Research project
Blackbox Hamiltonian Learning from Quantum Measurements
Deep learning models for predicting quantum system dimensions and Hamiltonian parameters from minimal measurement data
This project is being conducted as my graduation project (CMPE491) at Boğaziçi University under the supervision of Asst. Prof. İnci Meliha Baytaş from February 2025 to June 2025.
Project Overview
This project developed deep learning models for blackbox Hamiltonian learning from minimal quantum measurement data. The focus was on predicting both system dimension and Hamiltonian parameters jointly using multi-output neural networks, as well as 1D CNNs, RNNs, and Transformers. I simulated open quantum systems via Julia and QuantumOptics.jl for dataset generation, creating a comprehensive framework for quantum system parameter estimation.
Keywords
Python · Neural Networks · 1D CNNs · RNNs · Transformers · Julia · QuantumOptics.jl