Quantum Re-Uploading for Calorimetry: Optimized Architectures with Extended Expressivity

TL;DR

This paper demonstrates that quantum re-uploading units (QRUs) can outperform standard variational circuits in calorimetry classification tasks by enhancing expressivity through repeated data encoding, with practical deployment on a superconducting quantum processor.

Contribution

The study introduces optimized quantum re-uploading architectures that extend expressivity and are practically deployable, outperforming traditional variational circuits in a calorimetry task.

Findings

QRUs achieve higher accuracy than mono-encoded baselines.

Most performance gains occur at small circuit depths.

Re-encoding expands harmonic support, enhancing expressivity.

Abstract

Near-term quantum machine learning must balance expressivity, optimization, and hardware constraints. We study quantum re-uploading units (QRUs) as compact circuits and compare them, at matched parameter count, to a standard mono-encoded variational quantum circuit (VQC) baseline. On a three-feature calorimetry classification task, we train a single-qubit QRU that outputs a scalar in $[-1,1]$ and map it to three classes via fixed thresholds. In this setting, QRUs obtain higher accuracy than the mono-encoded baseline. A controlled ablation over depth, input scaling, circuit template, optimizer, and gradient accumulation indicates that most gains occur at small depths, with diminishing returns as depth increases while training cost grows approximately linearly. To interpret these observations, we analyze reachable Fourier components and find that repeated data re-encoding expands the per-coordinate harmonic support relative to mono-encoding, consistent with a spectral activation study over random initializations. Finally, we report an end-to-end proof-of-execution of the trained model on a superconducting QPU via a cloud workflow, illustrating practical deployability under current constraints.