  Quantum networks, which enable the transfer of quantum information across
long distances, promise to provide exciting benefits and new possibilities in
many areas including communication, computation, security, and metrology. These
networks rely on entanglement between qubits at distant nodes to transmit
information; however, creation of these quantum links is not dependent on the
information to be transmitted. Researchers have explored schemes for continuous
generation of entanglement, where network nodes may generate entanglement links
before receiving user requests. In this paper we present an adaptive scheme
that uses information from previous requests to better guide the choice of
randomly generated quantum links before future requests are received. We
analyze parameter spaces where such a scheme may provide benefit and observe an
increase in performance of up to 75% over other continuous schemes on
single-bottleneck and autonomous systems networks. We also test the scheme for
other parameter choices and observe continued benefits of up to 95%. The power
of our adaptive scheme on a randomized request queue is demonstrated on a
single-bottleneck topology. We also explore quantum memory allocation
scenarios, where a difference in latency performance implies the necessity of
optimal allocation of resources for quantum networks.
