Distributed quantum computing allows the modular construction of large-scale quantum computers and enables new protocols for blind quantum computation.However, such applications in the large-scale, fault-tolerant regime place stringent demands on the fidelity and rate of entanglement generation, which are not met by existing methods for quantum interconnects.In this work, we develop constant-rate entanglement distillation methods to address this bottleneck in the setting of noisy local operations.By using a sequence of two-way entanglement distillation protocols based on quantum error detecting codes with increasing rate, and combining with standard fault tolerance techniques, we achieve constant-rate entanglement distillation.We show that the scheme has constant-rate in expectation, and further numerically optimize to achieve low practical overhead under memory constraints.We find that compared to existing quantum interconnect * Both authors contributed equally to this research.

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.

Because of the largest number of qubits available, and the massive parallel execution of entangling two-qubit gates, atom arrays is a promising platform for quantum computing. The qubits are selectively loaded into arrays of optical traps, some of which can be moved during the computation itself. By adjusting the locations of the traps and shining a specific global laser, different pairs of qubits, even those initially far away, can be entangled at different stages of the quantum program execution. In comparison, previous QC architectures only generate entanglement on a fixed set of quantum register pairs. Thus, reconfigurable atom arrays (RAA) present a new challenge for QC compilation, especially the qubit mapping/layout synthesis stage which decides the qubit placement and gate scheduling. In this paper, we consider an RAA QC architecture that contains multiple arrays, supports 2D array movements, represents cutting-edge experimental platforms, and is much more general than previous works. We start by systematically examining the fundamental constraints on RAA imposed by physics. Built upon this understanding, we discretize the state space of the architecture, and we formulate layout synthesis for such an architecture to a satisfactory modulo theories problem. Finally, we demonstrate our work by compiling the quantum approximate optimization algorithm (QAOA), one of the promising near-term quantum computing applications. Our layout synthesizer reduces the number of required native two-qubit gates in 22-qubit QAOA by 5.72x (geomean) compared to leading experiments on a superconducting architecture. Combined with a better coherence time, there is an order-of-magnitude increase in circuit fidelity.