Q-MERGE: Parallelising Quantum State Preparation for Large-Scale Classical Data
Abstract
Quantum processing of classical data fundamentally relies on efficiently mapping classical datasets onto the amplitudes of quantum states. Preparing large amplitude-encoded states, however, remains a major bottleneck in quantum computing. In this paper, we introduce a novel state-preparation framework we call Q-MERGE, that addresses this bottleneck by partitioning a large target state into M n-qubit segments, preparing these segments independently and in parallel, and then coherently combining them into a single amplitude-encoded state using SELECT-SWAP operations and measurement. Q-MERGE is agnostic to the segment-level preparation method, allowing existing techniques to be applied to smaller subproblems while providing a tunable trade-off between circuit depth and qubit count. With mid-circuit measurement and preparation-register reuse, the required ancilla qubits can be reduced from O(Mn) to O(n). Applied to a real-world $32 x 1024$ ultrasound dataset, Q-MERGE achieves an infidelity of 3.5 x 10−9, compared with 6.2 x 10−3 for direct preparation using the same underlying method, a more than six orders of magnitude improvement at equal CNOT circuit depth. We demonstrate its feasibility experimentally with a 53 qubit circuit comprised of 954 1-qubit gates and 478 2-qubit gates, on the Quantinuum System Model H2 trapped-ion quantum computer. The prepared state is validated using shadow-overlap tomography. Numerical analysis of Haar-random states indicates consistent success probability for up to M=107 segments, supporting the scalability of Q-MERGE for encoding massive classical datasets.
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BibTeX
@misc{butterworth2026qmerge,
title = {{Q-MERGE: Parallelising Quantum State Preparation for Large-Scale Classical Data}},
author = {Archie Butterworth and Jens Renders and Jingbo Wang},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.04247},
eprint = {2610.04247},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.04247},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Q-MERGE: Parallelising Quantum State Preparation for Large-Scale Classical Data
AU - Archie Butterworth
AU - Jens Renders
AU - Jingbo Wang
PY - 2026
DA - 2026/10/28
PB - arXiv
DO - 10.48550/arxiv.2610.04247
UR - https://arxiv.org/abs/2610.04247
AB - Quantum processing of classical data fundamentally relies on efficiently mapping classical datasets onto the amplitudes of quantum states. Preparing large amplitude-encoded states, however, remains a major bottleneck in quantum computing. In this paper, we introduce a novel state-preparation framework we call Q-MERGE, that addresses this bottleneck by partitioning a large target state into M n-qubit segments, preparing these segments independently and in parallel, and then coherently combining them into a single amplitude-encoded state using SELECT-SWAP operations and measurement. Q-MERGE is agnostic to the segment-level preparation method, allowing existing techniques to be applied to smaller subproblems while providing a tunable trade-off between circuit depth and qubit count. With mid-circuit measurement and preparation-register reuse, the required ancilla qubits can be reduced from O(Mn) to O(n). Applied to a real-world $32 x 1024$ ultrasound dataset, Q-MERGE achieves an infidelity of 3.5 x 10^−9, compared with 6.2 x 10^−3 for direct preparation using the same underlying method, a more than six orders of magnitude improvement at equal CNOT circuit depth. We demonstrate its feasibility experimentally with a 53 qubit circuit comprised of 954 1-qubit gates and 478 2-qubit gates, on the Quantinuum System Model H2 trapped-ion quantum computer. The prepared state is validated using shadow-overlap tomography. Numerical analysis of Haar-random states indicates consistent success probability for up to M=10^7 segments, supporting the scalability of Q-MERGE for encoding massive classical datasets.
ER -