A New Algorithm for Applying Sequences of Affine Transformations in Quantum Circuits

TL;DR

This paper presents a scalable quantum algorithm framework for applying nested affine transformations, enabling complex amplitude pattern generation with applications in finance and signal processing.

Contribution

It introduces a novel method combining Hadamard-supported initialization and block encoding for systematic affine transformations in quantum circuits.

Findings

Efficient computation of portfolio returns in financial risk assessment.

Enhanced signal reconstruction through manipulation of Fourier coefficients.

Demonstrated scalability and robustness of the proposed framework.

Abstract

This paper introduces a robust and scalable framework for implementing nested affine transformations in quantum circuits. Utilizing Hadamard-supported conditional initialization and block encoding, the proposed method systematically applies sequential affine transformations while preserving state normalization. This approach provides an effective method for generating combinatorial amplitude patterns within quantum states with demonstrated applications in combinatorics and signal processing. The utility of the framework is exemplified through two key applications: financial risk assessment, where it efficiently computes portfolio returns using combinatorial sum of amplitudes, and discrete signal processing, where it enables precise manipulation of Fourier coefficients for enhanced signal reconstruction.