[Paper] Efficient Time-Aware Partitioning of Quantum Circuits for Distributed Quantum Computing

Source: arXiv - 2603.04126v1

Overview

The paper tackles a bottleneck in distributed quantum computing (DQC): the costly quantum communication required when a quantum circuit is split across multiple smaller quantum processors (QPUs). By introducing a time‑aware beam‑search heuristic, the authors provide a fast, scalable way to partition circuits that dramatically cuts teleportation overhead, making DQC more practical for near‑term hardware.

Key Contributions

• Time‑aware partitioning heuristic: A beam‑search algorithm that builds qubit‑to‑QPU assignments step‑by‑step, explicitly accounting for when and where inter‑QPU communication occurs.
• Complexity guarantees: The method runs in O(n² · d) time (n = number of qubits, d = circuit depth) and uses linear space, a huge improvement over exponential‑time metaheuristics.
• Topology‑sensitive optimization: The algorithm can incorporate arbitrary network topologies (e.g., line, ring, fully‑connected) to further reduce communication cost.
• Extensive empirical evaluation: Across a wide range of synthetic benchmarks (varying qubit counts, depths, and topologies), the approach consistently outperforms static graph‑partitioning baselines, often by 30‑70 % lower communication cost.
• Open‑source prototype: The authors release a lightweight compiler module that can be plugged into existing quantum software stacks.

Methodology

1. Circuit model: The input is a gate‑level quantum circuit represented as a sequence of layers (time steps). Each layer contains gates that can be executed in parallel.
2. Beam search formulation:
   • At each time step, the algorithm expands a set of partial qubit‑placement solutions (the “beam”).
   • For each partial solution, it evaluates the incremental communication cost incurred by the gates in the current layer (e.g., a CNOT between qubits on different QPUs triggers a teleportation).
   • Only the top‑k lowest‑cost partial solutions survive to the next step (k = beam width).
3. Cost function: Combines teleportation count, gate‑teleportation depth, and a topology‑aware distance metric (e.g., number of hops between QPUs).
4. Final assignment: After processing all layers, the best beam entry yields a full schedule of qubit‑to‑QPU mappings that minimizes the summed communication cost.
5. Complexity analysis: Because each layer processes at most k candidates and each candidate updates O(n) qubit assignments, the total runtime is quadratic in n and linear in depth d.

Results & Findings

• Speed: The beam‑search runs in seconds for circuits with >30 qubits, whereas a comparable simulated‑annealing metaheuristic required minutes to hours.
• Scalability: Communication savings hold steady as depth grows, indicating the algorithm’s ability to handle long‑running algorithms (e.g., variational quantum eigensolvers).
• Robustness to topology: Even with sparse inter‑QPU links (line topology), the method finds placements that cluster heavily interacting qubits, dramatically reducing long‑range teleportations.

Practical Implications

• Compiler integration: Quantum SDKs (Qiskit, Cirq, Braket) can adopt the heuristic as a drop‑in partitioning pass, enabling developers to target DQC hardware without hand‑crafting qubit layouts.
• Resource budgeting: By quantifying expected teleportation overhead early, system architects can better size interconnect bandwidth and error‑correction resources.
• Hybrid workloads: For algorithms that mix classical and quantum steps (e.g., QAOA), the fast partitioner allows on‑the‑fly recompilation as problem instances change.
• Edge‑to‑cloud quantum services: Companies planning to stitch together small quantum processors at the edge can use this tool to keep communication latency low, improving end‑to‑end application performance.

Limitations & Future Work

• Heuristic nature: While the beam‑search consistently beats static baselines, it does not guarantee a globally optimal partition; occasional pathological circuits may still incur higher cost than a full metaheuristic search.
• Fixed beam width: The trade‑off between runtime and solution quality hinges on the chosen beam width; adaptive strategies were not explored.
• Error model omission: The current cost function treats all teleportations equally, ignoring fidelity variations across links—a realistic DQC deployment would need to weight noisy channels differently.
• Dynamic re‑partitioning: Future work could extend the approach to handle runtime changes (e.g., QPU failures) by incrementally updating the beam rather than recomputing from scratch.

Overall, the paper delivers a practical, low‑overhead compiler technique that brings distributed quantum computing a step closer to real‑world deployment.

Authors

• Raymond P. H. Wu
• Chathu Ranaweera
• Sutharshan Rajasegarar
• Ria Rushin Joseph
• Jinho Choi
• Seng W. Loke

Paper Information

• arXiv ID: 2603.04126v1
• Categories: quant-ph, cs.DC
• Published: March 4, 2026
• PDF: Download PDF