.png){kind=icon}
Return
Grover Adaptive Search for Constrained Polynomial Binary Optimization
.png){kind=icon}
OA
AI
DOI：10.22331/q-2021-04-08-428.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper we discuss Grover Adaptive Search (GAS) for Constrained Polynomial Binary Optimization (CPBO) problems, and in particular, Quadratic Unconstrained Binary Optimization (QUBO) problems, as a special case. GAS can provide a quadratic speed-up for combinatorial optimization problems compared to brute force search. However, this requires the development of efficient oracles to represent problems and flag states that satisfy certain search criteria. In general, this can be achieved using quantum arithmetic, however, this is expensive in terms of Toffoli gates as well as required ancilla qubits, which can be prohibitive in the near-term. Within this work, we develop a way to construct efficient oracles to solve CPBO problems using GAS algorithms. We demonstrate this approach and the potential speed-up for the portfolio optimization problem, i.e. a QUBO, using simulation and experimental results obtained on real quantum hardware. However, our approach applies to higher-degree polynomial objective functions as well as constrained optimization problems.
Journal
IF:
5.4
Papers:
974
Citations:
1.0W
Organization
Cited Papers
ShareSaveAbnormal lipid and fatty acid compositions of kidneys from mice with polycystic kidney disease.png)
Lipids
LipidsIF0
PREAI
ShareSaveShareSave
View more