Results for “torch-quantile”

Simulações e análise de mecânica quântica usando QuTiP (Quantum Toolbox in Python). Use quando trabalhar com sistemas quânticos incluindo: (1) estados quânticos (kets, bras, matrizes densidade), (2) operadores e gates quânticos, (3) evolução temporal e dinâmica (Schrödinger, equações mestras, Monte Carlo), (4) sistemas quânticos abertos com dissipação, (5) medições quânticas e emaranhamento, (6) visualização (esfera de Bloch, funções de Wigner), (7) estados estacionários e funções de correlação, ou (8) métodos avançados (teoria de Floquet, HEOM, resolutores estocásticos). Manipula sistemas quânticos fechados e abertos em vários domínios incluindo óptica quântica, computação quântica e física da matéria condensada.

Quantum mechanics simulations and analysis using QuTiP (Quantum Toolbox in Python). Use when working with quantum systems including: (1) quantum states (kets, bras, density matrices), (2) quantum operators and gates, (3) time evolution and dynamics (Schrödinger, master equations, Monte Carlo), (4) open quantum systems with dissipation, (5) quantum measurements and entanglement, (6) visualization (Bloch sphere, Wigner functions), (7) steady states and correlation functions, or (8) advanced methods (Floquet theory, HEOM, stochastic solvers). Handles both closed and open quantum systems across various domains including quantum optics, quantum computing, and condensed matter physics.

Quantum mechanics simulations and analysis using QuTiP (Quantum Toolbox in Python). Use when working with quantum systems including: (1) quantum states (kets, bras, density matrices), (2) quantum operators and gates, (3) time evolution and dynamics (Schrödinger, master equations, Monte Carlo), (4) open quantum systems with dissipation, (5) quantum measurements and entanglement, (6) visualization (Bloch sphere, Wigner functions), (7) steady states and correlation functions, or (8) advanced methods (Floquet theory, HEOM, stochastic solvers). Handles both closed and open quantum systems across various domains including quantum optics, quantum computing, and condensed matter physics.

Framework de computação quântica para construir, simular, otimizar e executar circuitos quânticos. Use esta skill ao trabalhar com algoritmos quânticos, design de circuitos quânticos, simulação quântica (com ou sem ruído), execução em hardware quântico (Google, IonQ, AQT, Pasqal), otimização e compilação de circuitos, modelagem e caracterização de ruído, ou experimentos e benchmarking quântico (VQE, QAOA, QPE, randomized benchmarking).

Quantum mechanics simulations and analysis using QuTiP (Quantum Toolbox in Python). Use when working with quantum systems including: (1) quantum states (kets, bras, density matrices), (2) quantum operators and gates, (3) time evolution and dynamics (Schrödinger, master equations, Monte Carlo), (4) open quantum systems with dissipation, (5) quantum measurements and entanglement, (6) visualization (Bloch sphere, Wigner functions), (7) steady states and correlation functions, or (8) advanced methods (Floquet theory, HEOM, stochastic solvers). Handles both closed and open quantum systems across various domains including quantum optics, quantum computing, and condensed matter physics.

High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.

Quantum computing framework for building, simulating, optimizing, and executing quantum circuits. Use this skill when working with quantum algorithms, quantum circuit design, quantum simulation (noiseless or noisy), running on quantum hardware (Google, IonQ, AQT, Pasqal), circuit optimization and compilation, noise modeling and characterization, or quantum experiments and benchmarking (VQE, QAOA, QPE, randomized benchmarking).

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Simulates open quantum systems with QuTiP, the Quantum Toolbox in Python, solving Lindblad master equations (mesolve), Monte Carlo trajectories (mcsolve), and unitary dynamics (sesolve). Use when studying master-equation or Lindblad dynamics, decoherence, dissipation, quantum optics, cavity QED, or open-system time evolution. NOT for circuit-based quantum computing or hardware execution — for IBM Quantum circuits prefer alterlab-qiskit, for Google Quantum AI or NISQ circuits prefer alterlab-cirq, and for gradient-trained quantum ML prefer alterlab-pennylane. Part of the AlterLab Academic Skills suite.

Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.

Computes the Recall metric using torchmetrics, including configuration for binary, multiclass, and multilabel tasks.

Executes Atomic Red Team tests mapped to MITRE ATT&CK techniques, performs coverage gap analysis, and runs detection validation loops to measure blue team visibility.

Provides guidance on using the QMT quantitative trading terminal, including strategy development, backtesting, and live trading for Chinese securities markets.

Quantum computing framework for building, simulating, optimizing, and executing quantum circuits. Use this skill when working with quantum algorithms, quantum circuit design, quantum simulation (noiseless or noisy), running on quantum hardware (Google, IonQ, AQT, Pasqal), circuit optimization and compilation, noise modeling and characterization, or quantum experiments and benchmarking (VQE, QAOA, QPE, randomized benchmarking).

Organizes PyTorch code with a Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks, and minimal boilerplate. Scales from laptop to supercomputer with the same code.

Quantum computing framework for building, simulating, optimizing, and executing quantum circuits. Use this skill when working with quantum algorithms, quantum circuit design, quantum simulation (noiseless or noisy), running on quantum hardware (Google, IonQ, AQT, Pasqal), circuit optimization and compilation, noise modeling and characterization, or quantum experiments and benchmarking (VQE, QAOA, QPE, randomized benchmarking).

Kit de ferramentas abrangente de computação quântica para construir, otimizar e executar circuitos quânticos. Use quando trabalhar com algoritmos quânticos, simulações ou hardware quântico, incluindo (1) Construção de circuitos quânticos com portas e medições, (2) Execução de algoritmos quânticos (VQE, QAOA, Grover), (3) Transpilar/otimizar circuitos para hardware, (4) Executar em IBM Quantum ou outros provedores, (5) Química quântica e ciência dos materiais, (6) Aprendizado de máquina quântico, (7) Visualizar circuitos e resultados, ou (8) Qualquer tarefa de desenvolvimento de computação quântica.

PyTorch, TensorFlow, neural networks, CNNs, transformers, and deep learning for production

High-level PyTorch framework with Trainer class, automatic distributed training (DDP/FSDP/DeepSpeed), callbacks system, and minimal boilerplate. Scales from laptop to supercomputer with same code. Use when you want clean training loops with built-in best practices.

量化交易核心skill套件 — 因子挖掘、Alpha衰减监控、多市场套利、订单路由、压力测试、归因分析、仓位管理、样本外验证、A股T0、加密资金费率套利、Polymarket机器人。对标国际顶级量化基金（Renaissance/Two Sigma/Citadel/DE Shaw）的工程与策略水平。

灶门炭治郎（少年漫）认知与表达框架（压缩蒸馏）：温柔斩杀、嗅觉战、家族债 触发：鬼灭之刃 等。虚构；非暴力细节

Builds and trains deep learning models with PyTorch, including tensors, autograd, and neural network modules.