method eliminates redundant preparation costs, scales logarithmically with input size under sparsity, and enables direct integration into hybrid quantum-classical machine learning pipelines. This work provides a scalable and physically realizable pathway toward quantum-enhanced feature extraction, opening up new possibilities for quantum convolutional neural networks and data-driven quantum inference.

quant-ph↗

arXiv2024

XpookyNet: Advancement in Quantum System Analysis through Convolutional Neural Networks for Detection of Entanglement

The application of machine learning models in quantum information theory has surged in recent years, driven by the recognition of entanglement and quantum states, which are the essence of this field. However, most of these studies rely on existing prefabricated models, leading to inadequate accuracy. This work aims to bridge this gap by introducing a custom deep convolutional neural network (CNN) model explicitly tailored to quantum systems. Our proposed CNN model, the so-called XpookyNet, effectively overcomes the challenge of handling complex numbers data inherent to quantum systems and achieves an accuracy of 98.5%. Developing this custom model enhances our ability to analyze and understand quantum states. However, first and foremost, quantum states should be classified more precisely to examine fully and partially entangled states, which is one of the cases we are currently studying. As machine learning and quantum information theory are integrated into quantum systems analysis, various perspectives, and approaches emerge, paving the way for innovative insights and breakthroughs in this field.

quant-ph↗