  Variational quantum algorithms, inspired by neural networks, have become a
novel approach in quantum computing. However, designing efficient parameterized
quantum circuits remains a challenge. Quantum architecture search tackles this
by adjusting circuit structures along with gate parameters to automatically
discover high-performance circuit structures. In this study, we propose an
end-to-end distributed quantum architecture search framework, where we aim to
automatically design distributed quantum circuit structures for interconnected
quantum processing units with specific qubit connectivity. We devise a circuit
generation algorithm which incorporates TeleGate and TeleData methods to enable
nonlocal gate implementation across quantum processing units. While taking into
account qubit connectivity, we also incorporate qubit assignment from logical
to physical qubits within our quantum architecture search framework. A
two-stage progressive training-free strategy is employed to evaluate extensive
circuit structures without circuit training costs. Through numerical
experiments on three VQE tasks, the efficacy and efficiency of our scheme is
demonstrated. Our research into discovering efficient structures for
distributed quantum circuits is crucial for near-term quantum computing where a
single quantum processing unit has a limited number of qubits. Distributed
quantum circuits allow for breaking down complex computations into manageable
parts that can be processed across multiple quantum processing units.
