SeQUeNCe.DQC: Full-stack Simulation of Distributed Quantum Computing

Abstract

Distributed quantum computing (DQC) connects multiple quantum processing units to overcome the scale limits of monolithic hardware. We introduce SeQUeNCe$.$DQC a new top-level module within the SeQUeNCe package that compiles monolithic circuits into topology-aware execution plans and coordinates their execution using SeQUeNCe's existing quantum-network modules and protocols. We validate SeQUeNCe$.$DQC by reproducing a two-node trapped-ion Grover experiment in simulation and comparing success probabilities with the reported 71% benchmark. We then evaluate how compiler strategy, topology, and memory capacity jointly govern distributed circuit execution time and success rate. In these experiments, our hybrid compilers successfully identified mixed TeleGate/TeleData execution plans that outperformed pure gate-teleportation strategies. By capturing the physical overhead of distributed quantum primitives, SeQUeNCe$.$DQC provides a rigorous platform for co-designing DQC software and hardware architectures.

SeQUeNCe.DQC addresses how circuit compilation and quantum-network dynamics jointly determine distributed quantum computing runtime and success probability. It extends the SeQUeNCe discrete-event simulator with topology-aware compilation, controller–worker execution, and remote gate and state teleportation. Execution includes probabilistic entanglement generation, classical signaling, entanglement swapping, communication-memory contention, and noise. A common compiled-program interface supports built-in and external compilers; density-matrix and stochastic state-vector backends support different accuracy–cost trade-offs. Validation uses a two-node, two-qubit trapped-ion Grover experiment. With the experiment’s component error budget represented as noise channels, density-matrix simulations give $72.9\%$ mean success over $40{,}000$ shots, compared with the reported experimental $71\%$. Further Grover simulations examine compilation, topology, and memory allocation. For a four-search-qubit circuit on two nodes, a hybrid plan using 6 remote gates and 24 state moves takes $31.4$ ms, versus $54.6$ ms for the fixed-placement QAP baseline—a $42\%$ reduction—and achieves $89.3\%$ success. Ten-node topology comparisons retain 588 remote gates but yield success probabilities from $15.0\%$ to $27.1\%$, reflecting different paths and Bell-pair requirements. The evaluations are restricted to small Grover workloads and uncorrected noise, with fully connected, equivalent qubits within each QPU. The state-vector backend approximates relaxation using stochastic resets. Full-state simulation remains costly: the largest case, 18 circuit qubits, takes approximately 71 minutes per execution. Internal QPU routing, fault tolerance, switch-centric networks, and overlapping distributed jobs are left for future work.