Selected work

Representative Papers

Evaluating QAOA expectation values can be as hard as counting optimal solutions

Aug 11, 2026

This work investigates the computational complexity of evaluating expectation values in the Quantum Approximate Optimization Algorithm (QAOA) for the MaxCut problem. By employing deterministic polynomial-time Turing reductions, Laurent polynomial analysis, and #P-hardness proof techniques, it establishes that for circuit depth \( p \geq 2 \), computing exact or exponentially precise expectation values—and their gradients and Hessians—is #P-hard, even when restricted to a single two-body correlation term or a constrained parameter set. This result demonstrates that the complexity transition from \( p = 1 \) to \( p \geq 2 \) in QAOA transcends mere NP-hardness, ascending to the #P level associated with counting optimal solutions. Moreover, the reduction simultaneously recovers both the maximum cut value and the number of optimal solutions, thereby revealing a fundamental computational barrier inherent to this task.

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Sequential Reservoir Computing for Efficient High-Dimensional Spatiotemporal Forecasting

Jan 01, 2026arXiv.org

This work addresses the limitations of traditional RNNs/LSTMs and conventional reservoir computing in high-dimensional spatiotemporal system prediction, where the former suffer from gradient-related training difficulties and memory bottlenecks, and the latter exhibit poor scalability with increasing input dimensionality. The authors propose a serialized reservoir computing architecture that decomposes a large reservoir into multiple interconnected smaller sub-reservoirs for the first time. By constructing a cascaded network with fixed random recurrent layers and a convex optimization-based readout mechanism, the approach eliminates backpropagation and substantially reduces computational and memory costs. While maintaining model simplicity, the method significantly enhances scalability and long-term dependency modeling for high-dimensional dynamical systems. Experiments on Lorenz63, 2D vorticity, and shallow water equations demonstrate 15–25% longer prediction horizons, 20–30% lower SSIM and RMSE errors, and training costs reduced by three orders of magnitude compared to RNNs/LSTMs.

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GAIA: A Foundation Model for Operational Atmospheric Dynamics

May 15, 2025arXiv.org

This study addresses the challenge of learning semantically rich, atmosphere-dynamics-focused representations from geostationary satellite imagery—decoupled from diurnal texture variations—to enhance downstream atmospheric modeling. We propose the first geospatial foundation model architecture that synergistically integrates masked autoencoding (MAE) with label-free self-distillation (DINO), jointly capturing local spatiotemporal details and global dynamical dependencies. The model demonstrates robust reconstruction capability under high masking ratios and achieves high-accuracy precipitation estimation with minimal labeled data: a false alarm rate of 0.088 and structural similarity index of 0.881. Our core contribution lies in deeply adapting self-supervised learning paradigms to physical atmospheric process modeling, establishing a scalable, low-label-dependency framework for global meteorological representation learning.

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Recent publications

Latest Papers

Evaluating QAOA expectation values can be as hard as counting optimal solutions

Aug 11, 2026

This work investigates the computational complexity of evaluating expectation values in the Quantum Approximate Optimization Algorithm (QAOA) for the MaxCut problem. By employing deterministic polynomial-time Turing reductions, Laurent polynomial analysis, and #P-hardness proof techniques, it establishes that for circuit depth \( p \geq 2 \), computing exact or exponentially precise expectation values—and their gradients and Hessians—is #P-hard, even when restricted to a single two-body correlation term or a constrained parameter set. This result demonstrates that the complexity transition from \( p = 1 \) to \( p \geq 2 \) in QAOA transcends mere NP-hardness, ascending to the #P level associated with counting optimal solutions. Moreover, the reduction simultaneously recovers both the maximum cut value and the number of optimal solutions, thereby revealing a fundamental computational barrier inherent to this task.

0 citationsRead paper

Sequential Reservoir Computing for Efficient High-Dimensional Spatiotemporal Forecasting

Jan 01, 2026arXiv.org

This work addresses the limitations of traditional RNNs/LSTMs and conventional reservoir computing in high-dimensional spatiotemporal system prediction, where the former suffer from gradient-related training difficulties and memory bottlenecks, and the latter exhibit poor scalability with increasing input dimensionality. The authors propose a serialized reservoir computing architecture that decomposes a large reservoir into multiple interconnected smaller sub-reservoirs for the first time. By constructing a cascaded network with fixed random recurrent layers and a convex optimization-based readout mechanism, the approach eliminates backpropagation and substantially reduces computational and memory costs. While maintaining model simplicity, the method significantly enhances scalability and long-term dependency modeling for high-dimensional dynamical systems. Experiments on Lorenz63, 2D vorticity, and shallow water equations demonstrate 15–25% longer prediction horizons, 20–30% lower SSIM and RMSE errors, and training costs reduced by three orders of magnitude compared to RNNs/LSTMs.

0 citationsRead paper

GAIA: A Foundation Model for Operational Atmospheric Dynamics

May 15, 2025arXiv.org

This study addresses the challenge of learning semantically rich, atmosphere-dynamics-focused representations from geostationary satellite imagery—decoupled from diurnal texture variations—to enhance downstream atmospheric modeling. We propose the first geospatial foundation model architecture that synergistically integrates masked autoencoding (MAE) with label-free self-distillation (DINO), jointly capturing local spatiotemporal details and global dynamical dependencies. The model demonstrates robust reconstruction capability under high masking ratios and achieves high-accuracy precipitation estimation with minimal labeled data: a false alarm rate of 0.088 and structural similarity index of 0.881. Our core contribution lies in deeply adapting self-supervised learning paradigms to physical atmospheric process modeling, establishing a scalable, low-label-dependency framework for global meteorological representation learning.

0 citationsRead paper