GFlowQEC：用生成流网络搜索量子 LDPC 纠错码
GFlowQEC: Generative Flow Networks that Learn to Sample Quantum LDPC Codes
量子纠错码搜索太烧算力，这篇用 GFlowNet 学着只提好码，找到的高分码是老方法的两倍多，搞量子码的可以看看。
量子纠错码候选需要电路级仿真评分，一次搜索通常只能跑几百次评估，因此每次评估都很昂贵。论文提出 GFlowQEC，据作者称是首个用于量子码设计的生成流网络（GFlowNet），每次添加一个群元素来构建码，并按分数的幂比例提议候选码。在 180 个物理比特的 lifted-product 码上，每轮付费评估排序 10 个候选时，它找到的全新高分码数量是调优后的 parallel tempering 的两倍以上，是有监督筛选的 3.5 倍以上。当每轮排序 100 个候选时，有监督筛选反而胜出，而 Soft Q-learning 在其中一种噪声偏置下表现更好。
GFlowQEC: Generative Flow Networks that Learn to Sample Quantum LDPC Codes
Choosing a quantum error-correcting code for a real device means scoring candidates with circuit-level simulations so slow that only a few hundred can be run. So every evaluation counts, and a search is judged by how many good codes it finds per paid evaluation. Quantum LDPC codes built from a few group elements protect many logical qubits, but good ones are rare in a huge space. We introduce GFlowQEC, to our knowledge the first generative flow network (GFlowNet) for quantum code design. It builds a code one group element at a time and learns to propose codes in proportion to a power of their score. We train it once on scores from earlier searches, freeze it, and let it propose lifted-product codes with 180 physical qubits, scored by their distances. Ranking ten of its draws per paid call, it finds more than twice as many new top-scoring codes (inequivalent to any it was trained on) as tuned parallel tempering, even when tempering starts from GFlowQEC's training codes. It finds more than three and a half times as many as a supervised screen ranking ten random candidates per call. When both rank a hundred, the screen beats GFlowQEC, and does best ranking GFlowQEC's draws. Soft Q-learning, which shares the GFlowNet's optimal policy, does as well at one noise bias and better at the other, so the gain does not depend on the GFlowNet's training loss. Trained proposers can make a budget of expensive evaluations go further when good codes are rare.