Distributed quantum computing (DQC) offers a pathway for scaling up quantum computing architectures beyond the confines of a single chip. Entanglement is a crucial resource for implementing nonlocal operations in DQC, and it is required to allow teleportation of quantum states and gates. Remote entanglement generation in practical systems is probabilistic, has longer duration than that of local operations, and is nondeterministic. Therefore, optimizing the performance of probabilistic remote entanglement generation is critically important for the performance of DQC architectures. In this paper we propose and study a new DQC architecture that combines (1) buffering of successfully generated entanglement, (2) asynchronously attempted entanglement generation, and (3) adaptive scheduling of remote gates based on the entanglement generation pattern. We show that our hardware-software co-design improves both the runtime and the output fidelity under a realistic model of DQC.

Quantum entanglement is an essential resource for quantum networks, thus generation and distribution of entanglement between remote network nodes are two important tasks faced by practical implementation of quantum networks. Given that the number and scale of real quantum network testbeds are still limited we use simulation to study entanglement generation and distribution, aiming at providing insight and guidance for quantum network construction. We extend the functionality of the open-source Simulator of QUantum Network Communication (SeQUeNCe) developed by our group to support representation of quantum state under Fock basis, and use it to simulate entanglement generation between two atomic frequency comb (AFC) absorptive quantum memories. Regarding entanglement distribution, based on the so-called continuous generation scheme, we propose an adaptive scheme that uses history request information to better guide the choice of randomly generated quantum links before receiving future requests. We demonstrate the concept and power of our adaptive scheme, and explore relevant quantum memory allocation scenarios through simulation.