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 Optional, Dict, Any, List | |
| from quantum_topology import ChernSimonsTopology | |
| from quantum_gates import QuantumGate | |
| class QuantumCircuit: | |
| def __init__(self, topology: ChernSimonsTopology): | |
| self.topology = topology | |
| self.gates: List[QuantumGate] = [] | |
| self.initialize_gates() | |
| def initialize_gates(self): | |
| """Initialize quantum gates based on topology""" | |
| self.gates = [ | |
| QuantumGate("H"), | |
| QuantumGate("CNOT"), | |
| QuantumGate("Phase"), | |
| QuantumGate("X"), | |
| QuantumGate("Z") | |
| ] | |
| def prepare_input(self, data: str) -> np.ndarray: | |
| """Convert classical input to quantum state""" | |
| state = np.zeros(self.topology.dimension, dtype=np.complex128) | |
| state[0] = 1.0 # Initialize to |0...0⟩ | |
| for i, char in enumerate(data): | |
| if i >= self.topology.depth: | |
| break | |
| if ord(char) % 2: | |
| h_gate = QuantumGate("H") | |
| state = h_gate.apply(state, self.topology) | |
| state /= np.sqrt(np.sum(np.abs(state) ** 2)) | |
| return state | |
| def evolve( | |
| self, | |
| state: np.ndarray, | |
| params: Optional[Dict[str, Any]] = None | |
| ) -> np.ndarray: | |
| current_state = state.copy() | |
| if params and "gates" in params: | |
| for gate_name in params["gates"]: | |
| gate = QuantumGate(gate_name) | |
| current_state = gate.apply(current_state, self.topology) | |
| else: | |
| for gate in self.gates: | |
| current_state = gate.apply(current_state, self.topology) | |
| for i in range(self.topology.depth - 1): | |
| for j in range(i + 1, self.topology.depth): | |
| braiding = self.topology.calculate_braiding(i, j) | |
| current_state = np.dot(current_state.reshape(-1, 4), braiding.T).flatten() | |
| current_state /= np.sqrt(np.sum(np.abs(current_state) ** 2)) | |
| return current_state |