Defining the Quantum-GPU Supercomputer
Quantum algorithms have long been designed in the abstract — assuming perfect gates, idealized error models, and hardware that doesn't yet exist. That gap is closing, but not just because hardware is improving. The real shift is that the systems layer is finally catching up: AI-driven calibration is delivering cleaner, more characterizable qubits; real-time decoders are making error correction practically viable; and ultra-low-latency quantum–classical links are enabling the hybrid feedback loops that the most promising near-term algorithms demand. This talk examines what that means for algorithm design and discovery. Accelerated emulation is compressing the iteration cycle, letting us probe algorithmic behavior at scales and noise regimes previously out of reach. Higher-fidelity, better-characterized hardware is expanding the class of circuits worth designing for. And tighter quantum–classical integration is opening the door to variational, adaptive, and fault-tolerant algorithms that require real-time classical co-processing at scale. Rather than waiting for hardware to mature, the emerging blueprint of an accelerated quantum supercomputer gives algorithm designers a concrete, near-term target — one where co-design between the algorithmic and systems layers is the fastest route to demonstrating genuine quantum advantage.
BIOGRAPHY
Elica is Director of Quantum Algorithm Engineering at NVIDIA, where she leads the development and implementation of quantum algorithms for NVIDIA's hybrid quantum-classical computing platform CUDA-Q. With more than 15 years of experience in the field of quantum computing, Elica has held positions in academia, venture capital, and industry. Most recently, she was Program Director of Quantum for Bio, a $50M program by Wellcome Leap, whose goal was to develop quantum algorithms for pressing human health challenges.