Addressing the Current Challenges of Quantum Machine Learning through Multi-Chip Ensembles
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Abstract (arXiv)
Practical Quantum Machine Learning (QML) is challenged by noise, limited scalability, and poor trainability in Variational Quantum Circuits (VQCs) on current hardware. We propose a multi-chip ensemble VQC framework that systematically overcomes these hurdles. By partitioning high-dimensional computations across ensembles of smaller, independently operating quantum chips and leveraging controlled inter-chip entanglement boundaries, our approach demonstrably mitigates barren plateaus, enhances generalization, and uniquely reduces both quantum error bias and variance simultaneously without additional mitigation overhead. This allows for robust processing of large-scale data, as validated on standard benchmarks (MNIST, FashionMNIST, CIFAR-10) and a real-world PhysioNet EEG dataset, aligning with emerging modular quantum hardware and paving the way for more scalable QML.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 30 of its 99 references that are in the map. First posted on arXiv 13 May 2025.
Builds on
30 of 99 references.
References · 30
- 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,349
- 2020Variational Quantum Algorithmscited by 692
- 2018Barren plateaus in quantum neural network training landscapescited by 630
- 2020The power of quantum neural networkscited by 379
- 2021Generalization in quantum machine learning from few training datacited by 216
- 2023Challenges and Opportunities in Quantum Machine Learningcited by 203
- 2020Noise-Induced Barren Plateaus in Variational Quantum Algorithmscited by 193
- 2021Connecting ansatz expressibility to gradient magnitudes and barren plateauscited by 180
- 2019Efficient Learning for Deep Quantum Neural Networkscited by 161
- 2024Barren Plateaus in Variational Quantum Computingcited by 133
- 2020Absence of Barren Plateaus in Quantum Convolutional Neural Networkscited by 125
- 2020Entanglement Induced Barren Plateauscited by 101
18 more
- 2021Federated Quantum Machine Learningcited by 82
- 2021Encoding-dependent generalization bounds for parametrized quantum circuitscited by 82
- 2023Does provable absence of barren plateaus imply classical simulability?cited by 80
- 2022Exploiting symmetry in variational quantum machine learningcited by 69
- 2020Quantum Convolutional Neural Networks for High Energy Physics Data Analysiscited by 64
- 2022Theory for Equivariant Quantum Neural Networkscited by 62
- 2021Towards understanding the power of quantum kernels in the NISQ eracited by 43
- 2022Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuitscited by 43
- 2022Equivariant quantum circuits for learning on weighted graphscited by 41
- 2022High Dimensional Quantum Machine Learning With Small Quantum Computerscited by 18
- 2022Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantagecited by 18
- 2023Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applicationscited by 14
- 2022The Complexity of NISQcited by 13
- 2022An Invitation to Distributed Quantum Neural Networkscited by 13
- 2022Overfitting in quantum machine learning and entangling dropoutcited by 9
- 2024Noise-Aware Distributed Quantum Approximate Optimization Algorithm on Near-term Quantum Hardwarecited by 8
- 2023Distributed Quantum Neural Networks via Partitioned Features Encodingcited by 5
- 2024Entanglement-induced provable and robust quantum learning advantagescited by 4
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.