Adaptive Qubit Encoding for Continuous-Variable Quantum Optimization
Abstract
Optimizing continuous-variable functions on qubit-based quantum hardware requires a finite representation of the search space and a solver-compatible representation of the objective function. Fixed-grid discretizations provide a direct encoding, but restrict the search to a predefined range and resolution; at the same time, constructing a compact Hamiltonian for a general continuous-variable objective can be impractical. Here, we introduce a framework for continuous-variable optimization tailored to qubit-based quantum architectures. The method combines an adaptive discretization with an iterative local mapping of the objective function onto an effective spin-glass Hamiltonian. We assess the approach on unconstrained and constrained instances of the n-dimensional Gramacy-Lee benchmark, and on a proof-of-concept inverse-planning problem in intensity-modulated radiotherapy (IMRT) for cancer treatment. The results show that this framework can produce competitive solutions, supporting its use as a flexible mapping strategy for continuous-variable optimization in qubit-based quantum and quantum-inspired pipelines.
Related
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- Coherent receiver for Continuous-Variable Quantum Key Distribution using a 3 × 3 beam splitter and single-ended photodetectors
- Distributed Inner Product Estimation of Continuous-Variable Quantum States
BibTeX
@misc{cavinato2026adaptive,
title = {{Adaptive Qubit Encoding for Continuous-Variable Quantum Optimization}},
author = {Samuele Cavinato and Marco Tesoro and Gianluca Morettini and Marta Paiusco and Alessandro Scaggion and Simone Montangero and Ilaria Siloi},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.03987},
eprint = {2611.03987},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.03987},
}
RIS
TY - UNPB
TI - Adaptive Qubit Encoding for Continuous-Variable Quantum Optimization
AU - Samuele Cavinato
AU - Marco Tesoro
AU - Gianluca Morettini
AU - Marta Paiusco
AU - Alessandro Scaggion
AU - Simone Montangero
AU - Ilaria Siloi
PY - 2026
DA - 2026/11/02
PB - arXiv
DO - 10.48550/arxiv.2611.03987
UR - https://arxiv.org/abs/2611.03987
AB - Optimizing continuous-variable functions on qubit-based quantum hardware requires a finite representation of the search space and a solver-compatible representation of the objective function. Fixed-grid discretizations provide a direct encoding, but restrict the search to a predefined range and resolution; at the same time, constructing a compact Hamiltonian for a general continuous-variable objective can be impractical. Here, we introduce a framework for continuous-variable optimization tailored to qubit-based quantum architectures. The method combines an adaptive discretization with an iterative local mapping of the objective function onto an effective spin-glass Hamiltonian. We assess the approach on unconstrained and constrained instances of the n-dimensional Gramacy-Lee benchmark, and on a proof-of-concept inverse-planning problem in intensity-modulated radiotherapy (IMRT) for cancer treatment. The results show that this framework can produce competitive solutions, supporting its use as a flexible mapping strategy for continuous-variable optimization in qubit-based quantum and quantum-inspired pipelines.
ER -