mlsanityData to 14 Oct 2026

Circuit Partitioning for Multi-Core Quantum Architectures with Deep Reinforcement Learning

arXiv 2401.17976 ↗First posted 31 Jan 2024quant-ph cs.AIcited by 1

On the map: Deep reinforcement learning methods → Quantum reinforcement learning

Abstract (arXiv)

Quantum computing holds immense potential for solving classically intractable problems by leveraging the unique properties of quantum mechanics. The scalability of quantum architectures remains a significant challenge. Multi-core quantum architectures are proposed to solve the scalability problem, arising a new set of challenges in hardware, communications and compilation, among others. One of these challenges is to adapt a quantum algorithm to fit within the different cores of the quantum computer. This paper presents a novel approach for circuit partitioning using Deep Reinforcement Learning, contributing to the advancement of both quantum computing and graph partitioning. This work is the first step in integrating Deep Reinforcement Learning techniques into Quantum Circuit Mapping, opening the door to a new paradigm of solutions to such problems.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods. Builds on 4 of its 29 references that are in the map. First posted on arXiv 31 Jan 2024.

Builds on

4 of 29 references.

References · 4

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Deep Reinforcement Learning: An Overviewcited by 333
  3. 2018An Introduction to Deep Reinforcement Learningcited by 206
  4. 2020A Closer Look at Invalid Action Masking in Policy Gradient Algorithmscited by 171

Built on by

Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024Attention-Based Deep Reinforcement Learning for Qubit Allocation in Modular Quantum Architecturescited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025Reinforcement Learning for Quantum Circuit Design: Using Matrix Representations
  2. 2024Reinforcement learning-based architecture search for quantum machine learning
  3. 2024AI-Powered Algorithm-Centric Quantum Processor Topology Design
  4. 2026SQARL: A Size-Agnostic Reinforcement Learning approach for Circuit Allocation in Distributed Quantum Architectures
  5. 2025It's-A-Me, Quantum Mario: Scalable Quantum Reinforcement Learning with Multi-Chip Ensembles
  6. 2024Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardware
  7. 2025Hybrid Reward-Driven Reinforcement Learning for Efficient Quantum Circuit Synthesis
  8. 2025Optimizing Quantum Circuits via ZX Diagrams using Reinforcement Learning and Graph Neural Networks