Quantum Computing 101 with Qiskit

🐍📰 Understand quantum computing basics like qubits, superposition, and entanglement. Then use Python Qiskit to build your first quantum circuit #python

🐍📰 Quantum Computing Basics With Qiskit Learn the basics of quantum computing qubits, superposition, and entanglement—then use Python Qiskit to create your first quantum circuit. #python

My former colleague Hossein Ghorbanfekr and I recently wrote a book on GPU computing in Python. While many Python programmers, data scientists, and researchers rely on GPU acceleration through high-level frameworks like PyTorch, we noticed that few grasp what’s happening under the hood. Historically, low-level GPU programming was the domain of C/C++ developers, leaving Python users dependent on high-level libraries that wrap low-level code written by someone else. These days, tools like the Numba JIT compiler and the Numba-CUDA backend enable Python developers to write high-performance, low-level GPU code without switching languages. Our book, GPU-Accelerated Computing with Python 3 and CUDA, aims to make CUDA accessible to Python programmers who want to dig one level deeper or need more control over their GPU-accelerated code. We start with the fundamentals: writing and executing CUDA kernels, managing streams, profiling performance, and understanding memory hierarchies. Everything is taught through Python, using Numba-CUDA. We then try to connect these concepts to high-level libraries like CuPy and RAPIDS, which integrate seamlessly with the scientific Python ecosystem. We also included JAX as a flexible framework for differentiable programming and machine learning on GPUs and other accelerators. In the last third of the book, everything is combined to address practical applications, including solving the heat equation, detecting objects in images, simulating atomic interactions, and building + training a small transformer-based language model. This project took a lot of evenings, weekends, and holidays over 1.5 years, but we hope we managed to make something that will benefit other researchers, data scientists, and engineers. We’re grateful to Packt for the opportunity to bring this book to life. The e-book is available now on Amazon (https://a.co/d/03VXXelq), and the print version will be out in a few weeks. This is not an April fool's joke. #gpu #hpc #python #CUDA #numba #scientificcomputing #machinelearning #RAPIDS #cupy #JAX

Quantum Computing is on the rise Python stands out as the most popular language in this field. It serves as a wrapper for various libraries such as Qiskit (IBM), Cirq (Google), and Ocean (D-Wave). Additionally, specialized languages like Microsoft Research’s Q# and QASM (Quantum Assembly Language) are designed to create algorithms for quantum computers, managing unique quantum states like superposition and entanglement. Here are some popular Quantum Programming Languages & Frameworks: ▪️ Python (with Libraries): The dominant language due to its ease of use, integrating with major frameworks to build, simulate, and run quantum circuits. ▪️ Qiskit (IBM): An open-source Python SDK for working with quantum computers at the level of circuits, pulses, and algorithms. ▪️ Q# (Microsoft): A domain-specific language designed for quantum developers, integrating both classical and quantum paradigms, and supporting high-level abstractions. ▪️ Cirq (Google): A Python library focused on designing and running circuits specifically for Noisy Intermediate-Scale Quantum (NISQ) processors. ▪️ PyQuil (Rigetti Computing): A Python library used to write quantum programs that run on Rigetti quantum processors or simulators. ▪️ QASM (Open Quantum Assembly Language): A lower-level, intermediate language resembling assembly, used for defining quantum circuits, often generated by higher-level Python code. ▪️ QCL (Quantum Computation Language): A high-level language with syntax similar to C, allowing for operator definition and simulation. #SanFrancisco #WeLoveOurCity #SFTech #Software #TechEcosystem #Circuits #Quantum #Hardware #Software #Algorithms #TechWaves 🌊

The strength of Python lies in its community, and at Quansight, we are excited to contribute to a cross-industry initiative that makes the Python packaging ecosystem more capable and flexible. Our co-CEO Ralf Gommers, recently joined host Michael Kennedy on the Talk Python Training podcast together with Jonathan Dekhtiar from NVIDIA and Charlie Marsh from Astral, to discuss the collaborative work on WheelNext and wheel variants. Working alongside many other companies and open source projects, we are developing a new standard for hardware-aware packaging. This effort ensures that whether you are using a specialized GPU or a modern CPU, the tools you rely on, like NumPy and PyTorch, will automatically perform at their best. Listen to the full conversation here: https://lnkd.in/gnxvNdM6

URGENT HELP NEEDED!!! Lately someone asked me a question and I need your suggestions for my reply. Someone: " Why don’t you just build or find a Python library that can efficiently run, train, and deploy heavy LLMs on CPU instead of GPU? " Me: 🤨????? Open to suggestions before I accidentally reinvent physics.

🚀 What if geometry could unlock more efficient quantum computing? ⚛️ Meet QGeo—a Python package that turns abstract quantum complexity into computable geodesics, helping researchers design and analyze quantum circuits in entirely new ways. 📄 QGeo: A Python Package for Calculating Geodesic Control Functions for Quantum Computing ✍️ Sean T. Crowe et al. 🔗 https://brnw.ch/21x1SOY #QuantumTech #FutureComputing #PythonTools #QuantumPhysics #Algorithms #ScientificComputing

This is incredibly cool: a theoretical physicist proved that you can recreate all math functions like +, -, x, / , trig, log from just one operation. E(a, b) = e^a - ln(b) What's more interesting is that the researcher used OpenAI's Codex to translate his Mathematica code to Rust allowing much faster experimentation. Scientists are usually very bad at high performance computing and often use things like Matlab or Mathematica which is easier to learn than C++ or Python. But now, suddenly all of them get access to these tools and although they still do the research, coding agents help them unlock much faster experimentation.

Trained a neural network in Python (PyTorch) and ran it on an Arduino Uno with just 688 bytes of RAM. The model classifies analog signals (rising / falling / U / inverted-U) in real-time by exporting weights as C arrays and running a simple forward pass on the microcontroller. No TensorFlow, no GPU — just multiply, add, ReLU. 🔗 https://lnkd.in/d2PajsPp #MachineLearning #Arduino #TinyML #EdgeAI #PyTorch #EmbeddedSystems

Physics taught me how the world works. Math taught me how to measure it. Computer Science is teaching me how to change it. 🛠️ I’ve found that the best IT solutions don't come from just knowing a language like Python or C++. They come from First Principles Thinking. When I’m stuck on a complex Physics problem or a Math proof, I’m actually training my brain for debugging code. It’s all about: 1: Breaking down the "big" mess. 2: Identifying the core variables. 3: Building a logical sequence to the answer. IT isn't just about computers; it’s about the logic of problem-solving. That’s the mindset I’m bringing to everything I build this year. #FirstPrinciples #ProblemSolving #STEM #TechMindset #FutureEngineer