# AI-Powered Algorithm-Centric Quantum Processor Topology Design

arXiv: https://arxiv.org/abs/2412.13805 · HTML: https://mlsanity.com/paper/2412.13805/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 48 references that are in the map.
- First posted on arXiv 18 Dec 2024.

## Builds on (most cited first, 8 of 8)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Efficient Learning for Deep Quantum Neural Networks](https://mlsanity.com/paper/1902.10445/) (2019)
- [Noise-Adaptive Compiler Mappings for Noisy Intermediate-Scale Quantum Computers](https://mlsanity.com/paper/1901.11054/) (2019)
- [Extracting Success from IBM's 20-Qubit Machines Using Error-Aware Compilation](https://mlsanity.com/paper/1903.10963/) (2019)
- [Optimal qubit assignment and routing via integer programming](https://mlsanity.com/paper/2106.06446/) (2021)
- [Compiling quantum circuits to realistic hardware architectures using temporal planners](https://mlsanity.com/paper/1705.08927/) (2017)
- [Not All SWAPs Have the Same Cost: A Case for Optimization-Aware Qubit Routing](https://mlsanity.com/paper/2205.10596/) (2022)
- [Single-Qubit Gates Matter for Optimising Quantum Circuit Depth in Qubit Mapping](https://mlsanity.com/paper/2308.00876/) (2023)

## Built on by (most cited first, 1 of 1)

- [QuantumMind: Constraint-Grounded Agentic Reasoning for Speedup Analysis in Quantum Computing](https://mlsanity.com/paper/2608.07743/) (2026)

## Abstract (arXiv)

Quantum computing promises to revolutionize various fields, yet the execution of quantum programs necessitates an effective compilation process. This involves strategically mapping quantum circuits onto the physical qubits of a quantum processor. The qubits' arrangement, or topology, is pivotal to the circuit's performance, a factor that often defies traditional heuristic or manual optimization methods due to its complexity. In this study, we introduce a novel approach leveraging reinforcement learning to dynamically tailor qubit topologies to the unique specifications of individual quantum circuits, guiding algorithm-driven quantum processor topology design for reducing the depth of mapped circuit, which is particularly critical for the output accuracy on noisy quantum processors. Our method marks a significant departure from previous methods that have been constrained to mapping circuits onto a fixed processor topology. Experiments demonstrate that we have achieved notable enhancements in circuit performance, with a minimum of 20% reduction in circuit depth in 60% of the cases examined, and a maximum enhancement of up to 46%. Furthermore, the pronounced benefits of our approach in reducing circuit depth become increasingly evident as the scale of the quantum circuits increases, exhibiting the scalability of our method in terms of problem size. This work advances the co-design of quantum processor architecture and algorithm mapping, offering a promising avenue for future research and development in the field.