Timing and Resource-Aware Mapping of Quantum Circuits to Superconducting Processors
Open accessAbstract
Quantum algorithms need to be compiled to respect the constraints imposed by quantum processors, which is known as the mapping problem. The mapping procedure will result in an increase of the number of gates and of the circuit latency, decreasing the algorithm’s success rate. It is crucial to minimize mapping overhead, especially for noisy intermediate-scale quantum (NISQ) processors that have relatively short qubit coherence times and high gate error rates. Most of prior mapping algorithms have only considered constraints, such as the primitive gate set and qubit connectivity, but the actual gate duration and the restrictions imposed by the use of shared classical control electronics have not been taken into account. In this article, we present a mapper called Qmap to make quantum circuits executable on scalable processors with the objective of achieving the shortest circuit latency. In particular, we propose an approach to formulate the classical control restrictions as resource constraints in a conventional list scheduler with polynomial complexity. Furthermore, we implement a routing heuristic to cope with the connectivity limitation. This router finds a set of movement operations that minimally extends circuit latency. To analyze the mapping overhead and evaluate the performance of different mappers, we map 56 quantum benchmarks onto a superconducting processor named Surface-17. Compared to a prior mapping strategy that minimizes the number of operations, Qmap can reduce the latency overhead (LtyOH) up to 47.3% and operation overhead up to 28.6%, respectively.
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The runtime of algorithms that calculate costs of all potential swapping candidates in each iteration when selecting a SWAP was very long.
“Although the above-mentioned algorithms claimed to achieve optimal mapping solutions, the runtime of these algorithms was very long due to the fact that they needed to calculate the costs of all the potential swapping candidates in each iteration when selecting a SWAP.”
From Robust Qubit Mapping Algorithm via Double-Source Optimal Routing on Large Quantum Circuits (2024) · cites this paper as [39]
These algorithms could only handle mapping problems up to 50 qubits.
“Consequently, they could only handle mapping problems up to 50 qubits, which is much smaller than the number of required qubits for achieving quantum advantage.”
From Robust Qubit Mapping Algorithm via Double-Source Optimal Routing on Large Quantum Circuits (2024) · cites this paper as [39]
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Rodrigo, S., Spanò, D., Bandić, M., Abadal, S., van Someren, H., Ovide, A., Feld, S., Almudéver, C. G., & Alarcón, E. (2022) · ACM International Conference on Nanoscale Computing and Communication (NANOCOM) · doi:10.1145/3558583.3558846 · Cited by 8 · see citing works
Profiling quantum circuits for their efficient execution on single- and multi-core architectures
Bandić, M., le Henaff, P., Ovide, A., Escofet, P., Rached, S. B., Rodrigo, S., van Someren, H., Abadal, S., Alarcón, E., Almudéver, C. G., et al. (2024) · Quantum Science and Technology · doi:10.1088/2058-9565/ada180 · Cited by 6 · see citing works
DasAtom: A Divide-and-Shuttle Atom Approach to Quantum Circuit Transformation
Huang, Y., Gao, D., Ying, S., & Li, S. (2025) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2025.3532818 · Cited by 7 · see citing works
Qubit Mapping Based on Tabu Search
Jiang, H., Deng, Y., & Xu, M. Q. (2024) · Journal of Computer Science and Technology · doi:10.1007/s11390-023-2121-5 · Cited by 6 · see citing works
Li, Y.-Z., Liu, W., Xu, G., Li, M., Chen, K., & He, S.-L. (2025) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2025.101923 · Cited by 5 · see citing works
Symmetry-based quantum circuit mapping
Yu, D., & Fang, K. (2024) · Physical Review Applied · doi:10.1103/physrevapplied.22.024029 · Cited by 5 · see citing works
Exploring a Double Full-Stack Communications-Enabled Architecture for Multi-Core Quantum ComputersPreprint
Rodrigo, S., Abadal, S., Alarcón, E., & Almudéver, C. G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.08186 · Cited by 5 · see citing works
CTQr: Control and Timing-Aware Qubit Routing
Huang, C., & Mak, W.-K. (2024) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1109/asp-dac58780.2024.10473795 · Cited by 4 · see citing works
Escofet, P., Das, A., Rached, S. B., Rodrigo, S., Domingo, J., Sebastiano, F., Babaie, M., Keskin, B., Edoardo Charbon, A., Bolívar, P. H., et al. (2025) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce65121.2025.00110 · Cited by 4 · see citing works
Structured Optimized Architecting of Full-Stack Quantum Systems in the NISQ era
Almudéver, C. G., & Alarcón, E. (2021) · Design, Automation, and Test in Europe · doi:10.23919/date51398.2021.9474197 · Cited by 4 · see citing works
Deep Cryogenic Temperature CMOS Circuit and System Design for Quantum Computing Applications
Jency Rubia, J., Shibi, C. S., Rosi, A., Babitha Lincy, R., & Nithila, E. E. (2024) · EAI Endorsed Transactions on Energy Web · doi:10.4108/ew.4997 · Cited by 4 · see citing works
Spatio-Temporal Characterization of Qubit Routing in Connectivity-Constrained Quantum Processors
Rached, S. B., Almudéver, C. G., Alarcón, E., & Abadal, S. (2024) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas58744.2024.10558197 · Cited by 3 · see citing works
MLQM: Machine learning approach for accelerating optimal qubit mapping
Sun, W., Li, X., Yu, L., Wang, Z., Chen, G., & Yang, G. (2025) · Future Generation Computer Systems · doi:10.1016/j.future.2025.107906 · Cited by 3 · see citing works
Quantum Circuit Transformation Based on Subgraph Isomorphism and Tabu SearchPreprint
Jiang, H., Deng, Y., & Xu, M. Q. (2021) · arXiv (Cornell University) · Cited by 3 · see citing works
Overhead in Quantum Circuits with Time-Multiplexed Qubit Control
Richter, M., Strandberg, I., Gasparinetti, S., & Kockum, A. F. (2026) · PRX Quantum · doi:10.1103/82cj-lfzy · Cited by 2 · see citing works
Qukkos: Measurement-Based Quantum Compilation System
Dang, S., Son, Y., & Kim, B. (2025) · International Conference on Artificial Intelligence, Computer, Data Sciences and Applications (ACDSA) · doi:10.1109/acdsa65407.2025.11165960 · Cited by 2 · see citing works
Park, S., Kim, D., Sim, J., & Kang, S. (2022) · doi:10.1145/3508352.3549462 · Cited by 2 · see citing works
Towards Effective Local Search for Qubit Mapping
Luo, C. W., Cao, S., Guo, S., & Hu, C. (2025) · IEEE Transactions on Computers · doi:10.1109/tc.2025.3544869 · Cited by 2 · see citing works
Quantum Firefly Algorithm: A Novel Approach for Quantum Circuit Scheduling Optimization
Du, Z., Wang, J., & Li, H. (2025) · Electronics · doi:10.3390/electronics14112123 · Cited by 2 · see citing works
S-SYNC: Shuttle and Swap Co-Optimization in Quantum Charge-Coupled Devices
Zhu, C., Wu, X. G., Wang, J., & Wang, X. (2025) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3695053.3731084 · Cited by 3 · see citing works
Qubit Health Analytics and Clustering for HPC-Integrated Quantum Processors
Deng, X., Schulz, L. B., & Schulz, M. (2025) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce65121.2025.10284 · Cited by 2 · see citing works
Hierarchical system mapping for large-scale fault-tolerant quantum computing
Hwang, Y., & Choi, B.-S. (2021) · Quantum Information Processing · doi:10.1007/s11128-021-03151-8 · Cited by 2 · see citing works
Realizing Quantum Algorithms on Real Quantum Computing DevicesPreprint
Almudéver, C. G., Lao, L., Wille, R., & Guerreschi, G. G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.01000 · Cited by 2 · see citing works
Self-citationQuantum Circuit Transformation Based on Tabu SearchPreprint
Jiang, H., Deng, Y., & Xu, M. Q. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.05214 · Cited by 2 · see citing works
Asynchronous Telegate and Teledata Protocols for Distributed Quantum Computing
Peckham, J., Makaroff, D., & Rayan, S. (2024) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot62078.2024.10811197 · Cited by 1 · see citing works
Research on Optimization of Hierarchical Quantum Circuit Scheduling Strategy
Han, Z., Li, H., Lu, K., Liu, S., & Ju, M. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.059577 · Cited by 2 · see citing works
GraMA: A gradient matrix-guided assignment method for solving qubit mapping problems
Piao, X., Kim, J., & Kim, J. (2026) · Future Generation Computer Systems · doi:10.1016/j.future.2026.108485 · Cited by 1 · see citing works
Quantum Circuit Pruning: Improving Fidelity via Compilation-Aware Circuit Approximation
Escofet, P., Rodrigo, S., Sarkar, R. S., Almudéver, C. G., Alarcón, E., & Abadal, S. (2026) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas66217.2026.11562331 · Cited by 1 · see citing works
Quantum Potential Speed-Up of Combinatorial Optimization Solvers for AUV Path Planning
Monbroussou, L., & Kukla, R. (2021) · MTS/IEEE OCEANS Conference (OCEANS) · doi:10.23919/oceans44145.2021.9706114 · Cited by 1 · see citing works
Robust hybrid qubit plane architecture for enhancing lattice-surgery-based surface codes
Heng, S., Heng, S., Kim, D., & Han, Y. (2024) · Physical Review A · doi:10.1103/physreva.109.022440 · Cited by 1 · see citing works
Effective and Efficient Parallel Qubit Mapper
Fu, H., Zhu, M., Chen, F., Zhang, C., Wu, J., Xie, W., & Li, X. (2024) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2024.3500784 · Cited by 1 · see citing works
A Pattern Matching Based Framework for Quantum Circuit Rewriting
Jiang, H., Li, D.-K., Deng, Y., & Xu, M. Q. (2024) · Journal of Computer Science and Technology · doi:10.1007/s11390-024-2726-3 · Cited by 1 · see citing works
State-dependent Routing Dynamics in Noisy Quantum Computing DevicesPreprint
Sadlier, R. J., & Humble, T. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.13131
A binary integer programming-based method for qubit mapping in sparse architectures
Jiang, H., Fu, J., Deng, Y., & Wu, J. (2024) · Acta Informatica · doi:10.1007/s00236-024-00471-x · Cited by 1 · see citing works
Time-optimization framework for the implementation of robust low-latency quantum circuits
Lussi, E. W., de Santiago, R., & Duzzioni, E. I. (2025) · Physical Review Applied · doi:10.1103/physrevapplied.23.044036
Optimizing Initial Qubit Mappings Under Fixed Gate Error Rates Using Deep Reinforcement Learning
Oancea, R. A., van der Linde, S., de Kok, W., Sabatelli, M., & Feld, S. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-94263-1_12
Natarajan, V. (2025) · Springer eBooks · doi:10.1007/978-981-96-5051-4_2
Space‐Efficient Logical Qubit Architecture with a Bus for Magic State Consumption
Kang, Y., Chung, Y., & Heo, J. (2025) · Advanced Quantum Technologies · doi:10.1002/qute.202500505
Efficiently Architecting VQAs: Expressibility–Trainability–Resources Pareto-Optimality
Sanz, R. M., Anglés-Castillo, A., Alarcón, E., & Almudever, C. G. (2026) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2026.3721256 · Cited by 2 · see citing works
Optimizing Unitary Coupled Cluster With Single and Double Kernels for Modern NISQ Architectures
Li, Y., Zhang, Y., Deng, H., Chen, M., & Guo, G. (2026) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2026.3677771
Hardware-Aware Compilation for Different Quantum Computing Platforms
Schmid, L., Schoenberger, D., Stade, Y., Burgholzer, L., & Wille, R. (2026) · Springer eBooks · doi:10.1007/978-3-032-09303-5_5
QDP: Worst Case Fidelity-Aware Qubit Mapping and Routing Using Dynamic Programming
Jiang, S., Cheng, W., Chung, Y.-H., Ho, T.-Y., & Huang, T. (2026) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2026.3693628
- Authors
- Lao, L., van Someren, H., Ashraf, I., & Almudever, C. G.
- Affiliations
- QuTech (NL)
- HITEC University (PK)
- Venue
- IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
- Published
- 2022-02
- Volume
- 41
- Issue
- 2
- Pages
- 359-371
- ISSN
- 0278-0070 (ISSN-L)
- 1937-4151
- OpenAlex
- W3038380154
- Cited by
- 72
- References
- 55
- Citations by year
- 2026: 7 · 2025: 15 · 2024: 18 · 2023: 13 · 2022: 7 · 2021: 8 · 2020: 4
- FWCI
- 4.06
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Research Repository (Delft University of Technology) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- Intel Corporation
- China Scholarship Council
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Information and Cryptography
- Physical Sciences › Engineering › Electrical and Electronic Engineering › VLSI and FPGA Design Techniques
- Keywords
- quantum circuit mapping, superconducting quantum processors, resource-aware scheduling, list scheduling, qubit connectivity, heuristic routing, NISQ devices, quantum circuit compilation
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