Quantum compilation framework for data loading

• URL: http://arxiv.org/abs/2512.05183v1
• Date: Thu, 04 Dec 2025 19:00:01 GMT
• Title: Quantum compilation framework for data loading
• Authors: Guillermo Alonso-Linaje, Utkarsh Azad, Jay Soni, Jarrett Smalley, Leigh Lapworth, Juan Miguel Arrazola,
• Abstract summary: Efficient encoding of classical data into quantum circuits is a critical challenge that directly impacts the scalability of quantum algorithms.<n>We present an automated compilation framework for resource-aware quantum data loading tailored to a given input vector and target error tolerance.<n>We demonstrate the effectiveness of our framework across several applications, where it consistently uncovers non-obvious, resource-efficient strategies.
• Score: 0.9974960169271107
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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