qkabrine-automl 2.1.1

Comprehensive automatic quantum machine learning — intelligent search for the best quantum circuit, encoding, and hyperparameters for your data

⚛️ Qkabrine AutoML

An open-source framework that unifies intelligent architecture search, quantum kernel methods, and variational circuits in a single AutoML pipeline.

Created by Eric Jagwara at Solid Elf Labs

pip install qkabrine-automl

from qkabrine_automl import QkabrineAutoML

automl = QkabrineAutoML(task='classification', search_strategy='bayesian')
automl.fit(X_train, y_train)
automl.leaderboard()
preds = automl.predict(X_test)
print(automl.export_qasm())

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What Makes This Different

Existing quantum ML tools force you to choose a circuit, choose an encoding, choose a training strategy, and hope it works. Qkabrine AutoML searches across all of these simultaneously:

Dimension	What's Searched
Circuit architecture	12 ansätze (strongly entangling, hardware efficient, data re-uploading, cascading, criss-cross, all-to-all, ...)
Circuit depth	1 to max_layers for each architecture
Data encoding	Angle, Angle-YZ, IQP, Amplitude embedding
Model paradigm	Variational circuits and quantum kernel + SVM
Parameter init	Uniform, small, zeros, normal, block (avoids barren plateaus)
Learning rate	Co-optimized per candidate

Key Features

1. Five search strategies in one API — grid, random, Bayesian (GP + Expected Improvement), evolutionary (genetic algorithm), and successive halving (HyperBand-inspired).

2. Joint search over architecture × encoding × hyperparameters — most QAS tools only search over gate arrangements. We co-optimize the complete pipeline.

3. Quantum kernels as first-class citizens — the search considers both variational circuits and quantum kernel + SVM methods, comparing them on equal footing.

4. Circuit surgery — post-search pruning of near-identity rotation gates and simplification of redundant gate pairs, reducing circuit depth for NISQ deployment.

5. True multi-class classification — qml.probs() with cross-entropy loss trains all classes simultaneously.

6. Expressibility & entangling capability metrics — KL-divergence expressibility and Meyer-Wallach entanglement for circuit analysis.

7. Training dynamics — DQFIM trainability prediction, barren plateau monitoring, and quantum natural gradient optimization.

8. Noise-aware training for NISQ hardware readiness + QASM export for deployment.

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Quickstart

Binary Classification

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from qkabrine_automl import QkabrineAutoML

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

automl = QkabrineAutoML(
    task='classification',
    n_qubits=4,
    max_layers=2,
    search_strategy='bayesian',
    encodings=('angle', 'iqp'),
    feature_reduction='pca',
)
automl.fit(X_train, y_train)
automl.leaderboard()
print(f"Test accuracy: {automl.score(X_test, y_test):.4f}")

Multi-Class Classification

from sklearn.datasets import load_iris
from qkabrine_automl import QkabrineAutoML

X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

automl = QkabrineAutoML(
    task='classification',
    n_qubits=4,
    search_strategy='evolutionary',
)
automl.fit(X_train, y_train)

Regression

automl = QkabrineAutoML(task='regression', search_strategy='bayesian')
automl.fit(X_train, y_train)

Save and Load

automl.save('my_model.pkl')
loaded = QkabrineAutoML.load('my_model.pkl')
loaded.predict(X_test)

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Search Strategies

Strategy	Use When
'bayesian'	Default. Best sample efficiency. GP + Expected Improvement.
'evolutionary'	Large search spaces. Genetic algorithm with crossover + mutation.
'successive_halving'	Training is expensive. Start many, halve, double budget.
'grid'	Small spaces. Exhaustive enumeration.
'random'	Quick baseline. Budget-controlled sampling.

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Circuit Architectures (12)

Name	Key Property
strongly_entangling	High expressibility
hardware_efficient	Low depth
data_reuploading	Data re-encoding between layers
simplified_two_design	Near-Haar coverage
all_to_all	Maximum connectivity
cascading	QFT-inspired multi-scale entanglement
criss_cross	Butterfly-pattern spread
ring_of_cnots	Dense parameterization
full_rotation	Maximum rotation freedom
alternating_rx_ry	Layer diversity
shallow_rx	Barren-plateau resistant
hadamard_entangling	Superposition-first

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Training Dynamics

from qkabrine_automl import DataQuantumFisherMetric, BarrenPlateauMonitor

# Predict trainability before training
dqfim = DataQuantumFisherMetric(n_qubits=4)
metrics = dqfim.predict_generalization(circuit_fn, X, n_params=12)
print(f"Trainability: {metrics.trainability_score:.3f}")

# Monitor for barren plateaus during training
monitor = BarrenPlateauMonitor(threshold=1e-7, auto_surgery=True)

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API Reference

QkabrineAutoML

Parameter	Default	Description
task	'classification'	'classification' or 'regression'
n_qubits	None	Auto-inferred (max 10)
max_layers	3	Max circuit depth
train_steps	40	Gradient steps per candidate
search_strategy	'bayesian'	Search algorithm
encodings	('angle',)	Encodings to search
optimizer	'adam'	'adam', 'sgd', 'momentum'
include_kernels	True	Evaluate quantum kernel methods
cv_folds	None	Cross-validation folds (>= 2)
noise_model	None	'depolarizing', 'bitflip', etc.
feature_reduction	'pca'	Dimensionality reduction method
use_dqfim_prescreening	False	Pre-screen via DQFIM
monitor_barren_plateaus	False	Early-stop on vanishing gradients

Methods

Method	Description
.fit(X, y)	Run architecture search
.predict(X)	Predict with best model
.predict_proba(X)	Soft scores (classification)
.score(X, y)	Accuracy or R²
.leaderboard()	Print ranked results
.best_circuit_summary()	Gate-by-gate breakdown
.export_qasm()	OpenQASM 2.0 string
.save(path) / .load(path)	Serialize / deserialize

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Links

• Homepage: qkabrine.online
• Author: Eric Jagwara
• Lab: Solid Elf Labs
• Repository: github.com/ericjagwara/qkabrine
• Issues: github.com/ericjagwara/qkabrine/issues

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License

MIT — Copyright (c) 2026 Eric Jagwara, Solid Elf Labs