Notes d’épisode
This episode examines a paper that uses an LLM-driven evolutionary search (OpenEvolve with Claude Mythos 5) to find quantum arithmetic circuits needing fewer Toffoli gates and fewer qubits than the best published designs across eight tasks. These tasks include decoders for decoded quantum interferometry, modular inversion, and chemistry and lattice-gauge workloads. It explains why quantum arithmetic is hard to write: coherent control flow forces every branch to run, reversibility requires uncomputation, and a stated failure budget can be traded for savings. It also covers how candidates are checked classically by tracking basis states and phases, so hallucinated circuits are rejected by an exact evaluator. The discussion places the work against FunSearch, AlphaEvolve and rewrite-based optimizers, and notes the trade-off that correctness is statistical rather than proved. It also flags the Anthropic affiliation and the simplification of equally weighting gate count and qubit count into one cost metric.
Sources:
1. Discovering state-of-the-art quantum circuits with large language models — Andi Gu, Alek H. Kemeny, Susanne F. Yelin, Mikhail D. Lukin, 2026
http://arxiv.org/abs/2610.22367
2. How to factor 2048 bit RSA integers in 8 hours using 20 million noisy qubits — Craig Gidney, Martin Ekera, 2019 (Quantum 2021)
https://scholar.google.com/scholar?q=How+to+factor+2048+bit+RSA+integers+in+8+hours+using+20+million+noisy+qubits
3. Encoding Electronic Spectra in Quantum Circuits with Linear T Complexity — Ryan Babbush, Craig Gidney, Dominic Berry, Nathan Wiebe, Jarrod McClean, Alexandru Paler, Austin Fowler, Hartmut Neven, 2018
https://scholar.google.com/scholar?q=Encoding+Electronic+Spectra+in+Quantum+Circuits+with+Linear+T+Complexity
4. AlphaEvolve: A coding agent for scientific and algorithmic discovery (and the preceding FunSearch: Mathematical discoveries from program search with large language models) — Alexander Novikov, Ngan Vu, Marvin Eisenberger, et al. (Google DeepMind); FunSearch: Bernardino Romera-Paredes, Mohammadamin Barekatain, et al., 2025 (AlphaEvolve); 2023 (FunSearch, Nature 2024)
https://scholar.google.com/scholar?q=AlphaEvolve%3A+A+coding+agent+for+scientific+and+algorithmic+discovery+%28and+the+preceding+FunSearch%3A+Mathematical+discoveries+from+program+search+with+large+language+models%29
5. Discovering faster matrix multiplication algorithms with reinforcement learning (AlphaTensor) and Quantum Circuit Optimization with AlphaTensor (AlphaTensor-Quantum) — Alhussein Fawzi et al.; Francisco J. R. Ruiz et al. (DeepMind), 2022 (AlphaTensor); 2024 (AlphaTensor-Quantum, Nature Machine Intelligence 2025)
https://scholar.google.com/scholar?q=Discovering+faster+matrix+multiplication+algorithms+with+reinforcement+learning+%28AlphaTensor%29+and+Quantum+Circuit+Optimization+with+AlphaTensor+%28AlphaTensor-Quantum%29
6. Logical Reversibility of Computation — Charles H. Bennett…