QCNN for Phase Identification
Quantum Convolutional Neural Networks for quantum phase identification — QHack 2024 (3rd Place + Amazon Braket Prize)
At QHack 2024, we developed and trained Quantum Convolutional Neural Networks (QCNNs) for identifying quantum phases of matter.
Highlights:
- Trained on a GPU-accelerated quantum circuit simulator for rapid prototyping
- Evaluated robustness on IonQ’s real quantum hardware through AWS Braket
- Won 3rd place in the “A Matter of Taste” challenge and the Amazon Braket Prize (Top 3)
- Featured in PennyLane Highlights
QCNNs are quantum analogs of classical convolutional networks, leveraging translationally invariant circuits to classify quantum states efficiently.
GitHub: ruyi101/QCNN-Phases