Granular-Ball Quantum Clustering for Resource-Efficient and Robust Learning

• URL: http://arxiv.org/abs/2609.06016v1
• Date: Sat, 05 Sep 2026 10:42:04 GMT
• Title: Granular-Ball Quantum Clustering for Resource-Efficient and Robust Learning
• Abstract summary: Quantum clustering aims to exploit quantum representations to uncover complex data structures.<n>Sample-level kernel construction requires O(n2) quantum circuit structures for n data executions.<n>We propose Granular-Ball Quantum Clustering (GBQC) that couples granular-ball compression with quantum feature learning.
• Score: 13.354557013805527
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum clustering aims to exploit quantum feature representations to uncover complex data structures beyond conventional Euclidean geometry. Yet this sample-level kernel construction requires O(n^2) quantum circuit executions for n data points, creating a major bottleneck under near-term quantum resource constraints. Prior solutions fail to resolve this efficiency-accuracy dilemma: classical granular-ball clustering reduces sample complexity but relies on Euclidean metrics that cannot capture quantum correlations, while existing quantum compression schemes prioritize efficiency over structural preservation, degrading performance on non-convex or noisy data. Here we propose Granular-Ball Quantum Clustering (GBQC), a framework that tightly couples granular-ball structural abstraction with quantum feature learning. GBQC first compresses raw data into compact, representative granular balls via a PCA-guided splitting strategy, reducing kernel evaluations by 80% compared to full-sample methods. A quantum cohesion mechanism then filters noisy granules in Hilbert space to improve clustering robustness. Extensive experiments on synthetic, noisy, overlapping, and real-world datasets demonstrate that GBQC consistently achieves superior clustering accuracy and robustness compared with representative classical and quantum clustering methods. Meanwhile, the proposed granular-ball compression significantly reduces quantum kernel evaluations and computational overhead, enabling quantum clustering experiments on larger datasets within parameterized quantum learning frameworks. These results suggest that granular-ball representations serve not only as a compression mechanism to reduce quantum computational costs but also as an effective structural abstraction mechanism that improves clustering quality by eliminating redundant and structurally ambiguous learning units.

Related papers

• Balancing Expressivity and Learnability in Quantum Kernel Bandit Optimization [50.880384999888044]
  We develop misspecified GP bandit algorithms and derive regret bounds that characterize the trade-off between approximation error and information gain.<n>Our methods outperform full quantum kernels in sample efficiency, while substantially reducing computational overhead.
  arXiv  Detail & Related papers  (2026-07-01T15:38:00Z)
• Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing [68.35481158940401]
  CL-QAS is a continual quantum architecture search framework.<n>It mitigates challenges of costly encoding amplitude and forgetting in variational quantum circuits.<n>It achieves controllable robustness expressivity, sample-efficient generalization, and smooth convergence without barren plateaus.
  arXiv  Detail & Related papers  (2026-01-10T02:36:03Z)
• Structure-Fair Quantum Circuit Complexity: An Auditable Information-Theoretic Lower Bound [0.2606834301724095]
  This paper introduces the Reference-Contingent Complexity (RCC), an information-theoretic measure calibrated by the available quantum operations.<n>Our central result is a key theorem that rigorously proves the RCC serves as a lower bound for the complexity of any universal quantum circuit.<n>This work provides a "ruler" for quantum technology that is structure-fair and enables cross-platform comparison.
  arXiv  Detail & Related papers  (2025-09-20T14:58:34Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• An Efficient Quantum Classifier Based on Hamiltonian Representations [50.467930253994155]
  Quantum machine learning (QML) is a discipline that seeks to transfer the advantages of quantum computing to data-driven tasks.<n>We propose an efficient approach that circumvents the costs associated with data encoding by mapping inputs to a finite set of Pauli strings.<n>We evaluate our approach on text and image classification tasks, against well-established classical and quantum models.
  arXiv  Detail & Related papers  (2025-04-13T11:49:53Z)
• Quantum autoencoders for image classification [0.0]
  Quantum autoencoders (QAEs) leverage classical optimization solely for parameter tuning.<n>This study introduces a novel image-classification approach using QAEs, achieving classification without requiring additional qubits.
  arXiv  Detail & Related papers  (2025-02-21T07:13:38Z)
• A clustering aggregation algorithm on neutral-atoms and annealing quantum processors [0.44531072184246007]
  This work presents a hybrid quantum-classical algorithm to perform clustering aggregation.<n>It is designed for neutral-atoms quantum computers and quantum annealers.<n>Findings suggest promising potential for future advancements in hybrid quantum-classical pipelines.
  arXiv  Detail & Related papers  (2024-12-10T14:48:44Z)
• Projective Quantum Eigensolver with Generalized Operators [0.0]
  We develop a methodology for determining the generalized operators in terms of a closed form residual equations in the PQE framework. With the application on several molecular systems, we have demonstrated our ansatz achieves similar accuracy to the (disentangled) UCC with singles, doubles and triples.
  arXiv  Detail & Related papers  (2024-10-21T15:40:22Z)
• QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks [124.7972093110732]
  We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations. To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections. Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
  arXiv  Detail & Related papers  (2022-11-09T21:43:16Z)
• Quantum spectral clustering algorithm for unsupervised learning [0.8399688944263843]
  We propose a circuit design to implement spectral clustering on a quantum processor with a substantial speedup. Compared with the established quantum $k$-means algorithms, our method does not require a quantum random access memory or a quantum adiabatic process.
  arXiv  Detail & Related papers  (2022-03-07T05:06:47Z)
• Quantum Federated Learning with Quantum Data [87.49715898878858]
  Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems. This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner.
  arXiv  Detail & Related papers  (2021-05-30T12:19:27Z)

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.