A team from Wipro and Duy Tan University integrates quantum processing units with AI frameworks such as Qiskit, TensorFlow Quantum, and PennyLane. They leverage superposition, entanglement, and error-correction methods to design and optimize quantum machine learning algorithms, targeting accelerated drug discovery, portfolio optimization, and enhanced cybersecurity.

Key points

• Integration of QPU and classical CPU to run optimized quantum circuits for AI tasks.
• Quantum software stack features Qiskit, TensorFlow Quantum, and PennyLane for algorithm development.
• Implementation of error-correction codes to mitigate decoherence and gate errors in qubit systems.
• Applications include accelerated molecular simulation for drug discovery, financial portfolio optimization, and secure communications.
• Scalability achieved via qubit connectivity optimization and hybrid quantum–classical workflows.

Why it matters: Quantum AI enables computations unfeasible on classical hardware, promising orders-of-magnitude speedups for critical applications like molecular simulation and optimization. By harnessing quantum parallelism and entanglement, this approach could transform drug discovery, financial modeling, and cryptography.