How lilingxiao-bupt/qd-llm works
QD-LLM is a text-classification research system for emotion, hiding detection, and thematic analysis. Its documented workflow uses LLM knowledge to distill a compact quantum model, trains and evaluates that model, and also reports real-quantum-hardware deployment. The excerpts show dataset parsing, model construction, training, evaluation, and a standalone circuit-to-QASM script; they do not establish how the training pipeline connects to the hardware artifacts. Hardware integration is therefore left unlinked.
Main entry points: QD_LLM_main_Infer.py Training driver · real device.py Circuit generation · QNLP_network.py Quantum circuits · QD_LLM_dataLoader.py Dataset parsing
Components 14
Data preparation
- Task datasets
Dataset - Dataset parsing
QD_LLM_dataLoader.py
Distilled model
- LLM teacher
QNLP_network.py - Text embedding
QNLP_network.py - Quantum circuits
QNLP_network.py - RNN classifier
QNLP_network.py
Training and evaluation
- Training driver
QD_LLM_main_Infer.py - Metrics and evaluation
QD_LLM_main_Infer.py - Model checkpoints
QD_LLM_main_Infer.py
Quantum deployment
- Circuit generation
real_device_baihua/real device.py - Circuit QASM
real_device_baihua/qembedding1_used_in_model_2class.qasm - Circuit QASM copy
real_device_baihua/qembedding1_used_in_model_2class (2).qasm
Other components
- Research user
- Quantum hardware
Connections 11
- Research user → Training driver: runs training
- Dataset parsing → Task datasets: reads examples
QD_LLM_dataLoader.py - Training driver → LLM teacher: constructs
QD_LLM_main_Infer.py - Training driver → Text embedding: constructs
QD_LLM_main_Infer.py - Training driver → RNN classifier: constructs
QD_LLM_main_Infer.py - Training driver → Metrics and evaluation: evaluates
QD_LLM_main_Infer.py - Training driver → Model checkpoints: saves and loads
QD_LLM_main_Infer.py - Training driver → Quantum circuits: uses module
QD_LLM_main_Infer.py - Circuit generation → Circuit QASM: writes circuit
real_device_baihua/real device.py - Circuit generation → Quantum hardware: deploys circuit
- Quantum circuits → Quantum hardware: hardware execution