Tutorials#
Welcome to the Qrisp tutorials! This gallery is designed to take you from a curious beginner to a proficient developer of utility-scale quantum algorithms. By shifting the focus from low-level gate gymnastics to high-level programming with QuantumVariables, Qrisp allows you to express complex logic with ease.
The tutorials are organized into four topics, each containing multiple hands-on examples that build on each other:
Foundations and first Steps: Start here to master the core syntax of Qrisp, where you will learn to replace manual circuit building with high-level variables and implement textbook algorithms like Shor’s and Grover’s.
Optimization and Numerics: Explore how to solve combinatorial optimization problems in logistics and scheduling using hybrid approaches like QAOA, QIRO, and COLD, as well as learning about our qrispy Quantum Backtracking implementation.
High-Performance Compilation and Execution with Jasp: Leverage the JAX-based Jasp pipeline to enable hybrid real-time control flow, while scaling your code and estimating quantum resources for real-world-sized applications.
Scientific Computing and Quantum Numerical Linear Algebra: Dive into high-level abstractions for quantum chemistry, physics and linear systems using our new BlockEncoding class.
If you’re the type who learns best by breaking things (and then fixing them), you can download any of these tutorials as a Jupyter notebook. Just look for the download box on the right side of the page within each specific tutorial to grab the code and run it in your own local environment.
By the end of these tutorials, you’ll have a solid foundation of our high-level framework and be ready to tackle more complex projects. So let’s get started!
Foundations and First Steps#
Kickstart your quantum programming journey here! This section is designed to familiarize you with Qrisp’s core philosophy: shifting away from low-level circuit manipulation and toward intuitive, high-level programming using QuantumVariables.
Optimization and Numerics#
Discover how quantum algorithms can tackle complex search and optimization problems. In this section, you will dive into famous computational challenges using a diverse range of quantum algorithms. Learn how to optimize solutions across a variety of real-world domains using hybrid variational models, backtracking, and counterdiabatic driving.
High-Performance Compilation and Execution with Jasp#
Scale your quantum algorithms to practically relevant problem sizes by bypassing Python’s performance bottlenecks. By targeting the MLIR toolchain and QIR specification, Jasp enables high-speed compilation and seamless real-time control, allowing classical logic to execute within the quantum coherence window. Explore the Jasp pipeline to build highly performant algorithms, and ensure your code is ready for next-generation, fault-tolerant hardware.
Scientific Computing and Quantum Numerical Linear Algebra#
Unlock the potential of quantum computers for scientific discovery and advanced mathematics. Here, you will use Qrisp’s high-level abstractions to tackle complex problems in quantum chemistry and physics. This track demonstrates how to bridge the gap between complex scientific theory and executable quantum code using features like block encodings as programming abstractions and quantum signal processing.
You’ve now explored the breadth of what’s possible when you trade gate-level surgery for high-level logic. From your first QuantumVariable to fault-tolerant resource estimation, you have the roadmap to develop utility-scale applications that once seemed out of reach.
The quantum landscape is evolving rapidly. By mastering these tutorials, you’re no longer just a spectator, you’re an architect of the next generation of algorithms. So, take these concepts, experiment, and start building the future of quantum computing with intuitive, clean, and qrispy code.