Discrete Flow-Based Generative Models for Measurement Optimization in Quantum Computing
GFlowNets are a generative model that colors molecular qubit Hamiltonians to find non-overlapping commuting groupings. This approach optimizes quantum simulations by sampling groupings based on a reward function balancing measurement cost, circuit count, and two-qubit gate counts. This allows for resource-aware initialization of algorithms like iterative coefficient splitting.