The DOI matches its Crossref record; 78 of the 80 references it cites were found in a registry or an index.
t|ket⟩: a retargetable compiler for NISQ devices
Open accessAbstract
We present t | ket ⟩, a quantum software development platform produced by Cambridge Quantum Computing Ltd. The heart of t | ket ⟩ is a language-agnostic optimising compiler designed to generate code for a variety of NISQ devices, which has several features designed to minimise the influence of device error. The compiler has been extensively benchmarked and outperforms most competitors in terms of circuit optimisation and qubit routing.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“It was recently proven [65] that at a fixed gate error, RCS is not a scalable route to quantum supremacy at large N”
From A Race-Track Trapped-Ion Quantum Processor (2023) · cites this paper as [65]
Citations
Cites
Quantum Computing in the NISQ era and beyond
Preskill, J. (2018) · Quantum · doi:10.22331/q-2018-08-06-79 · Cited by 8,830
Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer
Shor, P. W. (1997) · SIAM Journal on Computing · doi:10.1137/s0097539795293172 · Cited by 8,472
Quantum supremacy using a programmable superconducting processor
Arute, F., Arya, K., Babbush, R., Bacon, D., Bardin, J. C., Barends, R., Biswas, R., Boixo, S., Brandao, F. G. S. L., Buell, D. A., et al. (2019) · Nature · doi:10.1038/s41586-019-1666-5 · Cited by 7,720
A single quantum cannot be cloned
Wootters, W. K., & Zurek, W. H. (1982) · Nature · doi:10.1038/299802a0 · Cited by 5,406 · cited 1×
“For qubits, this linear resource management is justified by the no-cloning [50] and no-deleting [51] theorems.”
MethodA variational eigenvalue solver on a photonic quantum processor
Peruzzo, A., McClean, J., Shadbolt, P., Yung, M.-H., Zhou, X.-Q., Love, P. J., Aspuru-Guzik, A., & O’Brien, J. L. (2014) · Nature Communications · doi:10.1038/ncomms5213 · Cited by 4,552
Quantum Algorithm for Linear Systems of Equations
Harrow, A. W., Hassidim, A., & Lloyd, S. (2009) · Physical Review Letters · doi:10.1103/physrevlett.103.150502 · Cited by 3,325
Georgescu, I. M., Ashhab, S., & Nori, F. (2014) · Reviews of Modern Physics · doi:10.1103/revmodphys.86.153 · Cited by 2,932
A Quantum Approximate Optimization Algorithm
Farhi, E., Goldstone, J., & Gutmann, S. (2014) · arXiv · doi:10.48550/arxiv.1411.4028 · Cited by 1,789
Improved simulation of stabilizer circuits
Aaronson, S., & Gottesman, D. (2004) · Physical Review A · doi:10.1103/physreva.70.052328 · Cited by 1,515
Error mitigation extends the computational reach of a noisy quantum processor
Kandala, A., Temme, K., Corcoles, A. D., Mezzacapo, A., Chow, J. M., & Gambetta, J. (2019) · Nature · doi:10.1038/s41586-019-1040-7 · Cited by 947
Cited by
Sorted by citations
A Race-Track Trapped-Ion Quantum Processor
Moses, S. A., Baldwin, C. H., Allman, M. S., Ancona, R., Ascarrunz, L., Barnes, C., Bartolotta, J., Bjork, B., Blanchard, P., Bohn, M., et al. (2023) · Physical Review X · doi:10.1103/physrevx.13.041052 · Cited by 391
Quantum Simulation for High-Energy Physics
Bauer, C., Davoudi, Z., Balantekin, A. B., Tanmoy Bhattacharya, A., Carena, M. S., de Jong, W. A., Draper, P., El-Khadra, A. X., Gemelke, N., Hanada, M., et al. (2023) · PRX Quantum · doi:10.1103/prxquantum.4.027001 · Cited by 503
Qulacs: a fast and versatile quantum circuit simulator for research purpose
Suzuki, Y., Kawase, Y., Masumura, Y., Hiraga, Y., Nakadai, M., Chen, J., Nakanishi, K. M., Mitarai, K., Imai, R., Tamiya, S., et al. (2021) · Quantum · doi:10.22331/q-2021-10-06-559 · Cited by 357
Distributed quantum computing: A survey
Caleffi, M., Amoretti, M., Ferrari, D., Illiano, J., Manzalini, A., & Cacciapuoti, A. S. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110672 · Cited by 361
The bitter truth about gate-based quantum algorithms in the NISQ era
Leymann, F., & Barzen, J. (2020) · Quantum Science and Technology · doi:10.1088/2058-9565/abae7d · Cited by 251
MQT Bench: Benchmarking Software and Design Automation Tools for Quantum Computing
Quetschlich, N., Burgholzer, L., & Wille, R. (2023) · Quantum · doi:10.22331/q-2023-07-20-1062 · Cited by 270
Machine Learning of Noise-Resilient Quantum Circuits
Cincio, L., Rudinger, K., Sarovar, M., & Coles, P. J. (2021) · PRX Quantum · doi:10.1103/prxquantum.2.010324 · Cited by 126
QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer
Lorenz, R., Pearson, A., Meichanetzidis, K., Kartsaklis, D., & Coecke, B. (2023) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.14329 · Cited by 177
Filtering variational quantum algorithms for combinatorial optimization
Amaro, D., Modica, C., Rosenkranz, M., Fiorentini, M., Benedetti, M., & Lubasch, M. (2021) · Quantum Science and Technology · doi:10.1088/2058-9565/ac3e54 · Cited by 144
Review of Distributed Quantum Computing: From single QPU to High Performance Quantum Computing
Barral, D., Cardama, F. J., Díaz-Camacho, G., Faílde, D., Llovo, I. F., Juane, M. M., Vázquez-Pérez, J., Villasuso, J., Piñeiro, C., Costas, N., et al. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100747 · Cited by 179
Shaydulin, R., Li, C., Chakrabarti, S., DeCross, M., Herman, D., Kumar, N., Larson, J., Lykov, D., Minssen, P., Sun, Y., et al. (2024) · Science Advances · doi:10.1126/sciadv.adm6761 · Cited by 120
Heim, B., Soeken, M., Marshall, S., Granade, C., Roetteler, M., Geller, A., Troyer, M., & Svore, K. M. (2020) · Nature Reviews Physics · doi:10.1038/s42254-020-00245-7 · Cited by 131
A verified optimizer for Quantum circuits
Hietala, K., Rand, R., Hung, S.-H., Wu, X., & Hicks, M. (2021) · Proceedings of the ACM on Programming Languages · doi:10.1145/3434318 · Cited by 119
Optimal layout synthesis for quantum computing
Tan, B., & Cong, J. (2020) · Proceedings of the 39th International Conference on Computer-Aided Design · doi:10.1145/3400302.3415620 · Cited by 126
Scaling of the quantum approximate optimization algorithm on superconducting qubit based hardware
Weidenfeller, J., Valor, L. C., Gacon, J., Tornow, C., Bello, L., Woerner, S., & Egger, D. J. (2022) · Quantum · doi:10.22331/q-2022-12-07-870 · Cited by 127
A case study of variational quantum algorithms for a job shop scheduling problem
Amaro, D., Rosenkranz, M., Fitzpatrick, N., Hirano, K., & Fiorentini, M. (2022) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-022-00123-4 · Cited by 96
Quantum Natural Language Processing: Challenges and Opportunities
Guarasci, R., Pietro, G. D., & Esposito, M. (2022) · Applied Sciences · doi:10.3390/app12115651 · Cited by 85
Farrell, R. C., Chernyshev, I. A., Powell, S. J. M., Zemlevskiy, N. A., Illa, M., & A, M. J. S. (2023) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.107.054513 · Cited by 76
Topological order from measurements and feed-forward on a trapped ion quantum computer
Iqbal, M., Tantivasadakarn, N., Gatterman, T. M., Gerber, J. A., Gilmore, K., Gresh, D., Hankin, A. M., Hewitt, N., Horst, C. V., Matheny, M., et al. (2024) · Communications Physics · doi:10.1038/s42005-024-01698-3 · Cited by 121
TEQUILA: a platform for rapid development of quantum algorithms
Kottmann, J. S., Alperin-Lea, S., Tamayo-Mendoza, T., Cervera-Lierta, A., Lavigne, C., Yen, T.-C., Verteletskyi, V., Schleich, P., Anand, A., Degroote, M., et al. (2021) · Quantum Science and Technology · doi:10.1088/2058-9565/abe567 · Cited by 85
Quantum support vector machines for classification and regression on a trapped-ion quantum computer
Suzuki, T., Hasebe, T., & Miyazaki, T. (2024) · Quantum Machine Intelligence · doi:10.1007/s42484-024-00165-0 · Cited by 88
Quantum Software Development Lifecycle
Weder, B., Barzen, J., Leymann, F., & Vietz, D. (2022) · Springer eBooks · doi:10.1007/978-3-031-05324-5_4 · Cited by 78
Qubit-Reuse Compilation with Mid-Circuit Measurement and Reset
DeCross, M. P., Chertkov, E., Kohagen, M., & Foss‐Feig, M. (2023) · Physical Review X · doi:10.1103/physrevx.13.041057 · Cited by 73
The Quantum software lifecycle
Weder, B., Barzen, J., Leymann, F., Salm, M., & Vietz, D. (2020) · doi:10.1145/3412451.3428497 · Cited by 69
QFAST: Conflating Search and Numerical Optimization for Scalable Quantum Circuit Synthesis
Younis, E., Sen, K., Yelick, K., & Iancu, C. (2021) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce52317.2021.00041 · Cited by 66
Quartz: superoptimization of Quantum circuits
Xu, M., Li, Z., Padon, O., Lin, S., Pointing, J., Hirth, A., Ma, H., Palsberg, J., Aiken, A., Acar, U. A., et al. (2022) · ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) · doi:10.1145/3519939.3523433 · Cited by 64
TEQUILA: a platform for rapid development of quantum algorithms
Kottmann, J. S., Alperin-Lea, S., Chaney, S., Kesibi, M., Curnow, N. G., Solo, B., Tsilimigkounakis, G., Zendejas-Morales, C., Izmaylov, A. F., Aspuru‐Guzik, A., et al. (2021) · RWTH Publications (RWTH Aachen) · Cited by 68
Pulser: An open-source package for the design of pulse sequences in programmable neutral-atom arrays
Silvério, H., Grijalva, S., Dalyac, C., Leclerc, L., Karalekas, P. J., Shammah, N., Beji, M., Henry, L.-P., & Henriet, L. (2022) · Quantum · doi:10.22331/q-2022-01-24-629 · Cited by 83
Paulihedral: a generalized block-wise compiler optimization framework for Quantum simulation kernels
Li, G., Wu, A., Shi, Y., Javadi-Abhari, A., Ding, Y., & Xie, Y. (2022) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3503222.3507715 · Cited by 85
Quantum machine learning of large datasets using randomized measurements
Haug, T., Self, C. N., & Kim, M. S. (2023) · Machine Learning Science and Technology · doi:10.1088/2632-2153/acb0b4 · Cited by 88
QFaaS: A Serverless Function-as-a-Service framework for Quantum computing
Nguyen, H. T., Usman, M., & Buyya, R. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.01.018 · Cited by 77
Quantum computational quantification of protein–ligand interactions
Kirsopp, J. J. M., Di Paola, C., Manrique, D. Z., Krompiec, M., Greene‐Diniz, G., Guba, W., Meyder, A., Wolf, D., Strahm, M., & Muñoz Ramo, D. (2022) · International Journal of Quantum Chemistry · doi:10.1002/qua.26975 · Cited by 59
RetractedConstrained quantum optimization for extractive summarization on a trapped-ion quantum computer
Niroula, P., Shaydulin, R., Yalovetzky, R., Minssen, P., Herman, D., Hu, S., & Pistoia, M. (2022) · Scientific Reports · doi:10.1038/s41598-022-20853-w · Cited by 59
The NISQ Analyzer: Automating the Selection of Quantum Computers for Quantum Algorithms
Salm, M., Barzen, J., Breitenbücher, U., Leymann, F., Weder, B., & Wild, K. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-64846-6_5 · Cited by 66
Foundations for Near-Term Quantum Natural Language ProcessingPreprint
Coecke, B., de Felice, G., Meichanetzidis, K., & Toumi, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.03755 · Cited by 136
Suppressing Quantum Circuit Errors Due to System Variability
Nation, P. D., & Treinish, M. (2023) · PRX Quantum · doi:10.1103/prxquantum.4.010327 · Cited by 56
Integrating quantum computing resources into scientific HPC ecosystems
Beck, T. L., Baroni, A., Bennink, R. S., Buchs, G., Pérez, E. A. C., Eisenbach, M., da Silva, R. F., Meena, M. G., Gottiparthi, K., Groszkowski, P., et al. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.06.058 · Cited by 82
Optimal Qubit Assignment and Routing via Integer Programming
Nannicini, G., Bishop, L. S., Günlük, O., & Jurcevic, P. (2022) · ACM Transactions on Quantum Computing · doi:10.1145/3544563 · Cited by 67
MorphQ: Metamorphic Testing of the Qiskit Quantum Computing Platform
Paltenghi, M., & Pradel, M. (2023) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse48619.2023.00202 · Cited by 57
Best Approximate Quantum Compiling Problems
Madden, L., & Simonetto, A. (2022) · ACM Transactions on Quantum Computing · doi:10.1145/3505181 · Cited by 68
AutoComm: A Framework for Enabling Efficient Communication in Distributed Quantum Programs
Wu, A., Zhang, H., Li, G., Shabani, A., Xie, Y., & Ding, Y. (2022) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1109/micro56248.2022.00074 · Cited by 68
Advancing hybrid quantum–classical computation with real-time execution
Lubinski, T., Granade, C., Anderson, A., Geller, A., Roetteler, M., Petrenko, A., & Heim, B. (2022) · Frontiers in Physics · doi:10.3389/fphy.2022.940293 · Cited by 63
Qubit Mapping and Routing via MaxSAT
Molavi, A., Xu, A., Diges, M., Pick, L., Tannu, S., & Albarghouthi, A. (2022) · 2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1109/micro56248.2022.00077 · Cited by 54
Machine Learning Optimization of Quantum Circuit Layouts
Paler, A., Sasu, L. M., Florea, A.-C., & Andonie, R. (2022) · ACM Transactions on Quantum Computing · doi:10.1145/3565271 · Cited by 64
Alignment between initial state and mixer improves QAOA performance for constrained optimization
He, Z., Shaydulin, R., Chakrabarti, S., Herman, D., Li, C., Sun, Y., & Pistoia, M. (2023) · npj Quantum Information · doi:10.1038/s41534-023-00787-5 · Cited by 68
Application-Motivated, Holistic Benchmarking of a Full Quantum Computing Stack
Mills, D., Sivarajah, S., Scholten, T. L., & Duncan, R. (2021) · Quantum · doi:10.22331/q-2021-03-22-415 · Cited by 62
Self-citationVariational Inference with a Quantum Computer
Benedetti, M., Coyle, B., Fiorentini, M., Lubasch, M., & Rosenkranz, M. (2021) · Physical Review Applied · doi:10.1103/physrevapplied.16.044057 · Cited by 62
Equivalence Checking of Quantum Circuits With the ZX-Calculus
Peham, T., Burgholzer, L., & Wille, R. (2022) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2022.3202204 · Cited by 65
Compiler Optimization for Quantum Computing Using Reinforcement Learning
Quetschlich, N., Burgholzer, L., & Wille, R. (2023) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac56929.2023.10248002 · Cited by 47
Quantum Circuit Optimization and Transpilation via Parameterized Circuit Instantiation
Younis, E., & Iancu, C. (2022) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce53715.2022.00068 · Cited by 46
Compiling Quantum Circuits for Dynamically Field-Programmable Neutral Atoms Array Processors
Tan, D. B., Bluvstein, D., Lukin, M. D., & Cong, J. (2024) · Quantum · doi:10.22331/q-2024-03-14-1281 · Cited by 68
Qiskit Experiments: A Python package to characterize and calibrate quantum computers
Kanazawa, N., Egger, D. J., Ben‐Haim, Y., Zhang, H., Shanks, W., Aleksandrowicz, G., & Wood, C. J. (2023) · The Journal of Open Source Software · doi:10.21105/joss.05329 · Cited by 43
Synthesizing Quantum-Circuit Optimizers
Xu, A., Molavi, A., Pick, L., Tannu, S., & Albarghouthi, A. (2023) · Proceedings of the ACM on Programming Languages · doi:10.1145/3591254 · Cited by 42
Multi-Neutrino Entanglement and Correlations in Dense Neutrino Systems
Illa, M., & A, M. J. S. (2023) · Physical Review Letters · doi:10.1103/physrevlett.130.221003 · Cited by 42
Demonstrating Bayesian quantum phase estimation with quantum error detection
Yamamoto, K., Duffield, S., Kikuchi, Y., & Ramo, D. M. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.013221 · Cited by 52
Relaxed Peephole Optimization: A Novel Compiler Optimization for Quantum Circuits
Liu, J., Bello, L., & Zhou, H. (2021) · Proceedings of the CGO/Proceedings of the ... CGO · doi:10.1109/cgo51591.2021.9370310 · Cited by 47
Realization of quantum signal processing on a noisy quantum computer
Kikuchi, Y., Keever, C. M., Coopmans, L., Lubasch, M., & Benedetti, M. (2023) · npj Quantum Information · doi:10.1038/s41534-023-00762-0 · Cited by 43
Dahlberg, A., van der Vecht, B., Donne, C. D., Skrzypczyk, M., Raa, I. T., Kozlowski, W., & Wehner, S. (2022) · Quantum Science and Technology · doi:10.1088/2058-9565/ac753f · Cited by 54
LEAP: Scaling Numerical Optimization Based Synthesis Using an Incremental Approach
Smith, E., Davis, M. G., Larson, J. M., Younis, E., Oftelie, L. B., Lavrijsen, W., & Iancu, C. (2022) · ACM Transactions on Quantum Computing · doi:10.1145/3548693 · Cited by 45
MQT QMAP: Efficient Quantum Circuit Mapping
Wille, R., & Burgholzer, L. (2023) · Proceedings of the 2023 International Symposium on Physical Design · doi:10.1145/3569052.3578928 · Cited by 41
Mundada, P., Barbosa, A., Maity, S., Wang, Y., Merkh, T., Stace, T. M., Nielson, F. F., Carvalho, A. R. R., Hush, M., Biercuk, M. J., et al. (2023) · Physical Review Applied · doi:10.1103/physrevapplied.20.024034 · Cited by 51
Lao, L., & Browne, D. E. (2022) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3470496.3527394 · Cited by 45
Distributing circuits over heterogeneous, modular quantum computing network architectures
Andrés-Martínez, P., Forrer, T., Mills, D., Wu, J.-Y., Henaut, L., Yamamoto, K., Murao, M., & Duncan, R. (2024) · Quantum Science and Technology · doi:10.1088/2058-9565/ad6734 · Cited by 41
Self-citationClifford Circuit Optimization with Templates and Symbolic Pauli Gates
Bravyi, S. B., Shaydulin, R., Hu, S., & Maslov, D. (2021) · Quantum · doi:10.22331/q-2021-11-16-580 · Cited by 53
Application-motivated, holistic benchmarking of a full quantum computing stack
Mills, D., Sivarajah, S., Scholten, T. L., & Duncan, R. (2021) · Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) · Cited by 40
Self-citationDisCoPy: Monoidal Categories in Python
de Felice, G., Toumi, A., & Coecke, B. (2021) · Electronic Proceedings in Theoretical Computer Science · doi:10.4204/eptcs.333.13 · Cited by 37
Grammar-aware sentence classification on quantum computers
Meichanetzidis, K., Toumi, A., de Felice, G., & Coecke, B. (2023) · Quantum Machine Intelligence · doi:10.1007/s42484-023-00097-1 · Cited by 96
Scalable Optimal Layout Synthesis for NISQ Quantum Processors
Lin, W.-H., Kimko, J., Tan, D. B., Bjørner, N., & Cong, J. (2023) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac56929.2023.10247760 · Cited by 45
Investigating the effect of circuit cutting in QAOA for the MaxCut problem on NISQ devices
Bechtold, M., Barzen, J., Leymann, F., Mandl, A., Obst, J., Truger, F., & Weder, B. (2023) · Quantum Science and Technology · doi:10.1088/2058-9565/acf59c · Cited by 43
Mapping Quantum Circuits to Modular Architectures with QUBO
Bandić, M., Prielinger, L., Nüßlein, J., Ovide, A., Rodrigo, S., Abadal, S., van Someren, H., Vardoyan, G., Alarcón, E., Almudéver, C. G., et al. (2023) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce57702.2023.00094 · Cited by 42
Software-Hardware Co-Optimization for Computational Chemistry on Superconducting Quantum Processors
Li, G., Shi, Y., & Javadi-Abhari, A. (2021) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca52012.2021.00070 · Cited by 40
Optimal qubit mapping with simultaneous gate absorption
Tan, D. B., & Cong, J. (2021) · International Conference on Computer Aided Design · doi:10.1109/iccad51958.2021.9643554 · Cited by 40
A Generic Compilation Strategy for the Unitary Coupled Cluster AnsatzPreprint
Cowtan, A., Simmons, W., & Duncan, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.10515 · Cited by 49
Self-citationSchmid, L., Locher, D. F., Rispler, M., Blatt, S., Zeiher, J., Müller, M., & Wille, R. (2024) · Quantum Science and Technology · doi:10.1088/2058-9565/ad33ac · Cited by 43
Modelling carbon capture on metal-organic frameworks with quantum computing
Greene‐Diniz, G., Manrique, D. Z., Sennane, W., Magnin, Y., Shishenina, E., Cordier, P., Llewellyn, P. L., Krompiec, M., Rančić, M. J., & Ramo, D. M. (2022) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-022-00155-w · Cited by 35
Not All SWAPs Have the Same Cost: A Case for Optimization-Aware Qubit Routing
Liu, J., Li, P., & Zhou, H. (2022) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca53966.2022.00058 · Cited by 40
Quantum algorithms with local particle-number conservation: Noise effects and error correction
Streif, M., Leib, M., Wudarski, F., Rieffel, E. G., & Wang, Z. (2021) · Physical Review A · doi:10.1103/physreva.103.042412 · Cited by 36
Reducing the CNOT Count for Clifford+T Circuits on NISQ Architectures
Gheorghiu, V., Huang, J., Li, S. M., Mosca, M., & Mukhopadhyay, P. (2022) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2022.3213210 · Cited by 49
Predicting Good Quantum Circuit Compilation Options
Quetschlich, N., Burgholzer, L., & Wille, R. (2023) · IEEE International Conference on Quantum Software (QSW) · doi:10.1109/qsw59989.2023.00015 · Cited by 40
Bandić, M., Almudéver, C. G., & Feld, S. (2023) · Quantum Machine Intelligence · doi:10.1007/s42484-023-00124-1 · Cited by 35
Revisiting the Mapping of Quantum Circuits: Entering the Multi-core Era
Escofet, P., Ovide, A., Bandić, M., Prielinger, L., van Someren, H., Feld, S., Alarcón, E., Abadal, S., & Almudéver, C. G. (2024) · ACM Transactions on Quantum Computing · doi:10.1145/3655029 · Cited by 43
Noisy intermediate-scale quantum simulation of the one-dimensional wave equation
Wright, L., Keever, C. M., First, J. T., Johnston, R., Tillay, J., Chaney, S., Rosenkranz, M., & Lubasch, M. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.043169 · Cited by 30
Automating the Comparison of Quantum Compilers for Quantum Circuits
Salm, M., Barzen, J., Leymann, F., Weder, B., & Wild, K. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-87568-8_4 · Cited by 31
Shuttling for Scalable Trapped-Ion Quantum Computers
Schoenberger, D., Hillmich, S., Brandl, M., & Wille, R. (2024) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2024.3513262 · Cited by 39
Maronese, M., Moro, L. D., Rocutto, L., & Prati, E. (2022) · Springer eBooks · doi:10.1007/978-3-030-89746-8_2 · Cited by 33
Architecture-Aware Synthesis of Phase Polynomials for NISQ Devices
de Griend, A. M.-V., & Duncan, R. (2020) · Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) · doi:10.4204/eptcs.394.8 · Cited by 11
Self-citationA Quantum Natural Language Processing Approach to Musical Intelligence
Miranda, E. R., Yeung, R. W. K., Pearson, A., Meichanetzidis, K., & Coecke, B. (2022) · Springer eBooks · doi:10.1007/978-3-031-13909-3_13 · Cited by 28
Quantum Circuit Compiler for a Shuttling-Based Trapped-Ion Quantum Computer
Kreppel, F., Melzer, C., Millán, D. O., Wagner, J., Hilder, J., Poschinger, U., Schmidt‐Kaler, F., & Brinkmann, A. (2023) · Quantum · doi:10.22331/q-2023-11-08-1176 · Cited by 37
Tackling the Qubit Mapping Problem with Permutation-Aware Synthesis
Liu, J., Younis, E., Weiden, M., Hovland, P., Kubiatowicz, J. D., & Iancu, C. (2023) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce57702.2023.00090 · Cited by 36
NISQ Quantum Computing: A Security-Centric Tutorial and Survey [Feature]
Chen, F., Jiang, L., Müller, H. A., Richerme, P., Chu, C., Fu, Z., & Min, Y. (2024) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2024.3349665 · Cited by 32
HamLib: A library of Hamiltonians for benchmarking quantum algorithms and hardware
Sawaya, N. P. D., Marti-Dafcik, D., Ho, Y., Tabor, D. P., Bernal, D. E., Magann, A. B., Premaratne, S., Dubey, P. K., Matsuura, A. Y., Bishop, N. C., et al. (2024) · Quantum · doi:10.22331/q-2024-12-11-1559 · Cited by 39
Qubit Routing Using Graph Neural Network Aided Monte Carlo Tree Search
Sinha, A., Azad, U., & Singh, H. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i9.21231 · Cited by 29
Noisy gates for simulating quantum computers
Bartolomeo, G. D., Vischi, M., Cesa, F., Wixinger, R., Grossi, M., Donadi, S., & Bassi, A. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.043210 · Cited by 32
Benchmarking quantum computer simulation software packages: State vector simulators
Jamadagni, A., Läuchli, A. M., & Hempel, C. (2024) · SciPost Physics Core · doi:10.21468/scipostphyscore.7.4.075 · Cited by 43
Limiting the Search Space in Optimal Quantum Circuit Mapping
Burgholzer, L., Schneider, S., & Wille, R. (2022) · Springer eBooks · doi:10.1007/978-3-032-06770-8_11 · Cited by 28
Quetschlich, N., Burgholzer, L., & Wille, R. (2024) · ACM Transactions on Quantum Computing · doi:10.1145/3673241 · Cited by 40
Elsharkawy, A., To, X.-T. M., Seitz, P., Chen, Y., Stade, Y., Geiger, M., Huang, Q., Guo, X., Ansari, M. A., Mendl, C. B., et al. (2025) · ACM Transactions on Quantum Computing · doi:10.1145/3743149 · Cited by 27
Yamamoto, K., Manrique, D. Z., Khan, I. T., Sawada, H., & Ramo, D. M. (2022) · Physical Review Research · doi:10.1103/physrevresearch.4.033110 · Cited by 32
Improving Quantum Computation by Optimized Qubit Routing
Wagner, F., Bärmann, A., Liers, F., & Weissenbäck, M. (2023) · Journal of Optimization Theory and Applications · doi:10.1007/s10957-023-02229-w · Cited by 33
Graphical structures for design and verification of quantum error correction
Chancellor, N., Kissinger, A., Zohren, S., Roffe, J., & Horsman, D. C. (2023) · Quantum Science and Technology · doi:10.1088/2058-9565/acf157 · Cited by 69
Metadata
- Authors
- Sivarajah, S., Dilkes, S., Cowtan, A., Simmons, W., Edgington, A., & Duncan, R.
- Affiliations
- Cambridge Quantum Computing (United Kingdom) (GB)
- University of Strathclyde (GB)
- Corresponding author
- Sivarajah, Seyon
- Dilkes, Silas
- Cowtan, Alexander
- Simmons, Will
- Edgington, Alec
- Duncan, Ross
- Venue
- Quantum Science and Technology
- Publisher
- IOP Publishing
- Published
- 2020-11-06
- Volume
- 6
- Issue
- 1
- Pages
- 014003
- ISSN
- 2058-9565 (ISSN-L)
- OpenAlex
- W3013348036
- Cited by
- 475
- References
- 80
- Citations by year
- 2026: 69 · 2025: 111 · 2024: 84 · 2023: 88 · 2022: 66 · 2021: 40 · 2020: 16
- FWCI
- 20.64
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Quantum Science and Technology (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Quantum Information and Cryptography
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Keywords
- quantum compiler, NISQ devices, quantum circuit optimization, qubit routing, noise mitigation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review