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>
59 lines
1.7 KiB
Python
59 lines
1.7 KiB
Python
"""Helper builders for Qiskit EstimatorQNN and SamplerQNN."""
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from __future__ import annotations
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from typing import Optional
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from qiskit import QuantumCircuit
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from qiskit.circuit import ParameterVector
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from qiskit_machine_learning.neural_networks import EstimatorQNN, SamplerQNN
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from .backends import AerCPUBackend, QuantumBackend
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def build_estimator_qnn(
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feature_map: QuantumCircuit,
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ansatz: QuantumCircuit,
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observable: QuantumCircuit | None,
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input_size: int,
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weight_size: int,
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backend: Optional[QuantumBackend] = None,
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) -> EstimatorQNN:
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"""Construct an :class:`EstimatorQNN` with gradients enabled."""
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backend = backend or AerCPUBackend()
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input_params = ParameterVector("x", length=input_size)
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weight_params = ParameterVector("w", length=weight_size)
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return EstimatorQNN(
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feature_map=feature_map,
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ansatz=ansatz,
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observable=observable,
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input_params=input_params,
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weight_params=weight_params,
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backend=backend.simulator,
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input_gradients=True,
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)
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def build_sampler_qnn(
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feature_map: QuantumCircuit,
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ansatz: QuantumCircuit,
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input_size: int,
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weight_size: int,
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num_classes: int,
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backend: Optional[QuantumBackend] = None,
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) -> SamplerQNN:
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"""Construct a probabilistic :class:`SamplerQNN`."""
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backend = backend or AerCPUBackend()
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input_params = ParameterVector("x", length=input_size)
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weight_params = ParameterVector("w", length=weight_size)
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return SamplerQNN(
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feature_map=feature_map,
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ansatz=ansatz,
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input_params=input_params,
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weight_params=weight_params,
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output_shape=num_classes,
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backend=backend.simulator,
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)
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