Routing in Non-Isotonic Quantum Networks

• URL: http://arxiv.org/abs/2511.20628v1
• Date: Tue, 25 Nov 2025 18:48:49 GMT
• Title: Routing in Non-Isotonic Quantum Networks
• Authors: Maxwell Tang, Garrett Hinkley, Kenneth Goodenough, Stefan Krastanov, Guus Avis,
• Abstract summary: Optimal routing in quantum-repeater networks requires finding the best path that connects a pair of end nodes.<n>We show that utility functions that take into account both the rate and quality of the entanglement generation are often non-isotonic.<n>We present two best-first-search algorithms that use destination-aware merit functions for faster convergence.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Optimal routing in quantum-repeater networks requires finding the best path that connects a pair of end nodes. Most previous work on routing in quantum networks assumes utility functions that are isotonic, meaning that the ordering of two paths does not change when extending both with the same edge. However, we show that utility functions that take into account both the rate and quality of the entanglement generation (e.g., the secret-key rate) are often non-isotonic. This makes pathfinding difficult as classical algorithms such as Dijkstra's become unsuitable, with the state of the art for quantum networks being an exhaustive search over all possible paths. In this work we present improved algorithms. First, we present two best-first-search algorithms that use destination-aware merit functions for faster convergence. One of these provably finds the best path, while the other uses heuristics to achieve an effectively sublinear scaling of the query count in the network size while in practice always finding a close-to-optimal path. Second, we present metaheuristic algorithms (simulated annealing and a genetic algorithm) that enable tuning a tradeoff between path quality and computational overhead. While we focus on swap-ASAP quantum repeaters for concreteness, our algorithms are readily generalized to different repeater schemes and models.

Related papers

• TANGO: A Robust Qubit Mapping Algorithm via Two-Stage Search and Bidirectional Look [7.064817742048067]
  Current quantum devices lack full qubit connectivity, making it difficult to directly execute logical circuits on quantum devices.<n>We propose the TANGO algorithm, which balances the impact of qubit mapping on both mapped and unmapped nodes.<n>We show that the algorithm achieves multi-objective co-optimization of gate count and circuit depth across various benchmarks and quantum devices.
  arXiv  Detail & Related papers  (2025-03-10T13:44:16Z)
• Solving quadratic binary optimization problems using quantum SDP methods: Non-asymptotic running time analysis [1.9081120388919084]
  Quantum computers can solve semidefinite programs (SDPs) using resources that scale better than state-of-the-art classical methods.<n>We present an analysis of the non-asymptotic resource requirements of a quantum SDP solver.
  arXiv  Detail & Related papers  (2025-02-21T12:54:05Z)
• Algorithm-Oriented Qubit Mapping for Variational Quantum Algorithms [3.990724104767043]
  Quantum algorithms implemented on near-term devices require qubit mapping due to noise and limited qubit connectivity.<n>We propose a strategy called algorithm-oriented qubit mapping (AOQMAP) that aims to bridge the gap between exact and scalable mapping methods.
  arXiv  Detail & Related papers  (2023-10-15T13:18:06Z)
• Automatic and effective discovery of quantum kernels [41.61572387137452]
  Quantum computing can empower machine learning models by enabling kernel machines to leverage quantum kernels for representing similarity measures between data.<n>We present an approach to this problem, which employs optimization techniques, similar to those used in neural architecture search and AutoML.<n>The results obtained by testing our approach on a high-energy physics problem demonstrate that, in the best-case scenario, we can either match or improve testing accuracy with respect to the manual design approach.
  arXiv  Detail & Related papers  (2022-09-22T16:42:14Z)
• A single $T$-gate makes distribution learning hard [56.045224655472865]
  This work provides an extensive characterization of the learnability of the output distributions of local quantum circuits. We show that for a wide variety of the most practically relevant learning algorithms -- including hybrid-quantum classical algorithms -- even the generative modelling problem associated with depth $d=omega(log(n))$ Clifford circuits is hard.
  arXiv  Detail & Related papers  (2022-07-07T08:04:15Z)
• Locality-aware Qubit Routing for the Grid Architecture [1.4459640831465588]
  We introduce a locality-aware qubit routing algorithm based on a graph theoretic framework. Our algorithm is designed for the grid and certain "grid-like" architectures.
  arXiv  Detail & Related papers  (2022-03-21T20:46:39Z)
• Waypoint Planning Networks [66.72790309889432]
  We propose a hybrid algorithm based on LSTMs with a local kernel - a classic algorithm such as A*, and a global kernel using a learned algorithm. We compare WPN against A*, as well as related works including motion planning networks (MPNet) and value networks (VIN) It is shown that WPN's search space is considerably less than A*, while being able to generate near optimal results.
  arXiv  Detail & Related papers  (2021-05-01T18:02:01Z)
• Towards Optimally Efficient Tree Search with Deep Learning [76.64632985696237]
  This paper investigates the classical integer least-squares problem which estimates signals integer from linear models. The problem is NP-hard and often arises in diverse applications such as signal processing, bioinformatics, communications and machine learning. We propose a general hyper-accelerated tree search (HATS) algorithm by employing a deep neural network to estimate the optimal estimation for the underlying simplified memory-bounded A* algorithm.
  arXiv  Detail & Related papers  (2021-01-07T08:00:02Z)
• Purification and Entanglement Routing on Quantum Networks [55.41644538483948]
  A quantum network equipped with imperfect channel fidelities and limited memory storage time can distribute entanglement between users. We introduce effectives enabling fast path-finding algorithms for maximizing entanglement shared between two nodes on a quantum network.
  arXiv  Detail & Related papers  (2020-11-23T19:00:01Z)
• Learning to Accelerate Heuristic Searching for Large-Scale Maximum Weighted b-Matching Problems in Online Advertising [51.97494906131859]
  Bipartite b-matching is fundamental in algorithm design, and has been widely applied into economic markets, labor markets, etc. Existing exact and approximate algorithms usually fail in such settings due to either requiring intolerable running time or too much computation resource. We propose textttNeuSearcher which leverages the knowledge learned from previously instances to solve new problem instances.
  arXiv  Detail & Related papers  (2020-05-09T02:48:23Z)
• Lagrangian Decomposition for Neural Network Verification [148.0448557991349]
  A fundamental component of neural network verification is the computation of bounds on the values their outputs can take. We propose a novel approach based on Lagrangian Decomposition. We show that we obtain bounds comparable with off-the-shelf solvers in a fraction of their running time.
  arXiv  Detail & Related papers  (2020-02-24T17:55:10Z)

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.