Quantum AS-DeepOnet: Quantum Attentive Stacked DeepONet for Solving 2D Evolution Equations

2026-02-28 · Hongquan Wang, Hanshu Chen, Ilia Marchevsky, Zhuojia Fu arxiv

DeepONet enables retraining-free inference across varying initial conditions or source terms at the cost of high computational requirements. This paper proposes a hybrid quantum operator network (Quantum AS-DeepOnet) suitable for solving 2D evolution equations. By combining Parameterized Quantum Circuits and cross-subnet attention methods, we can solve 2D evolution equations using only 60% of the trainable parameters while maintaining accuracy and convergence comparable to the classical DeepONet method.

📄 PDF Abstract BibTeX arXiv:2603.02261

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Conformalized Quantum DeepONet Ensembles for Scalable Operator Learning with Distribution-Free Uncertainty

2026-05-01 · Purav Matlia, Christian Moya, Guang Lin arxiv

Operator learning enables fast surrogate modeling of high-dimensional dynamical systems, but existing approaches face two fundamental limitations: quadratic inference complexity and unreliable uncertainty quantification …

Quantum Machine Learning

Multi-Head Residual-Gated DeepONet for Coherent Nonlinear Wave Dynamics

2026-04-13 · Zhiwei Fan, Yiming Pan, Daniel Coca arxiv

Coherent nonlinear wave dynamics are often strongly shaped by a compact set of physically meaningful descriptors of the initial state. Traditional neural operators typically treat the input-output mapping as a largely bl…

Multi-scale DeepOnet (Mscale-DeepOnet) for Mitigating Spectral Bias in Learning High Frequency Operators of Oscillatory Functions

2025-04-15 · Bo wang, Lizuo Liu, Wei Cai

In this paper, a multi-scale DeepOnet (Mscale-DeepOnet) is proposed to reduce the spectral bias of the DeepOnet in learning high-frequency mapping between highly oscillatory functions, with an application to the nonlinea…

DeepONet-Grid-UQ: A Trustworthy Deep Operator Framework for Predicting the Power Grid's Post-Fault Trajectories

2022-02-15 · Christian Moya, Shiqi Zhang, Meng Yue, Guang Lin

This paper proposes a new data-driven method for the reliable prediction of power system post-fault trajectories. The proposed method is based on the fundamentally new concept of Deep Operator Networks (DeepONets). Compa…

Uncertainty Quantification

Ensemble and Mixture-of-Experts DeepONets For Operator Learning

2024-05-20 · Ramansh Sharma, Varun Shankar

We present a novel deep operator network (DeepONet) architecture for operator learning, the ensemble DeepONet, that allows for enriching the trunk network of a single DeepONet with multiple distinct trunk networks. This …

Mixture-of-Experts Operator learning Unity