Juq-253 -

# Load a classic CNN backbone model = tf.keras.applications.MobileNetV2( input_shape=(28, 28, 1), weights=None, classes=10 )

# Attach a quantum layer for the final classification head @qatf.quantum def quantum_classifier(x): # 5‑qubit variational circuit (auto‑generated) return qatf.qnn(x, n_qubits=5, depth=4) juq-253

Stay tuned, experiment, and let the quantum acceleration begin! # Load a classic CNN backbone model = tf

# Build the hybrid model inputs = tf.keras.Input(shape=(28, 28, 1)) x = model(inputs) outputs = quantum_classifier(x) hybrid_model = tf.keras.Model(inputs, outputs) Additionally, the company is collaborating with the Open

# Dummy image import numpy as np img = np.random.rand(1, 28, 28, 1).astype('float32') pred = hybrid_model.predict(img) print("Hybrid prediction:", np.argmax(pred, axis=1)) Running this on a workstation with a JUQ‑253 card reduces the inference latency from to ~12 ms , as shown in the benchmark table. The QATF SDK automatically handles the data transfer to the QPU, error mitigation, and result stitching. 7. The Road Ahead – What’s Next for JUQ‑253? QuantumFlux has already hinted at a JUQ‑353 in development, promising a 350‑qubit core and an even slimmer 0.3 kg cryocooler. Additionally, the company is collaborating with the Open Quantum Safe (OQS) project to embed post‑quantum cryptographic primitives directly in the QPU firmware.