Solving combinatorial optimization and machine learning problems on hybrid near-term quantum photonic computers

Quantum computers are increasingly being integrated into classical computing environments, particularly within supercomputing and data centers, where they serve as accelerators for solving a wide range of specific problems. Previous research has indicated that quantum computers have the potential to...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Future generation computer systems Ročník 174; s. 107934
Hlavní autoři: Slysz, Mateusz, Grodzki, Łukasz, Rydlichowski, Piotr, Siera, Dawid, Kurowski, Krzysztof, Waligóra, Grzegorz, Węglarz, Jan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.01.2026
Témata:
Boson sampling
Combinatorial optimization
Hybrid quantum-classical computing
Job shop scheduling problem
Max-cut
Multi-QPUs
Photonic quantum computer
Quantum computing
Quantum neural networks
Photonic quantum computer
Job shop scheduling problem
Quantum computing
Boson sampling
Combinatorial optimization
Hybrid quantum-classical computing
Multi-QPUs
Quantum neural networks
Max-cut
ISSN:0167-739X
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Quantum computers are increasingly being integrated into classical computing environments, particularly within supercomputing and data centers, where they serve as accelerators for solving a wide range of specific problems. Previous research has indicated that quantum computers have the potential to resolve optimization problems with theoretically lower computational complexity than classical algorithms. However, in practical scenarios, even minor instances of these problems often require substantial computational resources, enhanced quality quantum hardware, and the results may not always be optimal. In this paper, we highlight the importance of hybrid systems, combining quantum and classical computations to address growing problem sizes and computational demands. We focus on optimization problems using hardware and software-enhanced quantum computers operational within a novel quantum–classical hybrid setup, incorporating GPUs and photonic quantum computers. The study specifically addresses combinatorial optimization problems, such as the Max-Cut and the Job Shop Scheduling Problem, in addition to selected machine learning classification use cases. Notably, the quantum algorithm in Max-Cut optimization outperformed complete solution searches, especially for larger problem instances. For the Job-Shop Scheduling Problem, we made a significant advancement by successfully solving substantially larger instances compared to our previous previous work. Furthermore, selected hybrid neural networks incorporating quantum layers showed improved stability, though without a clear quality advantage over classical models. The paper also highlights the rapid progress and technological achievements in both hardware and software used in near-term photonic quantum computers, suggesting a promising future for quantum–classical hybrid systems in useful applications.
ISSN:0167-739X
DOI:10.1016/j.future.2025.107934