Quantum compilation framework for data loading

TL;DR

This paper introduces an automated, resource-aware quantum data loading framework that optimizes quantum circuit resources by balancing approximation and precision, enabling more efficient large-scale quantum algorithms.

Contribution

It presents a systematic compilation framework that integrates multiple state-of-the-art data loading methods with error trade-off optimization, improving resource efficiency in quantum data encoding.

Findings

Achieved over four orders of magnitude resource reduction in fluid dynamics applications.

Developed a more efficient circuit for d-diagonal matrices.

Created an optimized block encoding for kinetic energy operators.

Abstract

Efficient encoding of classical data into quantum circuits is a critical challenge that directly impacts the scalability of quantum algorithms. In this work, we present an automated compilation framework for resource-aware quantum data loading tailored to a given input vector and target error tolerance. By explicitly exploiting the trade-off between exact and approximate state preparation, our approach systematically partitions the total error budget between precision and approximation errors, thereby minimizing quantum resource costs. The framework supports a comprehensive suite of state-of-the-art methods, including multiplexer-based loaders, quantum read-only memory (QROM) constructions, sparse encodings, matrix product states (MPS), Fourier series loaders (FSL), and Walsh transform-based diagonal operators. We demonstrate the effectiveness of our framework across several applications, where it consistently uncovers non-obvious, resource-efficient strategies enabled by controlled approximation. In particular, we analyze a computational fluid dynamics workflow where the automated selection of MPS state preparation and Walsh transform-based encoding, combined with a novel Walsh-based measurement technique, leads to resource reductions of over four orders of magnitude compared to previous approaches. We also introduce two independent advances developed through the framework: a more efficient circuit for d-diagonal matrices, and an optimized block encoding for kinetic energy operators. Our results underscore the indispensable role of automated, approximation-aware compilation in making large-scale quantum algorithms feasible on resource-constrained hardware.