quantlof 0.1.5

Quantum-enhanced Local Outlier Factor algorithm

QuantumLOFClassifier – Inspired by Guo et al. (2023)

This repository provides an implementation partially inspired by the algorithm proposed in:

Ming-Chao Guo et al., “Quantum Algorithm for Unsupervised Anomaly Detection” arXiv:2304.08710 (2023)

📘 Overview

QuantumLOFClassifier is a quantum-enhanced Local Outlier Factor (LOF) anomaly detection classifier, using Hadamard-test quantum circuits to estimate inner products between vectors, which are then used to calculate pairwise distances for LOF scoring.

• ✅ Quantum LOF step using Qiskit 2.0.2 compatible circuits
• ✅ LOF score computation
• ✅ Dual downstream models for clean and noisy regions
• ⚠️ Currently lacks Grover-based quantum minimum/average steps

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🧠 Algorithm Mapping to Paper (Guo 2023 - arXiv:2304.08710)

Paper Section	Functionality	Implemented?	Notes
III-A Eq.(7)	Amplitude embedding	✅ Yes	Implemented via sin² encoding: amplitudes = sin(x_i).
III-A Eq.(11)	Swap test ⟨x｜y⟩	✅ Yes	Implemented manually using CSWAP gates and initialize().
III-A Eq.(15) - Eq.(17)	Distance from inner product	⚠ Partially	Uses d(x, y) = √(1 - ⟨x｜y⟩) assuming normalized inputs (not amplitude-based overlap fidelity).
III-A Step 1.6–1.7	Quantum Minimum Search	❌ No	Classical sort is used instead of quantum minimum finding.
III-B	Quantum LRD (inverse of avg reach dist)	❌ No	Classical averaging is used for local reachability density calculation.
Eq.(2), Eq.(28)	Grover-based anomaly extraction	❌ No	Anomalies are detected classically using a threshold on LOF score (delta).

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🚀 Installation

pip install quantlof

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🧪 Example Usage

from quantum_lof import QuantumLOFClassifier

clf = QuantumLOFClassifier(
    n_neighbors=20,
    delta=1.5,
    quantum_backend='qiskit_simulator',  # or actual IBM backend like 'ibm_cairo'
    shots=1024,
    random_state=42
)

clf.detect_anomalies(X, y)
print(clf.lof_scores_)

anom_idx = clf.get_anomaly_indices()
clean_idx = clf.get_clean_indices()
print("Anomalies indices:", anom_idx)
print("Clean indices:", clean_idx)

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⚙️ Core Features

• ✅ Hadamard-test for ⟨x|y⟩ inner products
• ✅ Euclidean distance via Eq. (15–17)
• ✅ k-distance via quantum estimation
• ✅ Local Reachability Density (LRD)
• ✅ LOF scores (Eq. 18) with thresholding
• ✅ Clean/noise classification downstream
• ✅ Fallback to classical when n > maxsample_for_quantum

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🛠️ API

QuantumLOFClassifier(...)

Argument	Description
n_neighbors	Number of neighbors for LOF
delta	LOF threshold (LOF ≥ δ → anomaly)
quantum_backend	Qiskit backend (e.g. "qiskit_simulator", "ibm_cairo")
shots	Number of shots in Hadamard test
maxsample_for_quantum	Fallback threshold for classical mode
clean_model	Classifier for clean samples (default: SVM)
noise_model	Classifier for all samples (default: RandomForest)

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🤖 Implementation Notes

• The quantum inner product estimation uses Hadamard test with controlled inverse StatePreparation.
• Classical LOF score is maintained to allow hybrid quantum–classical behavior.
• The quantum part only replaces pairwise distance calculations.

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📜 License

MIT License © 2025 Yuto Mueller

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💡 Future Work

• Grover-based minimum search
• QRAM emulation
• Amplitude estimation for LOF
• GPU-accelerated classical fallback