A new Python library called Unitaria simplifies the development of quantum algorithms by bridging the gap between classical linear algebra and quantum computation. Matthias Deiml and colleagues at University of Augsburg present a set of tools that allows researchers to define and manipulate block encodings of matrices and vectors using a familiar, array-like interface. The library eliminates the need for complex, low-level circuit construction, enabling algorithm development, verification and analysis via classical simulation and resource estimation, all without requiring fully error-corrected quantum hardware. Unitaria’s unique matrix-arithmetic evaluation path sharply accelerates progress in quantum linear algebra by offering a scalable and accessible platform for both research and development. Unitaria enables scalable classical simulation of quantum algorithms via block encoding Classical simulation of quantum algorithms has historically been constrained by exponential scaling with system size, limiting the verification of algorithms beyond a few qubits. Traditional state vector simulation, while conceptually straightforward, quickly becomes intractable as the number of qubits increases. Unitaria circumvents this limitation by leveraging block encodings and a novel matrix-arithmetic evaluation path, allowing classical simulation to scale beyond the thresholds previously imposed by state vector methods. Block encoding is a technique where a matrix is embedded as a sub-block within a larger unitary operator, effectively transforming linear algebra operations into quantum operations. This allows algorithms to be expressed in terms of unitary transformations, which can then be simulated classically using efficient matrix operations. The library’s approach bypasses the need for explicit ancillary qubit management or detailed circuit simulation, significantly reducing the computational overhead associated with classical simulation. This advancement allows researchers to move beyond the limitations of small-scale simulations and explore more intricate quantum designs, facilitating the development of algorithms that would otherwise be impossible to test. The project received funding from the Deutsche Forschungsgemeinschaft under number 571768116, supporting the