activity
20242026
collaborators

6 papers

quant-ph2026

Measurement and reload costs in direct quantum simulation of nonlinear waves

Ziqing Guo, Viraj Dsouza, Alex Khan +3

Quantum processors encode an N-point field in log_2(N) qubits, which renders nonlinear wave equations an important application for quantum simulation. Nonlinear evolution, however,…

cs.CE2026

Exploring the non-convexity in machine learning using quantum-inspired optimization

Kandula Eswara Sai Kumar, Parth Dhananjay Danve, Abhishek Chopra +1

The escalating complexity of modern machine learning necessitates solving challenging non-convex optimization problems, particularly in high-dimensional regimes and scenarios conta…

physics.comp-ph2026

Design of Magnetic Lattices with a Quantum-Inspired Evolutionary Optimization Algorithm

Zekeriya Ender Eğer, Waris Khan, Priyabrata Maharana +5

This article investigates the identification of magnetic spin distributions in ferromagnetic materials by minimizing the system's free energy. Magnetic lattices of varying sizes ar…

cs.CE2025

Investigation of Performance and Scalability of a Quantum-Inspired Evolutionary Optimizer (QIEO) on NVIDIA GPU

Aman Mittal, Kasturi Venkata Sai Srikanth, Ferdin Sagai Don Bosco +3

Quantum inspired evolutionary optimization leverages quantum computing principles like superposition, interference, and probabilistic representation to enhance classical evolutiona…

cs.CE2024

Benchmarking of GPU-optimized Quantum-Inspired Evolutionary Optimization Algorithm using Functional Analysis

Kandula Eswara Sai Kumar, Supreeth B S, Rajas Dalvi +5

This article presents a comparative analysis of GPU-parallelized implementations of the quantum-inspired evolutionary optimization (QIEO) approach and one of the well-known classic…

physics.flu-dyn2024

Demonstration of Scalability and Accuracy of Variational Quantum Linear Solver for Computational Fluid Dynamics

Ferdin Sagai Don Bosco, Dhamotharan S, Rut Lineswala +1

The solution for non-linear, complex partial differential Equations (PDEs) is achieved through numerical approximations, which yield a linear system of equations. This approach is…