Αποθηκεύτηκε σε:
| Κύριος συγγραφέας: | |
|---|---|
| Μορφή: | Recurso digital |
| Γλώσσα: | Αγγλικά |
| Έκδοση: |
Zenodo
2026
|
| Θέματα: | |
| Διαθέσιμο Online: | https://doi.org/10.5281/zenodo.19024106 |
| Ετικέτες: |
Προσθήκη ετικέτας
Δεν υπάρχουν, Καταχωρήστε ετικέτα πρώτοι!
|
Πίνακας περιεχομένων:
- <p>We present a preliminary investigation of quantum-assisted training for BitNet neural networks, which constrain all weights to ternary values {-1, 0, +1} for memory efficiency. Classical gradient descent gets trapped in saddle points due to vanishing gradients through the sign() quantization function. We propose Quantum-Assisted Critical Weight Search (QACWS): gradient magnitude analysis identifies the 16 hardest weights for classical training; a QAOA-inspired quantum circuit on real IBM Torino hardware (133 qubits) searches exclusively those weights (1 qubit = 1 real weight); classical fine-tuning polishes the result. On a 16-bit XOR benchmark with a 272-weight BitNet model, the hybrid pipeline achieved 59.0% test accuracy, surpassing the classical plateau of 56.8% by +2.2 percentage points. We document a complete five-version experimental trajectory on IBM Torino quantum hardware revealing key failure modes and engineering solutions. All IBM Quantum job IDs are provided for full reproducibility. Source code: github.com/sh1vam-03/bitnet_quantum</p>