🤖 AI Summary
Although block encoding theoretically underpins numerous advanced quantum algorithms—such as Quantum Signal Processing (QSP) and Quantum Singular Value Transformation (QSVT)—its intricate implementation has hindered practical adoption. This work introduces, for the first time, a generalized programming interface that abstracts block encoding and integrates it into the Eclipse Qrisp framework. The interface encapsulates key techniques including qubitization, the Childs–Kothari–Somma construction, and arithmetic composition, enabling high-level expression and automated resource estimation for algorithms like matrix inversion, polynomial filtering, and Hamiltonian simulation. By providing clear mechanisms for constructing and composing block encodings, this interface substantially lowers the barrier to using state-of-the-art quantum algorithms, enhances developer productivity, and improves accessibility, as demonstrated through illustrative code examples.
📝 Abstract
Block-encoding is a foundational technique in modern quantum algorithms, enabling the implementation of non-unitary operations by embedding them into larger unitary matrices. While theoretically powerful and essential for advanced protocols like Quantum Singular Value Transformation (QSVT) and Quantum Signal Processing (QSP), the generation of compilable implementations of block-encodings poses a formidable challenge. This work presents the BlockEncoding interface within the Eclipse Qrisp framework, establishing block-encodings as a high-level programming abstraction accessible to a broad scientific audience. Serving as both a technical framework introduction and a hands-on tutorial, this paper explicitly details key underlying concepts abstracted away by the interface, such as block-encoding construction and qubitization, and their practical integration into methods like the Childs-Kothari-Somma (CKS) algorithm. We outline the interface's software architecture, encompassing constructors, core utilities, arithmetic composition, and algorithmic applications such as matrix inversion, polynomial filtering, and Hamiltonian simulation. Through code examples, we demonstrate how this interface simplifies both the practical realization of advanced quantum algorithms and their associated resource estimation.