Genetic Algorithm for VQC ansatz search
raw JSON → 1.0.14 verified Sat May 09 auth: no python
GA-VQC provides a genetic algorithm to automatically search for optimal variational quantum circuit (VQC) ansätze for quantum machine learning tasks. Version 1.0.14 is the latest; development appears to be active on GitHub.
pip install ga-vqc Common errors
error ModuleNotFoundError: No module named 'ga_vqc' ↓
cause Incorrect import path; correct module name is 'gavqc'.
fix
Use: from gavqc import GenAlg
error TypeError: __init__() got an unexpected keyword argument 'X' ↓
cause GenAlg expects 'X' and 'y' as positional or keyword arguments, but older versions used different parameter names.
fix
Ensure you are using the latest version (pip install --upgrade ga-vqc). The current API accepts 'X' and 'y'.
Warnings
breaking Import path changed: module name is 'gavqc' (not 'ga_vqc' or 'ga-vqc'). ↓
fix Use 'from gavqc import ...' instead of 'import ga_vqc'.
gotcha GenAlg.run() modifies the instance; best circuit stored in GenAlg.best_circuit attribute. ↓
fix Access results via the GenAlg instance after calling run().
deprecated Support for Qiskit <0.45 is dropped. ↓
fix Upgrade to Qiskit 0.45+.
Imports
- GenAlg wrong
from ga_vqc import GenAlgcorrectfrom gavqc import GenAlg - VQCAnsatz wrong
from ga_vqc.ansatz import VQCAnsatzcorrectfrom gavqc import VQCAnsatz
Quickstart
from gavqc import GenAlg
from qiskit.circuit import QuantumCircuit
from qiskit.circuit.library import RealAmplitudes
import numpy as np
# Example: optimize a simple circuit structure
ansatz = RealAmplitudes(2, reps=1)
X = np.random.rand(10, 2)
y = np.random.randint(0, 2, 10)
ga = GenAlg(
ansatz=ansatz,
X=X, y=y,
generations=10,
population_size=10,
mutation_rate=0.1
)
ga.run()
print("Best fitness:", ga.best_fitness)
print("Best circuit:", ga.best_circuit)