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English(EN) Hamilton-Zero: A Neural Tensor-Network Foundation Model for Ground States of Arbitrary Quadratic Qubit Hamiltonians

量子计算进展：基础模型和高效神经网络解决复杂问题

作者 PulseAugur 编辑部 · [3 个来源] ·

研究人员开发了两种不同的量子计算方法来解决复杂问题。一种名为“Hamilton-Zero”的神经张量网络基础模型，旨在计算任意二次量子比特哈密顿量的基态，并采用来自大型语言模型（LLM）和深度强化学习的技术进行训练。另一种是单量子比特量子神经网络（SQQNN），它利用参数化酉算符和一种新颖的高效训练方法，在MNIST和Wisconsin Breast Cancer等数据集上实现了有效的回归和分类。 AI

影响 量子神经网络和基础模型的进步可能会加速复杂的科学模拟和数据分析任务。

排序理由 arXiv上发表了两篇不同的研究论文，详细介绍了量子计算和机器学习的新方法。

在 arXiv cs.AI 阅读 →

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量子计算进展：基础模型和高效神经网络解决复杂问题

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arXiv上发表了两篇不同的研究论文，详细介绍了量子计算和机器学习的新方法。
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报道来源 [3]

  1. arXiv cs.LG TIER_1 English(EN) · Bodo Rosenhahn, Tobias J. Osborne, Christoph Hirche ·

    Stochastic Neural Networks for Quantum Devices

    arXiv:2602.22241v2 Announce Type: replace-cross Abstract: This work presents a formulation to express and optimize stochastic neural networks as quantum circuits in gate-based quantum computing. Motivated by a classical perceptron, stochastic artificial neurons are introduced and…

  2. arXiv cs.AI TIER_1 English(EN) · Timothy Heightman, Elena Orlova, Philip Mantrov, Aleksei Ustimenko ·

    Hamilton-Zero：任意二次量子比特哈密顿量基态的神经张量网络基础模型

    arXiv:2608.11911v1 Announce Type: cross Abstract: A central promise of useful quantum advantage is the ability to compute ground states of Hamiltonian systems beyond the reach of classical simulation methods. Here we demonstrate that this problem can be effectively amortized acro…

  3. arXiv cs.AI TIER_1 English(EN) · Leandro C. Souza, Bruno C. Guingo, Gilson Giraldi, Renato Portugal ·

    使用单量子比特量子神经网络进行回归和分类

    arXiv:2412.09486v2 Announce Type: replace-cross Abstract: The literature reflects a mutually beneficial relationship between machine learning and quantum computing, where progress in one field frequently drives improvements in the other. Motivated by the rich connection between t…

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