QuICS Special Seminar: Andi Gu
Description
Title:Â Discovering state-of-the-art quantum circuits with large language models
Speaker:  Andi Gu (Harvard University)
Date & Time:Â Â November 12, 2026, 10:30am
Where to Attend:Â Â Virtual Via Zoom: https://umd.zoom.us/j/9893676372?pwd=VVNOd2xNZ3FCblk4aFdTMjkzTllvQT09&omn=95678034423 Meeting ID: 989 367 6372 Passcode: abc123
Realizing useful quantum advantage requires not only large-scale quantum processors but also efficient quantum algorithms. In many practical problems the dominant resource costs come from a small set of computational primitives, and optimizing them in the quantum setting is hard, in part because of a reversibility constraint absent from classical computation. Here we introduce an evolutionary framework in which large language models (LLMs) propose circuit designs and an independent evaluator verifies and scores them. On tasks spanning numerical computation, chemistry, optimization, quantum simulation, and number theory, the discovered circuits significantly reduce both Toffoli and qubit counts relative to the state of the art in seven of eight cases. In the eighth, the search finds a new reversible architecture for prime-field modular inversion that makes divsteps viable and substantially improves the Toffoli-qubit trade-off. These results open the door for evaluator-guided LLM co-design of new algorithmic structures for quantum computation.
*We strongly encourage attendees to use their full name (and if possible, their UMD credentials) to join the zoom session.*
*Please note that this talk is VIRTUAL ONLY*
Speaker:  Andi Gu (Harvard University)
Date & Time:Â Â November 12, 2026, 10:30am
Where to Attend:Â Â Virtual Via Zoom: https://umd.zoom.us/j/9893676372?pwd=VVNOd2xNZ3FCblk4aFdTMjkzTllvQT09&omn=95678034423 Meeting ID: 989 367 6372 Passcode: abc123
Realizing useful quantum advantage requires not only large-scale quantum processors but also efficient quantum algorithms. In many practical problems the dominant resource costs come from a small set of computational primitives, and optimizing them in the quantum setting is hard, in part because of a reversibility constraint absent from classical computation. Here we introduce an evolutionary framework in which large language models (LLMs) propose circuit designs and an independent evaluator verifies and scores them. On tasks spanning numerical computation, chemistry, optimization, quantum simulation, and number theory, the discovered circuits significantly reduce both Toffoli and qubit counts relative to the state of the art in seven of eight cases. In the eighth, the search finds a new reversible architecture for prime-field modular inversion that makes divsteps viable and substantially improves the Toffoli-qubit trade-off. These results open the door for evaluator-guided LLM co-design of new algorithmic structures for quantum computation.
*We strongly encourage attendees to use their full name (and if possible, their UMD credentials) to join the zoom session.*
*Please note that this talk is VIRTUAL ONLY*