A Scalable Distributed Quantum Optimization Framework via Factor Graph Paradigm

• URL: http://arxiv.org/abs/2603.07673v1
• Date: Sun, 08 Mar 2026 15:15:52 GMT
• Title: A Scalable Distributed Quantum Optimization Framework via Factor Graph Paradigm
• Abstract summary: We introduce a structure-aware framework for distributed quantum optimization.<n>We find that for a search space of size $N$, our framework achieves $O(sqrtN)$ query complexity up to processors and separator dependent factors.<n>We show that structure-aware decomposition delivers a practical path to scalable distributed quantum optimization on quantum networks.
• Score: 46.08923284345648
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Distributed quantum computing (DQC) connects many small quantum processors into a single logical machine, offering a practical route to scalable quantum computation. However, most existing DQC paradigms are structure-agnostic. Circuit cutting proposed by Peng et al. in [Phys. Rev. Lett., Oct. 2020] reduces per-device qubits at the cost of exponential classical post-processing, while search-space partitioning proposed by Avron et al. in [Phys. Rev. A., Nov. 2021] distributes the workload but weakens Grover's ideal quadratic speedup. In this paper, we introduce a structure-aware framework for distributed quantum optimization that resolves this complexity-resource trade-off. We model the objective function as a factor graph and expose its sparse interaction structure. We cut the graph along its natural ``seams'', i.e., a separator of boundary variables, to obtain loosely coupled subproblems that fit on resource-constrained processors. We coordinate these subproblems with shared entanglement, so the network executes a single globally coherent search rather than independent local searches. We prove that this design preserves Grover-like scaling: for a search space of size $N$, our framework achieves $O(\sqrt{N})$ query complexity up to processors and separator dependent factors, while relaxing the qubit requirement of each processor. We extend the framework with a hierarchical divide-and-conquer strategy that scales to large-scale optimization problems and supports two operating modes: a fully coherent mode for fault-tolerant networks and a hybrid mode that inserts measurements to cap circuit depth on near-term devices. We validate the predicted query-entanglement trade-offs through simulations over diverse network topologies, and we show that structure-aware decomposition delivers a practical path to scalable distributed quantum optimization on quantum networks.

Related papers

• Congruence Decomposition with Neural Block Solvers for Large-Scale PCI Assignment [43.4438977596482]
  Physical Cell Identity assignment is essential for interference management in 5G networks.<n>We propose a congruence decomposition framework with neural block solvers for large-scale PCI assignment.<n> Experiments on synthetic cellular graphs and real-world 5G networks show that the proposed solvers outperforms existing modular-interference baselines.
  arXiv  Detail & Related papers  (2026-08-21T12:08:36Z)
• COMPAS: A Distributed Multi-Party SWAP Test for Parallel Quantum Algorithms [4.584616394519209]
  We introduce COMPAS, an architecture that realizes multivariate trace estimation across a multi-party network of interconnected modular and distributed QPUs.<n>Unlike other schemes, which must choose between optimality in circuit depth or GHZ width, COMPAS achieves both at once.<n>We analyze network-level errors and simulate the effects of circuit-level noise on the architecture.
  arXiv  Detail & Related papers  (2025-11-28T18:31:15Z)
• Network Operations Scheduling for Distributed Quantum Computing [0.0]
  We compare and contrast two approaches to solving the make span minimization problem.<n>One approach is based on the resource constrained project scheduling (RCPSP) framework, and another based on a greedy algorithm.<n>Our results illustrate the effectiveness of the RCPSP framework, while also underlining the relevance and usefulness of greedy algorithms.
  arXiv  Detail & Related papers  (2025-11-17T18:40:50Z)
• Optimal multicore quantum computing with few interconnects [0.0]
  We study the complexity behavior of a paradigmatic universal family of random circuits distributed into several cores.<n>We find that the optimal complexity is reached with few interconnects.<n>We also analyze the complexity properties when scaling processors up by means of adding cores of the same size.
  arXiv  Detail & Related papers  (2025-01-17T15:23:54Z)
• DynaSplit: A Hardware-Software Co-Design Framework for Energy-Aware Inference on Edge [40.96858640950632]
  We propose DynaSplit, a framework that dynamically configures parameters across both software and hardware. We evaluate DynaSplit using popular pre-trained NNs on a real-world testbed. Results show a reduction in energy consumption up to 72% compared to cloud-only computation.
  arXiv  Detail & Related papers  (2024-10-31T12:44:07Z)
• Mapping quantum circuits to modular architectures with QUBO [3.0148208709026005]
  In multi-core architectures, it is crucial to minimize the amount of communication between cores when executing an algorithm. We propose for the first time a Quadratic Unconstrained Binary Optimization technique to encode the problem and the solution. Our method showed promising results and performed exceptionally well with very dense and highly-parallelized circuits.
  arXiv  Detail & Related papers  (2023-05-11T09:45:47Z)
• Majorization-based benchmark of the complexity of quantum processors [105.54048699217668]
  We numerically simulate and characterize the operation of various quantum processors. We identify and assess quantum complexity by comparing the performance of each device against benchmark lines. We find that the majorization-based benchmark holds as long as the circuits' output states have, on average, high purity.
  arXiv  Detail & Related papers  (2023-04-10T23:01:10Z)
• An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic [72.35307086274912]
  High-dimension parameter model and large-scale mathematical calculation restrict execution efficiency, especially for Internet of Things (IoT) devices. We propose a new Deep Reinforcement Learning (DRL)-Soft Actor Critic for discrete (SAC-d), which generates the emphexit point, emphexit point, and emphcompressing bits by soft policy iterations. Based on the latency and accuracy aware reward design, such an computation can well adapt to the complex environment like dynamic wireless channel and arbitrary processing, and is capable of supporting the 5G URL
  arXiv  Detail & Related papers  (2022-01-09T09:31:50Z)
• Scaling Quantum Approximate Optimization on Near-term Hardware [49.94954584453379]
  We quantify scaling of the expected resource requirements by optimized circuits for hardware architectures with varying levels of connectivity. We show the number of measurements, and hence total time to synthesizing solution, grows exponentially in problem size and problem graph degree. These problems may be alleviated by increasing hardware connectivity or by recently proposed modifications to the QAOA that achieve higher performance with fewer circuit layers.
  arXiv  Detail & Related papers  (2022-01-06T21:02:30Z)
• Realization of arbitrary doubly-controlled quantum phase gates [62.997667081978825]
  We introduce a high-fidelity gate set inspired by a proposal for near-term quantum advantage in optimization problems. By orchestrating coherent, multi-level control over three transmon qutrits, we synthesize a family of deterministic, continuous-angle quantum phase gates acting in the natural three-qubit computational basis.
  arXiv  Detail & Related papers  (2021-08-03T17:49:09Z)
• Divide and Conquer for Combinatorial Optimization and Distributed Quantum Computation [3.8221353389253676]
  We introduce the Quantum Divide and Conquer Algorithm (QDCA), a hybrid variational approach to mapping large optimization problems onto distributed quantum architectures. This is achieved through the combined use of graph partitioning and quantum circuit cutting. We simulate the QDCA on instances of the Maximum Independent Set problem and find that it is able to outperform similar classical algorithms.
  arXiv  Detail & Related papers  (2021-07-15T18:00:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.