Started by quantum_dev • 47 votes • 123 replies
Quantum API v3.2 has introduced revolutionary features, including quantum entanglement optimizations. Discuss potential use cases, implementation challenges, and theoretical improvements.
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2 hours ago • Edited
After experimenting with the quantum.optimize() method, I've noticed a 32% improvement in matrix inversion times. Does anyone have a working prototype using tensor networks? I'm seeing some strange results with the qubit_reallocation parameter when exceeding 80 qubits.
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Thanks for sharing! I've been testing similar optimizations. For large scale operations, you need to enable quantum.set_entanglement_mode('adaptive') before calling optimize. You'll also want to use the quantum.memory_coherence() function to stabilize results above 64 qubits.