Optimized Quantum Embedding: A Universal Minor-Embedding Framework for Large Complete Bipartite Graph

• URL: http://arxiv.org/abs/2504.21112v1
• Date: Tue, 29 Apr 2025 18:44:12 GMT
• Title: Optimized Quantum Embedding: A Universal Minor-Embedding Framework for Large Complete Bipartite Graph
• Authors: Salvatore Sinno, Thomas Groß, Nicholas Chancellor, Bhavika Bhalgamiya, Arati Sahoo,
• Abstract summary: Minor embedding is essential for mapping largescale problems onto quantum annealers, particularly in quantum machine learning and optimization.<n>This work presents an optimized, universal minor-embedding framework that efficiently complete bipartite graphs onto the hardware topology of quantum annealers.
• Score: 0.5242869847419834
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Minor embedding is essential for mapping largescale combinatorial problems onto quantum annealers, particularly in quantum machine learning and optimization. This work presents an optimized, universal minor-embedding framework that efficiently accommodates complete bipartite graphs onto the hardware topology of quantum annealers. By leveraging the inherent topographical periodicity of the physical quantum adiabatic annealer processor, our method systematically reduces qubit chain lengths, resulting in enhanced stability, computational efficiency, and scalability of quantum annealing. We benchmark our embedding framework against Minorminer, the default heuristic embedding algorithm, for the Pegasus topology, demonstrating that our approach significantly improves embedding quality. Our empirical results show a 99.98% reduction in embedding time for a 120 x 120 complete bipartite graphs. Additionally, our method eliminates long qubit chains, which primarily cause decoherence and computational errors in quantum annealing. These findings advance the scalability of quantum embeddings, particularly for quantum generative models, anomaly detection, and large-scale optimization tasks. Our results establish a foundation for integrating efficient quantum-classical hybrid solutions, paving the way for practical applications in quantum-enhanced machine learning and optimization.

Related papers

• A Quantum Genetic Algorithm Framework for the MaxCut Problem [49.59986385400411]
  The proposed method introduces a Quantum Genetic Algorithm (QGA) using a Grover-based evolutionary framework and divide-and-conquer principles. On complete graphs, the proposed method consistently achieves the true optimal MaxCut values, outperforming the Semidefinite Programming (SDP) approach. On ErdHos-R'enyi random graphs, the QGA demonstrates competitive performance, achieving median solutions within 92-96% of the SDP results.
  arXiv  Detail & Related papers  (2025-01-02T05:06:16Z)
• Noise-Aware Distributed Quantum Approximate Optimization Algorithm on Near-term Quantum Hardware [2.753858051267023]
  This paper introduces a noise-aware distributed Quantum Approximate Optimization Algorithm (QAOA) tailored for execution on near-term quantum hardware. We address the limitations of current Noisy Intermediate-Scale Quantum (NISQ) devices, which are hindered by limited qubit counts and high error rates.
  arXiv  Detail & Related papers  (2024-07-24T14:50:01Z)
• Bias-field digitized counterdiabatic quantum optimization [39.58317527488534]
  We call this protocol bias-field digitizeddiabatic quantum optimization (BF-DCQO) Our purely quantum approach eliminates the dependency on classical variational quantum algorithms. It achieves scaling improvements in ground state success probabilities, increasing by up to two orders of magnitude.
  arXiv  Detail & Related papers  (2024-05-22T18:11:42Z)
• Near-Term Distributed Quantum Computation using Mean-Field Corrections and Auxiliary Qubits [77.04894470683776]
  We propose near-term distributed quantum computing that involve limited information transfer and conservative entanglement production. We build upon these concepts to produce an approximate circuit-cutting technique for the fragmented pre-training of variational quantum algorithms.
  arXiv  Detail & Related papers  (2023-09-11T18:00:00Z)
• Quantum Worst-Case to Average-Case Reductions for All Linear Problems [66.65497337069792]
  We study the problem of designing worst-case to average-case reductions for quantum algorithms. We provide an explicit and efficient transformation of quantum algorithms that are only correct on a small fraction of their inputs into ones that are correct on all inputs.
  arXiv  Detail & Related papers  (2022-12-06T22:01:49Z)
• Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage [43.3054117987806]
  We introduce a scalable procedure for harnessing classical computing resources to provide pre-optimized initializations for quantum circuits. We show this method significantly improves the trainability and performance of PQCs on a variety of problems. By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing.
  arXiv  Detail & Related papers  (2022-08-29T15:24:03Z)
• Squeezing and quantum approximate optimization [0.6562256987706128]
  Variational quantum algorithms offer fascinating prospects for the solution of optimization problems using digital quantum computers. However, the achievable performance in such algorithms and the role of quantum correlations therein remain unclear. We show numerically as well as on an IBM quantum chip how highly squeezed states are generated in a systematic procedure.
  arXiv  Detail & Related papers  (2022-05-20T18:00:06Z)
• Quantum Robustness Verification: A Hybrid Quantum-Classical Neural Network Certification Algorithm [1.439946676159516]
  In this work, we investigate the verification of ReLU networks, which involves solving a robustness many-variable mixed-integer programs (MIPs) To alleviate this issue, we propose to use QC for neural network verification and introduce a hybrid quantum procedure to compute provable certificates. We show that, in a simulated environment, our certificate is sound, and provide bounds on the minimum number of qubits necessary to approximate the problem.
  arXiv  Detail & Related papers  (2022-05-02T13:23:56Z)
• 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)
• Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
  We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions. Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
  arXiv  Detail & Related papers  (2021-06-24T20:16:02Z)

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.