Quantum computing has been "5 years away" for decades. So what's actually holding it back? In this episode of Eye on AI, Craig Smith sits down with Izhar Medalsy, Co-founder & CEO of Quantum Elements, to break down the real bottleneck in quantum computing today and why the future of the industry may depend more on classical systems and AI than quantum hardware itself. Izhar explains how digital twins of quantum systems are being used to simulate real hardware, generate massive amounts of training data, and solve one of the biggest challenges in the field: noise and error correction. They dive into how his team improved Shor's Algorithm from 80% to 99% accuracy on IBM hardware, without changing the hardware itself, and what that means for the future of quantum performance. The conversation also explores how AI is being used to optimise quantum systems, why classical computing will continue to play a central role in quantum development, and what milestones to watch as the industry moves closer to real-world applications. If you want to understand where quantum computing actually stands today and what will unlock its next phase, this episode gives you a clear, grounded perspective. Subscribe for more conversations with the people building the future of AI and emerging technology.
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(00:00) The 99% Accuracy Breakthrough (Quantum's Turning Point) (01:03) Why Quantum Hardware Alone Isn't Enough (03:50) Digital Twins Explained (The Missing Layer) (08:09) The Real Problem: Noise, Instability & Environment (15:43) From 80% to 99% on Shor's Algorithm (26:36) How AI Is Fixing Quantum's Biggest Bottleneck (33:53) Inside the Platform: From Circuit to Optimization (40:51) Logical Qubits & Scaling Quantum Systems (43:34) The Limits of Simulation vs Real Quantum Hardware (54:29) When Quantum Becomes Useful (Real Timeline)
