Simple, Clean, Intuitive, and Qrisp: Quantum Linear Algebra for Developers

Quantum computers are slowly getting there. But building algorithms gate-by-gate is the “Assembly language” of quantum that simply doesn’t scale for industrial applications. To build real-world software, we need high-level abstractions!

In this session, we explore how Eclipse Qrisp has evolved into a high-performance framework that lets developers move beyond circuit-level noise and focus on algorithmic logic using constructs like QuantumVariables and QuantumFloats.

The star of the show is the new BlockEncoding class in the 0.8 update. Think of it as the “quantum Numpy”, or at least Numpy’s cousin. We will demonstrate live on stage how to perform complex quantum linear algebra with a clean, pythonic interface:

• From Arrays to block encodings: Construct block encodings directly from standard arrays with .from_array(A), form operators with .from_operator(O), or construct your custom block encoding with from_lcu(unitaries, coeffs). Combine them effortlessly using standard matrix arithmetic (+, -, *, .kron(), …).

• Solving linear systems: We will tackle matrix inversion (solving $Ax = b$) using .inv(epsilon, kappa). This is a gateway to speedups in machine learning, fluid dynamics, and optimization.

• Hamiltonian simulation & Polynomials: Perform Hamiltonian simulation with .sim() and arbitrary polynomial transformation/filtering with .poly().

• Hardware-Ready Workflow: Move from theory to execution. We’ll show how to compile for simulators or hardware using .apply() or deterministic “Repeat-Until-Success” protocols with .apply_rus.

• Quantum Resource Estimation: Track gate counts, circuit depth, and qubit usage on the fly simply by calling .resources.

Whether you’re a software engineer entering the quantum domain or a researcher streamlining your workflow, this talk provides a practical roadmap for building scalable quantum applications today.

The future of Quantum Linear Algebra is here, and it’s Qrisp.