Dataframed

Quantum computing is advancing fast, but it comes with a core industry challenge: noise. The big promise—better simulations, faster optimization, and maybe new kinds of AI—depends on quantum error correction and scaling from physical qubits to reliable logical qubits. For working professionals, that translates into system design questions, not just theory. How do you budget for the overhead of error correction? What does a hybrid quantum‑classical workflow look like when classical processors must process error data in real time? If a quantum approach shows “advantage” today, how do you know a better classical heuristic won’t catch up next month? Where should you focus first: hardware readiness or use cases?Dr. Yonatan Cohen is a physicist, entrepreneur, and co-founder of Quantum Machines, where he serves as Chief Technology Officer. He earned his Ph.D. at the Weizmann Institute of Science in Israel, focusing on quantum electronics, superconducting–semiconducting devices, and microfabrication. He is also a co-