6afdb4b148
Lucidia Core - AI reasoning engines for specialized domains: - Physicist (867 lines) - energy modeling, force calculations - Mathematician (760 lines) - symbolic computation, proofs - Geologist (654 lines) - terrain modeling, stratigraphy - Engineer (599 lines) - structural analysis, optimization - Painter (583 lines) - visual generation, graphics - Chemist (569 lines) - molecular analysis, reactions - Analyst (505 lines) - pattern recognition, insights - Plus: architect, researcher, mediator, speaker, poet, navigator Features: - FastAPI wrapper with REST endpoints for each agent - CLI with `lucidia list`, `lucidia run`, `lucidia api` - Codex YAML configurations for agent personalities - Quantum engine extensions 12,512 lines of Python across 91 files. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
50 lines
1.5 KiB
Python
50 lines
1.5 KiB
Python
"""Example quantum models."""
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from __future__ import annotations
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import torch
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import torch.nn.functional as F
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from torch import nn
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from third_party import torchquantum as tq
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class PQCClassifier(nn.Module):
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def __init__(self, n_wires: int = 4):
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super().__init__()
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self.n_wires = n_wires
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self.measure = tq.MeasureAll(tq.PauliZ)
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self.rx0 = tq.RX(True, True)
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self.ry0 = tq.RY(True, True)
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self.rz0 = tq.RZ(True, True)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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bsz = x.shape[0]
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qdev = tq.QuantumDevice(n_wires=self.n_wires, bsz=bsz, device=x.device)
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self.rx0(qdev, wires=0)
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self.ry0(qdev, wires=1)
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self.rz0(qdev, wires=2)
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meas = self.measure(qdev)
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if meas.shape[-1] < 4:
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pad = torch.zeros(bsz, 4 - meas.shape[-1], device=meas.device, dtype=meas.dtype)
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meas = torch.cat([meas, pad], dim=-1)
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logits = meas[..., :4].reshape(bsz, 2, 2).sum(-1)
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measurements = self.measure(qdev)
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if measurements.shape[1] < 4:
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pad = 4 - measurements.shape[1]
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measurements = F.pad(measurements, (0, pad))
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logits = measurements[:, :4].reshape(bsz, 2, 2).sum(-1)
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return F.log_softmax(logits, dim=1)
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class VQEModel(nn.Module):
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"""Placeholder VQE model."""
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def forward(self, *args, **kwargs):
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return torch.tensor(0.0)
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class QAOAModel(nn.Module):
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"""Placeholder QAOA model."""
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def forward(self, *args, **kwargs):
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return torch.tensor(0.0)
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