Text Generation
QuantumPeer
OpenPeerLLM
PyTorch
English
quantum-llm
quantum-computing
chern-simons
neural-networks
causal-lm
decentralized-learning
transformer
boinc
decent-torch
lonscript
Eval Results (legacy)
Instructions to use OpenPeerAI/QuantumPeer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- QuantumPeer
How to use OpenPeerAI/QuantumPeer with QuantumPeer:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- OpenPeerLLM
How to use OpenPeerAI/QuantumPeer with OpenPeerLLM:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
| import numpy as np | |
| from typing import List, Tuple | |
| class ChernSimonsTopology: | |
| def __init__(self, depth: int): | |
| self.depth = depth | |
| self.dimension = 2 ** depth | |
| self.connections = self._initialize_connections() | |
| def _initialize_connections(self) -> List[Tuple[int, int]]: | |
| """Initialize topological connections based on Chern-Simons theory""" | |
| connections = [] | |
| for i in range(self.depth - 1): | |
| for j in range(i + 1, self.depth): | |
| connections.append((i, j)) | |
| return connections | |
| def get_allowed_operations(self, qubit: int) -> List[str]: | |
| """Get allowed quantum operations for given qubit""" | |
| if 0 <= qubit < self.depth: | |
| return ["H", "X", "Z", "Phase"] | |
| return [] | |
| def calculate_braiding(self, q1: int, q2: int) -> np.ndarray: | |
| """Calculate braiding operation between two qubits""" | |
| if (q1, q2) in self.connections or (q2, q1) in self.connections: | |
| theta = np.pi / 4 # Topological phase | |
| c = np.cos(theta) | |
| s = np.sin(theta) | |
| return np.array([[c, -s, 0, 0], | |
| [s, c, 0, 0], | |
| [0, 0, c, -s], | |
| [0, 0, s, c]]) | |
| return np.eye(4) |