Ever since Microsoft killed WordPad in 2024, the much-simpler Notepad app has been receiving several new features--almost as if it's evolving into a better, more modern version of WordPad. Meanwhile, Microsoft is introducing an even simpler text editor called Edit. Some of the recent additions to Notepad include spell check, AI-generated text, and Markdown formatting--and the improvements aren't done yet. The latest news is that Notepad will soon have updated context menus in Windows 11, reports Neowin. In Notepad version 11.2507.26.0, which is currently rolling out to Windows Insiders, the updated context menu now matches the look of Windows 11 24H2's context menus, with quick actions for Copy, Cut, Paste, Select all, and Delete, plus other actions like Write, Rewrite, Summarize, Define with Bing, and more.

We demonstrate that efficient meta-learning can be achieved via end-to-end training of deep neural networks with memory distributed across layers. The persistent state of this memory assumes the entire burden of guiding task adaptation. Moreover, its distributed nature is instrumental in orchestrating adaptation.

"Monkey see monkey do" is an age-old adage, referring to naïve imitation without a

Input: Network parameters θ; Input data x; Label y; Time steps T; Other hyperparameters; Output: Trained network parameters θ . Calculate the output o and the loss L based on o and y . Update θ based on the gradient-based optimizer. We first prove Theorem 1. Then Theorem 2 is similarly proved. We omit repetitive details here.

All prior results suffer from at least one of the two obstacles: the curse of multiple agents and the barrier of long horizon, regardless of the sampling protocol in use. We take a step towards settling this problem, assuming access to a flexible sampling mechanism: the generative model. Focusing on non-stationary finite-horizon Markov games, we develop a fast learning algorithm called Q-FTRL and an adaptive sampling scheme that leverage the optimism principle in online adversarial learning (particularly the Follow-the-Regularized-Leader (FTRL) method).

Scientific progress is tightly coupled to the emergence of new research tools. Today, machine learning (ML)-especially deep learning (DL)-has become a transformative instrument for quantum science and technology. Owing to the intrinsic complexity of quantum systems, DL enables efficient exploration of large parameter spaces, extraction of patterns from experimental data, and data-driven guidance for research directions. These capabilities already support tasks such as refining quantum control protocols and accelerating the discovery of materials with targeted quantum properties, making ML/DL literacy an essential skill for the next generation of quantum scientists. At the same time, DL's power brings risks: models can overfit noisy data, obscure causal structure, and yield results with limited physical interpretability. Recognizing these limitations and deploying mitigation strategies is crucial for scientific rigor. These lecture notes provide a comprehensive, graduate-level introduction to DL for quantum applications, combining conceptual exposition with hands-on examples. Organized as a progressive sequence, they aim to equip readers to decide when and how to apply DL effectively, to understand its practical constraints, and to adapt AI methods responsibly to problems across quantum physics, chemistry, and engineering.