Towards an Efficiently Simulated Quantum Approximate Optimisation

Many large-scale optimisation problems, such as those in power systems, finance and logistics can be potentially solved in the fundamentally new approach of quantum algorithms, namely Quantum Approximate Optimisation Algorithm (QAOA). In the context of fuzzy systems for instance, it is already shown...

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Vydané v:IEEE International Fuzzy Systems conference proceedings s. 1 - 6
Hlavní autori: Alizadeh, Amir, Pourabdollah, Amir, Lotfi, Ahmad
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 06.07.2025
Predmet:
Computational modeling
Field programmable gate arrays
FPGA
Hardware
Integrated circuit modeling
optimisation
Optimization
Parallel processing
QAOA
Quantum algorithm
quantum computing
Quantum simulation
Scalability
Vectors
ISSN:1558-4739
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Popis
Shrnutí:Many large-scale optimisation problems, such as those in power systems, finance and logistics can be potentially solved in the fundamentally new approach of quantum algorithms, namely Quantum Approximate Optimisation Algorithm (QAOA). In the context of fuzzy systems for instance, it is already shown that some of the intense computation associated with complex or higher-order fuzzy logic systems can be offloaded to such quantum algorithms. While the quantum computation hardware technology is still in its infancy, efficient simulations of the quantum algorithms play a crucial role in advancing quantum algorithms. The current CPU/GPU-based approaches suffer from high energy consumption and speed and scalability limitations. This paper presents the current position of our research to develop an efficient and scalable QAOA simulation based on a large network of Field-Programmable Gate Array (FPGA) modules, inspired by FPGA's hardware-level parallelism. Although there exist a few attempts on using FPGA for general quantum circuits simulation, this project introduces a novel approach by employing a highly efficient recursive matrix decomposition algorithms specifically designed for QAOA. This work can pave the pathway a scalable and energy-efficient solution for cross-domain optimisation problems. The actual results and evaluations are to be reported in follow-up publications.
ISSN:1558-4739
DOI:10.1109/FUZZ62266.2025.11152265