SQARL: A Size-Agnostic Reinforcement Learning approach for Circuit Allocation in Distributed Quantum Architectures
arXiv:2605.27027v1 Announce Type: new Abstract: The scaling of quantum processors is currently limited by technical challenges such as decoherence and cross-talk. As the number of qubits grows, interference increases the computational noise. Distributed quantum computing addresses these limitations by interconnecting smaller, easier-to-handle quantum processors (cores), but it introduces the challenge of minimizing slow, error-prone inter-core communication. The task of distributing quantum circuits across cores while minimizing communication costs is known as the Qubit Allocation problem. Thi
The continuous scaling challenges of quantum processors necessitate innovative architectural solutions to overcome decoherence and noise, making distributed quantum computing a critical area of research.
Efficient qubit allocation in distributed quantum architectures will be crucial for the development of scalable and fault-tolerant quantum computers, impacting future computational capabilities.
This research outlines a method to better manage inter-core communication in distributed quantum systems, potentially accelerating the practical application of larger-scale quantum computing.
- · Quantum computing hardware developers
- · Quantum software developers
- · High-performance computing sector
- · Traditional supercomputing
Improved performance and scalability of distributed quantum computers.
Accelerated development of quantum algorithms and applications due to more robust hardware.
Potential for quantum advantage in new computational domains by overcoming current hardware limitations.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG