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Distribution of Quantum Circuits Over General Quantum Networks

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arxiv 2206.06437 v1 pith:QVOB5QAG submitted 2022-06-13 cs.ET quant-ph

classification cs.ETquant-ph
keywords quantumcomputerscat-entanglementcircuitscommunicationqubitsacrossalgorithm
verification ladder T0 review T1 audit T2 compute T3 formal
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Near-term quantum computers can hold only a small number of qubits. One way to facilitate large-scale quantum computations is through a distributed network of quantum computers. In this work, we consider the problem of distributing quantum programs represented as quantum circuits across a quantum network of heterogeneous quantum computers, in a way that minimizes the overall communication cost required to execute the distributed circuit. We consider two ways of communicating: cat-entanglement that creates linked copies of qubits across pairs of computers, and teleportation. The heterogeneous computers impose constraints on cat-entanglement and teleportation operations that can be chosen by an algorithm. We first focus on a special case that only allows cat-entanglements and not teleportations for communication. We provide a two-step heuristic for solving this specialized setting: (i) finding an assignment of qubits to computers using Tabu search, and (ii) using an iterative greedy algorithm designed for a constrained version of the set cover problem to determine cat-entanglement operations required to execute gates locally. For the general case, which allows both forms of communication, we propose two algorithms that subdivide the quantum circuit into several portions and apply the heuristic for the specialized setting on each portion. Teleportations are then used to stitch together the solutions for each portion. Finally, we simulate our algorithms on a wide range of randomly generated quantum networks and circuits, and study the properties of their results with respect to several varying parameters.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Architecture-Aware Reinforcement Learning for Communication-Efficient Distributed Quantum Circuit Compilation

    quant-ph 2026-08 conditional novelty 5.0 of 10

    A reinforcement-learning agent trained to schedule inter-QPU communication in distributed quantum circuits matches heuristic compilers on structured benchmarks, with small gains from lookahead rewards on random circuits.

  2. Optimized Quantum Circuit Partitioning Across Multiple Quantum Processors

    quant-ph 2025-01 reject novelty 4.0 of 10

    A window-based circuit partitioning heuristic with dynamic one-way teleportation reduces EPR pairs versus a static baseline, and a structured QFT distribution uses nm/2 EPR pairs on m processors.

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