arXiv subjects

Mahmood Hasani

Publications and source records attributed to Mahmood Hasani.

4 recordsLinked to original sources

arXiv2025

Quantum-Inspired Ising Machines for Quantum Chemistry Calculations

Four decades after Richard Feynman's famous remark, we have reached a stage at which nature can be simulated quantum mechanically. Quantum simulation is among the most promising applications of quantum computing. However, like many quantum algorithms, it is severely constrained by noise in near-term hardware. Quantum-inspired algorithms provide an attractive alternative by avoiding the need for error-prone quantum devices. In this study, we demonstrate that the coherent Ising machine and simulated bifurcation algorithms can accurately reproduce the electronic energy profiles of H_2 and H_2O, capturing their essential energetic features. Notably, we obtain computational times of 1.2 s and 2.4 s for the H_2 and H_2O profiles, respectively, representing a substantial speed-up compared to gate-based quantum computing approaches, which typically require at least 6 s to compute a single molecular geometry with comparable accuracy. These results highlight the potential of quantum-inspired approaches for scaling to larger molecular systems and for future applications in chemistry and materials science.

quant-ph↗

arXiv2025

XG-Attention-WGAN PIC: Utilizing XGboost-Attention-WGAN for Photonics Integrated Circuit Design

Photonic Integrated Circuits (PICs) are fundamental for optical computing, communication, quantum information processing, and precision sensing. However, traditional numerical simulations for designing PIC components are computationally intensive and struggle with high-dimensional parameter spaces. This paper introduces XG-Attention-WGAN PIC, a novel framework that synergistically combines Wasserstein Generative Adversarial Networks (WGANs) with eXtreme Gradient Boosting (XGBoost) to enhance parameter prediction and inverse design in PICs. We utilize Finite-Difference Time-Domain simulations to generate high-fidelity training data, which is augmented by WGAN-generated synthetic data, yielding a root mean squared error (RMSE) of 0.26089. When integrated with XGBoost, this error is reduced to 0.008. The integration of a 64-head self-attention mechanism within the WGAN generator significantly improves data quality and model efficiency over 1000 training epochs. Demonstrated on microring resonators, our approach not only achieves superior prediction accuracy and design optimization but also autonomously discovers a novel, experimentally realizable geometry with enhanced Q-factor performance. The proposed framework provides a scalable, data-driven strategy for developing high-performance PIC components, with promising implications for quantum computing and advanced optical systems.

physics.optics↗

arXiv2025

A Survey on Continuous Variable Quantum Key Distribution for Secure Data Transmission: Toward the Future of Secured Quantum-Networks

Quantum key distribution (QKD) represents a cornerstone of secure communication in the quantum era. While discrete-variable QKD (DV-QKD) protocols were historically the first to demonstrate secure key exchange, continuous-variable QKD (CV-QKD) has emerged as a more practical alternative due to its seamless compatibility with current telecommunications infrastructure. CV-QKD relies on coherent and squeezed states of light, offering significant advantages for integration into modern optical networks. This review comprehensively explores the theoretical underpinnings, technological advancements, and practical challenges of CV-QKD. Special attention is given to the role of photonic integrated circuits (PICs) in enabling scalable and efficient implementation of CV-QKD systems. Furthermore, recent advances in machine learning have been leveraged to optimize CV-QKD performance, with data-driven techniques enhancing noise estimation, parameter optimization, and system security. Additionally, tensor networks provide efficient computational tools for analyzing complex quantum correlations, improving the efficiency and robustness of quantum key distribution protocols. These developments, combined with ongoing improvements in quantum photonic integration, pave the way for the practical deployment of large-scale, high-speed quantum-secure networks.

quant-ph↗

arXiv2024

Continuous Approximation of the Ising Hamiltonian: Exact Ground States and Applications to Fidelity Assessment in Ising Machines

In this study, we present a novel analytical approach to solving large-scale Ising problems by reformulating the discrete Ising Hamiltonian into a continuous framework. This transformation enables us to derive exact solutions for a non-trivial class of fully connected Ising models. To validate our method, we conducted numerical experiments comparing our analytical solutions with those obtained from a quantum-inspired Ising algorithm and a quantum Ising machine. The results demonstrate that the quantum-inspired algorithm and brute-force method successfully align with our solutions, while the quantum Ising machine exhibits notable deviations. Our method offers promising avenues for analytically solving diverse Ising problem instances, while the class of Ising problems addressed here provides a robust framework for assessing the fidelity of Ising machines.

physics.comp-ph↗