🧠 Code & Systems Preview
We engineer explainable, quantum-inspired AI systems for regulated, low-latency, high-integrity applications. Below are selected components and logic excerpts from quantum-inspired explainable AI, quantum-powered anomaly detection, and supporting hybrid quantum-classical ML pipelines.
🔍 Feature Attribution via Hilbert Activation Norms
A quantum-inspired explainable AI (QIXAI) Keras-based CNN outputting a dynamically generated relevance heatmap. Integrates mutual information scoring and cosine similarity for real-time interpretability.
def hilbert_activation(x):
norm = K.sqrt(K.sum(K.square(x), axis=-1, keepdims=True))
return x / (norm + K.epsilon())
Tech: Keras, Numpy, Custom QIXAI layer API
Use Case: Real-time medical image diagnosis
📈 Interpretable Output Generator (IOG)
An inline decision trace generator, providing justification trails during inferencing. Layered inside a TensorFlow or PyTorch quantum inspired interpretability lifecycle system deployment.
# Sample IOG output trace (text form)
"This decision was made on 2025-06-10 at 14:05 UTC.
It was driven primarily by domain entropy (37%) and query type rarity (34%),
confirmed by feature activation cluster B6-G. Model version: V3.1."
Tech: PyTorch Lightning, TorchServe, Kafka
Use Case: DNS anomaly scoring pipelines
🚨 Quantum-Powered Anomaly Detector (Q-PAD): Real-Time Quantum-Enhanced DNS Threat Alert
A simulated STIX/TAXII alert triggered by a quantum-classical anomaly classifier running BERT embeddings + Braket hybrid kernel expansion.
{
"type": "indicator",
"spec_version": "2.1",
"id": "indicator--a883de12-4cf9",
"name": "Q-PAD DNS anomaly detection alert",
"description": "Anomaly detected in DNS flow stream. Features: entropy (37%), rarity (34%), cluster B6-G.",
"pattern": "[network-traffic:dst_port = '53']",
"valid_from": "2025-06-10T14:05:00Z",
"labels": ["anomaly", "quantum-enhanced", "real-time"],
"confidence": 87
}
Tech: AWS Braket, BERT, TorchServe, Kafka
Stage: Under prototype testing for inline defense
📚 Hybrid NLP Circuit on AWS Braket
A toy example of a Braket Hybrid Job to execute a QNLP sentence encoding circuit on Rigetti, using PyTorch and PennyLane orchestration.
# hybrid_main.py
@braket_hybrid_job
def run(job: HybridJob):
device = qml.device("braket.aws.qubit", wires=4)
@qml.qnode(device, interface="torch")
def circuit(x):
qml.AngleEmbedding(x, wires=range(4))
return qml.expval(qml.PauliZ(0))
return circuit(torch.tensor([0.1, 0.3, 0.2, 0.4]))
Tech: PennyLane, AWS Braket Hybrid, Rigetti
Use Case: Experimental QNLP pipeline (Q-PAD)
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