Quantum Computing with Qiskit Basics

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

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

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 🌊

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

Day 20, 21 and 22 of Building Quantum Projects! Happy World Quantum Day my friends! I am celebrating this day with a very interesting project you all can learn from. To be honest this project stretched me! It took me 3 days to perfect it. In this project I had to find a way to simulate quantum concepts like superposition, entanglement and measurement in python, the CLI worked perfectly, so I thought to make it accessible on the web so that people can learn how it works and how to build theirs. Because it is world quantum day, I added a simulation to teach how to play the game. The game allows one to play with human and AI. I have also open sourced the codebase with detailed explanation of how I modelled the quantum concepts in python. Please star the repo and contribute, let's improve this and make it an impactful project for the quantum community. This all started from the tasks we were given in the GDG OAU QUANTUM COMPUTING COMMUNITY. The most supportive quantum community I have belonged to. GitHub Repo: https://lnkd.in/e-_KTrJ4 Live Link: https://lnkd.in/euK_HEbp #quantum #computing #programming #world #quantum #day

🚀 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

Happy World Quantum Day! Today we're releasing quantum computing for portfolio optimization on CPZAI ; the first systematic trading SDK with integrated QPU access. What's live: - Quantum HRP (1QBit algorithm): optimized asset permutation via QUBO - QUBO portfolio selection: cardinality-constrained asset picking - Real hardware: IonQ Forte-1, Rigetti Ankaa-3, IQM Garnet via Amazon Braket - Classical backends included: develop free, deploy on QPUs when ready Write strategies in Python. Run them classically or on quantum hardware with one parameter change. Check it out: https://ai.cpz-lab.com & https://lnkd.in/e-6YinUd #WorldQuantumDay #QuantumComputing #PortfolioOptimization #Fintech #QuantitativeFinance #Systematic #Trading #CPZAI #Python

Happy World Quantum Day Proud to share what we’ve been building at CPZ®. We’re introducing quantum computing for portfolio optimization directly into CPZAI. The goal is simple: make advanced optimization methods actually usable in a systematic workflow, without adding friction. A few things that stand out to me: • Quantum HRP using QUBO formulations • Cardinality-constrained portfolio selection • Direct access to real QPUs via Amazon Braket • Seamless switch between classical and quantum execution You can write strategies in Python, test everything locally, and move to quantum hardware when it makes sense. No redesign needed. Quantum computing has come a long way and still has a long way to go. But even today, we enjoy using it for very selective optimization problems where the formulation fits naturally. This is still early, but the direction is clear. Optimization problems in finance map well to quantum approaches, and we want to make that accessible in a practical way. Curious to hear how others are thinking about quantum in portfolio construction. #WorldQuantumDay #QuantumComputing #PortfolioOptimization CPZ® #QuantitativeFinance #SystematicTrading #CPZAI

Can #QuantumComputing help econometricians do their work better? Maybe someday. Carlos Madeira, Yash Rastogi, Harald Uhlig and I explore in a new Bank for International Settlements – BIS working paper how quantum computing could support #BayesianInference. We present a proof-of-concept #Quantum algorithm that performs posterior sampling, ie a step-by-step process for estimating possible outcomes based on new data and prior knowledge. We do so by showing code listings using the #Qiskit package in the #Python programming language. Importantly, our method does not yet offer faster computation than classical techniques such as Markov Chain Monte Carlo, importance sampling or particle filtering. But the approach demonstrates the feasibility of doing #Bayesian inference with quantum computation simulation. https://lnkd.in/e8ppawuG

Quantum-Supported Bayesian Inference Source: Bank for International Settlements – BIS Working Paper (Jon Frost, Carlos Madeira, Yash Rastogi, Harald Uhlig, et al.) Overview This proof-of-concept (#PoC) explores the #integration of #QuantumComputing into #econometrics. Specifically, it investigates how #quantumalgorithms can be applied to #BayesianInference, a statistical method essential for #economic #forecasting and #riskassessment. The study serves as a foundational #bridge between #theoretical #quantummechanics and #practical #macroeconomic #modeling. Core Objectives . Feasibility Testing: To determine if #quantum #architectures can successfully #execute the #mathematical rigors of #posteriorsampling. . Algorithmic Development: The creation of a novel #quantumalgorithm designed to #estimate #outcomes by #combining #prior #knowledge with #new #data. . Open-Source Implementation: Providing code listings via the Qiskit (Python) framework to allow the broader research community to simulate and verify the findings. Key Findings . Functional Viability: The researchers successfully demonstrated that Bayesian inference can be performed within a quantum computation simulation environment. . Performance Comparison: Currently, the quantum method does not outperform classical statistical techniques. Established methods such as Markov Chain Monte Carlo (MCMC), importance sampling, and particle filtering remain more efficient on traditional hardware. . Technical Framework: The study utilizes "posterior sampling," a step-by-step process that allows econometricians to update probability distributions as new economic data becomes available. Strategic Implications While the technology is not yet at the "quantum advantage" stage (where it beats classical computers), this paper establishes the theoretical groundwork for future applications. As #quantum #hardware matures and error rates decrease, the algorithms developed in this PoC could eventually allow for: . Increased Model #Complexity: Processing more variables than currently possible. . Enhanced #Precision: Better capturing "tail risks" or extreme economic events. . Real-time #PolicyAnalysis: Faster processing of global data for #centralbank #decisionmaking. Conclusion The paper concludes that while #quantumcomputing is not a current #replacement for classical #econometrictools, it is a #viable future-state #technology. This #PoC marks a significant #shift from "if" #quantumcomputing can be used in #economics to "how" it will be implemented as #hardware #capabilities catch up to the theory. Thanks for sharing Jon Frost