.png){kind=icon}
.png){kind=icon}
Return
Resource-efficient quantum optimization via higher-order encoding
.png){kind=icon}
OA
AI
DOI：10.1140/epjqt/s40507-026-00526-7.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Quantum approaches to combinatorial optimization problems (COPs) are often limited by the resource demands of Quadratic Unconstrained Binary Optimization (QUBO) encodings, which enlarge circuits through penalty terms and increase qubit and gate counts. We show that Higher-Order Unconstrained Binary Optimization (HUBO) enables a more resource-efficient formulation. Our method systematically constructs HUBO Hamiltonians and, compared to a QUBO formulation in benchmarks on Gate Assignment (GAP), Maximum k-Colorable Subgraph (MkCS), and Integer Programming (IP) problems, significantly reduces qubit requirements and decreases total CNOT gate counts by at least 89.6% for all tested instances. These results highlight HUBO as a practical alternative for quantum optimization on near-term devices. To promote adoption, we release an open-source Python library that automates HUBO model construction, extends beyond the examples presented in this work, and broadens access to resource-efficient quantum optimization.
Keywords:
Quadratic Unconstrained Binary Optimization (QUBO)
Higher-Order Unconstrained Binary Optimization (HUBO)
Polynomial Unconstrained Binary Optimization (PUBO)
Quantum Approximate Optimization Algorithm (QAOA)
Combinatorial Optimization Problems (COPs)
Graph Coloring
Gate Assignment Problem (GAP)
Integer Programming (IP)
Quantum Optimization (QO)
Quantum Circuit (QC)
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5.6
Papers:
531
Citations:
1.1K