As quantum computing transitions from experimental toys to complex industrial applications, the tools we use must evolve. Building algorithms gate-by-gate no longer scales. In this session, we explore how Eclipse Qrisp has grown from a high-level abstraction framework into a high-performance framework for developing scalable quantum software.

By introducing high-level abstractions such as QuantumVariables or QuantumFloats, developers can move beyond circuit/level programming and focus on the logic of the algorithm instead.

As an example, we will tackle matrix inversion: solving $Ax = b$ on a quantum computer is a gateway to speedups in machine learning, fluid dynamics, and optimization. By comparing key algorithms such as HHL, CKS, and QSVT, we’ll reveal their shared mathematical structure and show how they can all be expressed through a unified, qrispy implementation.

Whether you’re a software engineer seeking to enter the quantum domain or a researcher streamlining your workflow, this talk will demonstrate how Qrisp enables you to build real-world quantum applications without getting lost in the low-level noise.

pip install qrisp