mlsanityData to 7 Oct 2026

Optimal Layout Synthesis for Deep Quantum Circuits on NISQ Processors with 100+ Qubits

arXiv 2403.11598 ↗First posted 18 Mar 2024quant-ph cs.AIcited by 3

Not on the map. Why?

Abstract (arXiv)

Layout synthesis is mapping a quantum circuit to a quantum processor. SWAP gate insertions are needed for scheduling 2-qubit gates only on connected physical qubits. With the ever-increasing number of qubits in NISQ processors, scalable layout synthesis is of utmost importance. With large optimality gaps observed in heuristic approaches, scalable exact methods are needed. While recent exact and near-optimal approaches scale to moderate circuits, large deep circuits are still out of scope. In this work, we propose a SAT encoding based on parallel plans that apply 1 SWAP and a group of CNOTs at each time step. Using domain-specific information, we maintain optimality in parallel plans while scaling to large and deep circuits. From our results, we show the scalability of our approach which significantly outperforms leading exact and near-optimal approaches (up to 100x). For the first time, we can optimally map several 8, 14, and 16 qubit circuits onto 54, 80, and 127 qubit platforms with up to 17 SWAPs. While adding optimal SWAPs, we also report near-optimal depth in our mapped circuits.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 6 of its 31 references that are in the map. First posted on arXiv 18 Mar 2024.

Builds on

6 of 31 references.

References · 6

  1. 2022Qubit Mapping and Routing via MaxSATcited by 8
  2. 2020Optimality Study of Existing Quantum Computing Layout Synthesis Toolscited by 7
  3. 2023Optimal Layout Synthesis for Quantum Circuits as Classical Planning (full version)cited by 7
  4. 2019Optimization of Quantum Circuit Mapping using Gate Transformation and Commutationcited by 6
  5. 2020Optimal Layout Synthesis for Quantum Computingcited by 6
  6. 2023Constraint programming models for depth-optimal qubit assignment and SWAP-based routingcited by 1

Built on by

Cited by year

Citing papers per year: 2024: 1; 2025: 2.

3 papers.