AI for quantum computing met its referee at QCE26

This guide follows 51 talks on language models, learning agents and evolutionary search for quantum software at IEEE Quantum Week 2026 in Toronto. Their results held up best wherever an exact check stood behind them. On the afternoon of Thursday 17 September, Juan Cruz-Benito of IBM Research described a search that went very well and then very badly. Their team let a language model write small programs that generate error-correcting codes, then ran a huge pile of candidates through the search and kept the best. Several of the codes looked like new records. Then a stricter check showed that the fast score behind the search had been too generous, in one case by up to twelve times. For anyone building AI for quantum computing, it was the most useful failure of the week, because it showed a strong player judged by a lenient referee. Writing quantum software is hard in a particular way. A program is a recipe of gates applied to qubits, usually written in Python with IBM’s Qiskit or Xanadu’s PennyLane. The programmer must reason about amplitudes, the weights a quantum state gives each outcome, which can never be seen directly. A wrong program often returns results that…