Adaptive Interpolating Quantum Transform: A Quantum-Native Framework for Efficient Transform Learning

TL;DR

The paper introduces AIQT, a quantum-native framework that efficiently learns quantum transforms by interpolating between known transforms, reducing parameter overhead and maintaining quantum advantages.

Contribution

AIQT is a novel, flexible framework for quantum transform learning that minimizes parameters and inherits quantum advantages, improving scalability and interpretability.

Findings

AIQT achieves high performance with fewer parameters.

AIQT enables scalable and interpretable quantum learning.

AIQT inherits quantum advantages from constituent transforms.

Abstract

Machine learning on quantum computers has attracted attention for its potential to deliver computational speedups in different tasks. However, deep variational quantum circuits require a large number of trainable parameters that grows with both qubit count and circuit depth, often rendering training infeasible. In this study, we introduce the Adaptive Interpolating Quantum Transform (AIQT), a quantum-native framework for flexible and efficient learning. AIQT defines a trainable unitary that interpolates between quantum transforms, such as the Hadamard and quantum Fourier transforms. This approach enables expressive quantum state manipulation while controlling parameter overhead. It also allows AIQT to inherit any quantum advantages present in its constituent transforms. Our results show that AIQT achieves high performance with minimal parameter count, offering a scalable and interpretable alternative to deep variational circuits.