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Found 4 papers, 1 papers with code
G-ACIL: Analytic Learning for Exemplar-Free Generalized Class Incremental Learning
The generalized CIL (GCIL) aims to address the CIL problem in a more real-world scenario, where incoming data have mixed data categories and unknown sample size distribution, leading to intensified forgetting.
REAL: Representation Enhanced Analytic Learning for Exemplar-free Class-incremental Learning
The DS-BPT pretrains model in streams of both supervised learning and self-supervised contrastive learning (SSCL) for base knowledge extraction.
Learning many-body Hamiltonians with Heisenberg-limited scaling
In contrast, the best previous algorithms, such as recent works using gradient-based optimization or polynomial interpolation, require a total evolution time of $\mathcal{O}(\epsilon^{-2})$ and $\mathcal{O}(\epsilon^{-2})$ experiments.
Time-dependent unbounded Hamiltonian simulation with vector norm scaling
We demonstrate that under suitable assumptions of the Hamiltonian and the initial vector, if the error is measured in terms of the vector norm, the computational cost may not increase at all as the norm of the Hamiltonian increases using Trotter type methods.
Quantum Physics Numerical Analysis Numerical Analysis
