Accelerating the Path to Practical Quantum Applications with AI (www.hpcwire.com)

AI and quantum computing are moving from theory to practical deployment as hybrid architectures, quantum‑machine‑learning (QML) techniques and AI-driven toolchains produce measurable gains across chemistry, logistics, finance and energy. Real-world examples include Microsoft’s Azure Quantum Elements using two logical qubits plus classical HPC to screen tens of millions of battery-material candidates, an Australian Quantum Kernel‑Aligned Regressor that beat seven classical models on small noisy semiconductor datasets, and hybrid VQE workflows tested by AstraZeneca and IBM for small‑molecule design. Industry pilots span QAOA-style optimization for routing and grid balancing (DHL, Maersk, EDF, Enel), quantum‑enhanced Monte Carlo and QRNGs for finance (JPMorgan, BBVA, Goldman), and the IBM–AMD push toward “quantum‑centric supercomputers” for large‑scale materials and drug discovery. Technically, this momentum is powered by mature frameworks (Qiskit, TensorFlow Quantum, Guppy), emulator tooling (Quantinuum’s Selene) and compiler‑level AI: reinforcement‑learning qubit mappers, graph neural nets for noise‑aware compilation, and generative/variational gate synthesis that shorten circuits on NISQ devices. The result is viable hybrid workflows where classical AI/HPC handle data and training while quantum circuits tackle high‑dimensional sampling and combinatorial optimization. For the AI/ML and HPC communities, the takeaway is clear — adopt quantum‑aware skills, design hybrid pipelines, and target problems aligned with QML strengths as the field transitions from proofs‑of‑concept toward full‑stack, fault‑tolerant and networked quantum systems.