IonQ, a quantum computing company, has demonstrated a significant advance in how artificial intelligence (AI) can speed up the design of quantum hardware. Working with researchers at Oak Ridge National Laboratory and chipmaker NVIDIA, the team showed that generative AI can find quantum optimization circuits in about 28 seconds, a task that previously took roughly 11 minutes.
This research has been presented at IEEE Quantum Week in Toronto, and described in a preprint published in arXiv.
“This work brings generative AI, quantum computing, and high-performance computing together to tackle large-scale, complex optimization problems," said Oak Ridge National Laboratory spokespersons. "AI can become a new computational layer for quantum circuit synthesis, enabling the automatic design and optimization of quantum circuits for increasingly complex problems."
NVIDIA spokespersons added that this and similar research is laying the foundation for the next generation of advances in quantum computing and applications.
What the breakthrough means for quantum computing
Quantum computers need specialized circuits to run algorithms. Finding the right circuit design traditionally requires lengthy trial and error. The new approach uses generative AI. The researchers achieved these results using a single NVIDIA H200 graphics processing unit running on an Oak Ridge supercomputer, along with NVIDIA's open-source CUDA-Q platform. A qubit is the basic unit of quantum information, analogous to a bit in classical computing but able to exist in multiple states simultaneously. The test involved 12 qubits, a modest scale by quantum computing standards but large enough to demonstrate the technique's potential.
The results strengthen IonQ's argument that hybrid systems combining classical AI and quantum hardware offer a practical path forward for the field. Faster circuit design could eventually lower costs for commercial clients.
This development is important because it addresses one of quantum computing's practical bottlenecks: the time and expertise required to program these machines. If generative AI can reliably accelerate circuit design, it could help bridge the gap between theoretical quantum advantages and real-world deployment.