Encoding Matters: A Qubit-Efficient Reformulation for Quantum Resilient Routing
Abstract
Qubits are powerful computational resources because each additional qubit doubles the size of the accessible state space. This expressive power comes at a cost: adding qubits also increases circuit size, constraint overhead, and exposure to hardware noise. These limitations are especially critical for near-term quantum computers, making the number of qubits required to formulate a problem a central practical concern. In this work, we present a method for efficiently mapping logistics and routing problems onto qubits through structure-aware encoding reduction. We demonstrate the method on a benchmark problem of latency-resilient routing under dual-link failures. For this benchmark, the reduced formulation substantially lowers both the qubit count and the compiled circuit complexity while preserving the original optimization task. By running the reduced model on the same trapped-ion quantum computer as the benchmark, we obtain a 100X improvement in the success probability. This finding shows how careful problem formulation can make current quantum hardware substantially more effective for practical optimization.
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BibTeX
@misc{torre2026encoding,
title = {{Encoding Matters: A Qubit-Efficient Reformulation for Quantum Resilient Routing}},
author = {Emanuele G. Dalla Torre and Bat-El Friedman and Daniel Porat},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.13621},
eprint = {2610.13621},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.13621},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Encoding Matters: A Qubit-Efficient Reformulation for Quantum Resilient Routing
AU - Emanuele G. Dalla Torre
AU - Bat-El Friedman
AU - Daniel Porat
PY - 2026
DA - 2026/10/09
PB - arXiv
DO - 10.48550/arxiv.2610.13621
UR - https://arxiv.org/abs/2610.13621
AB - Qubits are powerful computational resources because each additional qubit doubles the size of the accessible state space. This expressive power comes at a cost: adding qubits also increases circuit size, constraint overhead, and exposure to hardware noise. These limitations are especially critical for near-term quantum computers, making the number of qubits required to formulate a problem a central practical concern. In this work, we present a method for efficiently mapping logistics and routing problems onto qubits through structure-aware encoding reduction. We demonstrate the method on a benchmark problem of latency-resilient routing under dual-link failures. For this benchmark, the reduced formulation substantially lowers both the qubit count and the compiled circuit complexity while preserving the original optimization task. By running the reduced model on the same trapped-ion quantum computer as the benchmark, we obtain a 100X improvement in the success probability. This finding shows how careful problem formulation can make current quantum hardware substantially more effective for practical optimization.
ER -