MultiHub Forum

I'm a computer science researcher transitioning from classical optimization algorithms to exploring quantum computing applications for logistics and supply chain problems. I've gone through the foundational theory but am now stuck on the practical leap from simulating small circuits to designing algorithms for near-term, noisy intermediate-scale quantum devices. For those working in applied quantum computing, what are the most promising hybrid quantum-classical approaches for real-world combinatorial optimization? How do you effectively model a problem like vehicle routing or portfolio optimization to map onto a quantum annealer or a gate-based quantum processor, and what are the current limitations in qubit count and coherence time that make certain problem sizes infeasible? What software development kits and cloud-based quantum processors would you recommend for hands-on experimentation?