Neutral atom arrays have recently emerged as a promising platform for fault-tolerant quantum computing.Based on these advances, including dynamically-reconfigurable connectivity and fast transversal operations, we present a low-overhead architecture that supports the layout and resource estimation of large-scale fault-tolerant quantum algorithms.Utilizing recent advances in fault tolerance with transversal gate operations, this architecture achieves a run time speed-up on the order of the code distance 𝑑, which we find directly translates to run time improvements of large-scale quantum algorithms.Our architecture consists of functional building blocks of key algorithmic subroutines, including magic state factories, quantum arithmetic units, and quantum look-up tables.These building blocks are implemented using efficient transversal operations, and we design space-time efficient versions of them that minimize interaction We acknowledge helpful discussions with M. Beverland, A.

Maximum Independent Set (MIS) is an NP-hard optimization problem with wide-ranging applications in science and technology. Recently, a super-linear speedup over classical simulated annealing in solving MIS was experimentally observed using a Rydberg atom array (RAA) quantum computer. The extent of the observed speedup depended on the graph instance and the circuit depth of the quantum algorithm. Due to the limited availability of RAA, it is beneficial to be able to efficiently predict the quantum optimization performance on a given graph and circuit depth prior to running it. In this work, we present a graph neural network (GNN)-based performance predictor of the RAA-based MIS optimizer. Our experimental results achieve accuracy with an average root mean squared error (RMSE) of 0.03 out of the range [0, 1]. We open source the experimental data collected for this study at https://github.com/UCLA-VAST/RAAMIS.