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https://github.com/blackboxprogramming/BlackRoad-Operating-System.git
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0108860bff
Create comprehensive research-lab pack structure with mathematical and quantum computing modules from blackroad-prism-console: Math Modules: - hilbert_core.py: Hilbert space symbolic reasoning - collatz/: Distributed Collatz conjecture verification - linmath/: Linear mathematics C library - lucidia_math_forge/: Symbolic proof engine - lucidia_math_lab/: Experimental mathematics Quantum Modules: - lucidia_quantum/: Quantum core - quantum_engine/: Circuit simulation Experiments: - br_math/: Gödel gap, quantum experiments Includes pack.yaml manifest and comprehensive README. 🤖 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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