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
File size: 2,188 Bytes
a49d7b4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 | import numpy as np
from quantum_circuit import QuantumCircuit
from quantum_topology import ChernSimonsTopology
class CircuitVisualizer:
def __init__(self, circuit: QuantumCircuit):
self.circuit = circuit
def draw_circuit(self) -> str:
"""Generate ASCII visualization of quantum circuit"""
output = []
output.append("Quantum Circuit:")
output.append("-" * 40)
for i, gate in enumerate(self.circuit.gates):
output.append(f"Gate {i}: {gate.gate_type}")
if gate.gate_type == "CNOT":
output.append(" |control⟩ ──●──")
output.append(" │")
output.append(" |target⟩ ─⊕─")
else:
output.append(f" |ψ⟩ ──{gate.gate_type}──")
output.append("")
return "\n".join(output)
def draw_topology(self) -> str:
"""Generate ASCII visualization of topology"""
output = []
output.append("Topology Layout:")
output.append("-" * 40)
for i in range(self.circuit.topology.depth):
connections = [j for j in range(self.circuit.topology.depth)
if (i,j) in self.circuit.topology.connections]
line = [f"Q{i}"]
for j in range(self.circuit.topology.depth):
if j in connections:
line.append("──●──")
else:
line.append("─────")
output.append("".join(line))
return "\n".join(output)
def get_state_visualization(self, state: np.ndarray) -> str:
"""Visualize quantum state"""
output = []
output.append("Quantum State:")
output.append("-" * 40)
# Show amplitudes and probabilities
for i, amplitude in enumerate(state):
prob = np.abs(amplitude) ** 2
binary = format(i, f'0{self.circuit.topology.depth}b')
output.append(f"|{binary}⟩: {amplitude:.3f} (Prob: {prob:.3f})")
return "\n".join(output) |