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
Items 401–473 of 473
BOSS: Blocking algorithm for optimizing shuttling scheduling in Ion Trap
Wu, X., Zhu, C., Wang, J., & Wang, X. (2025) · IEEE International Symposium on High-Performance Computer Architecture (HPCA) · doi:10.1109/hpca61900.2025.00032 · Cited by 4
Optimizing Ancilla-Based Quantum Circuits with SPARE
Sharma, R., & Achour, S. (2025) · Proceedings of the ACM on Programming Languages · doi:10.1145/3729253
Unleashing Optimization in Dynamic Circuits through Branch Expansion
Chen, Y. (2025) · ACM International Conference on Computing Frontiers (CF) · doi:10.1145/3719276.3725187
Parallel token swapping for qubit routing
Bansal, I., Günlük, O., & Shapley, R. (2025) · Discrete Applied Mathematics · doi:10.1016/j.dam.2025.08.005 · Cited by 2
An optimized sparrow search algorithm for quantum circuit mapping
Li, H., Liu, S., Wang, J., Ji, Y., Ma, Y., & Chen, Y. (2025) · The Computer Journal · doi:10.1093/comjnl/bxaf098
A Portable Auto-Tuning Framework for Quantum Compilation Optimization Based on D3QN
Liu, Y., Jin, Y., & Xu, J. (2025) · International Conference on Computer Information and Big Data Applications (CIBDA) · doi:10.1145/3746709.3746950
PHOENIX: Pauli-Based High-Level Optimization Engine for Instruction Execution on NISQ Devices
Yang, Z., Ding, D., Zhu, C., Chen, J., & Xie, Y. (2025) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac63849.2025.11133028 · Cited by 3
Assessing Quantum Layout Synthesis Tools via Known Optimal-SWAP Cost Benchmarks
Ping, S., Lin, W.-H., Tan, D. B., & Cong, J. (2025) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac63849.2025.11133143 · Cited by 5
DDRoute: a Novel Depth-Driven Approach to the Qubit Routing Problem
Annechini, A., Vénere, M., Sciuto, D., & Santambrogio, M. D. (2025) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac63849.2025.11133018 · Cited by 2
Ying, M., Zhou, L., & Barthe, G. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3765903
Preparing the Gutzwiller wave function for attractive SU(3) fermions on a quantum computer
Xu, H., Seki, K., & Yunoki, S. (2025) · Physical Review Research · doi:10.1103/rc6z-837q
How to Synthesize Quantum-Circuit Optimizers
Xu, A. (2025) · ACM SIGPLAN International Conference on Systems, Programming, Languages, and Applications: Software for Humanity (SPLASH) · doi:10.1145/3758316.3762820
Designing Hybrid Quantum-Classical Web Applications Across the Computing Continuum
Aparicio-Morales, Á. M., Moguel, E., & García-Alonso, J. M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-97207-2_36
A Hybrid Quantum Computing Method for UV–Vis Spectroscopy of Solvated Molecules at Room Temperature
Bistafa, C., Kirsopp, J. J. M., Romero, A. M., Koyama, J., & Krompiec, M. (2025) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.5c02811
Data-efficient quantum noise modeling via machine learning
Ji, Y., Roth, M., Kreplin, D. A., Polian, I., & Wilhelm, F. K. (2026) · Physical Review Applied · doi:10.1103/5r9m-y6z6
Mori, Y., Hakoshima, H., & Fujii, K. (2026) · PRX Quantum · doi:10.1103/qw5z-3bwz · Cited by 2
An Efficient Routing Optimization Framework for Silicon-Based Spin-Qubit Devices
Huang, C., & Mak, W.-K. (2025) · doi:10.1109/iccad66269.2025.11240960
Optimising Quantum Algorithm Schemes According to the Topology of Quantum Computers
Keibas, G., & Petkevičius, L. (2025) · IEEE Workshop on Advances in Information, Electronic and Electrical Engineering (AIEEE) · doi:10.1109/aieee66149.2025.11050773
Compiler Design for Hardware Specific Decomposition Optimizations, Tailored to Diamond NV Centers
de Ronde, F., Wong, S., & Feld, S. (2025) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce65121.2025.00095
Additional file 1 of 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. (2024) · Figshare · doi:10.6084/m9.figshare.26554478
OpenMP-Q: Quantum Task Offloading in OpenMP
Mittal, S., Mahesh, A., & Mueller, F. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-06343-4_6
MUSS-TI: Multi-level Shuttle Scheduling for Large-Scale Entanglement Module Linked Trapped-Ion
Wu, X., Zhu, C., Wang, J., & Wang, X. (2025) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1145/3725843.3756129 · Cited by 2
Petchartee, S. (2025) · International Computer Science and Engineering Conference · doi:10.1109/icsec67360.2025.11298108
Optimising Quantum Calculations Reliability via Machine Learning: The IBM Case Study
Dahi, Z. A., Chicano, F., Luque, G., & Alba, I. D. (2025) · IEEE International Conference on Quantum Artificial Intelligence (QAI) · doi:10.1109/qai63978.2025.00055
Divide-Et-Impera Heuristic-Based Randomized Search for the Qubit Routing Problem
Baioletti, M., Fagiolo, F., Oddi, A., & Rasconi, R. (2025) · IEEE International Conference on Quantum Artificial Intelligence (QAI) · doi:10.1109/qai63978.2025.00050
Quantum Synthesis of Large S-Boxes: Heuristic and MILP-Based Transpiled-Depth Optimization
Yadav, T., Singh, S., & Yadav, S. (2025) · IACR Cryptology ePrint Archive · doi:10.1007/978-3-032-16342-4_21 · Cited by 1
Dependence-Driven, Scalable Quantum Circuit Mapping with Affine Abstractions
Benbetka, M., Bekkar, M., Baghdadi, R., & Kong, M. (2026) · Proceedings of the CGO/Proceedings of the ... CGO · doi:10.1109/cgo68049.2026.11394841 · Cited by 1
OpenQudit: Extensible and Accelerated Numerical Quantum Compilation via a JIT-Compiled DSL
Younis, E. (2026) · Proceedings of the CGO/Proceedings of the ... CGO · doi:10.1109/cgo68049.2026.11394847
Performance of parity QAOA for the signed Max-Cut problem
Weidinger, A., Mbeng, G. B., FELLNER, M. J., Khachatryan, D., & Lechner, W. (2026) · New Journal of Physics · doi:10.1088/1367-2630/ae49b6
A Divide-et-Impera approach for compiling large quantum circuits via ant colony optimization
Baioletti, M., Oddi, A., & Rasconi, R. (2026) · Evolutionary Intelligence · doi:10.1007/s12065-025-01127-6
Assessing advantage from quantum linear solvers for graph-based problems
Shetty, D., Dutta, S., Chawla, P., Jayashankar, A., Riu, J., Nogué, J., Sugisaki, K., & Prasannaa, V. S. (2026) · Physical Review A · doi:10.1103/7g49-42r6
Architecting Scalable Trapped Ion Quantum Computers using Surface Codes
Jones, S., & Murali, P. (2026) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3779212.3790128 · Cited by 6
Efficiently Architecting VQAs: Expressibility–Trainability–Resources Pareto-Optimality
Sanz, R., Anglés-Castillo, A., Alarcon, E., & Almudéver, C. G. (2026) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2026.3721256 · Cited by 2
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
Logical-to-Physical Compilation for Reducing Depth in Distributed Quantum Systems
de Ronde, F., Wong, S., & Feld, S. (2026) · International Conference on Computer Communications and Networks · doi:10.1109/icccn69946.2026.11662592
Fujita, M., & Kimura, Y. (2026) · Springer eBooks · doi:10.1007/978-3-032-09303-5_8
Assertion-Based Debugging of Quantum Circuits: Techniques, Effectiveness, and Challenges
Kole, A., Maidl, A., & Drechsler, R. (2026) · Springer eBooks · doi:10.1007/978-3-032-09303-5_10
Distributed Quantum Computing for Scalable Systems
Casado-Faulí, D., Atchade-Adelomou, P., de Lara, D. P., & Gil-Merino, R. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-05748-8_20
Classification of security challenges and mitigation approaches in the quantum software engineering
Rahman, B., Haghparast, M., & Mikkonen, T. (2026) · Journal of Systems and Software · doi:10.1016/j.jss.2026.112884
Direct Energy Gap Calculations in Heisenberg Spin Systems Using Superconducting Quantum Devices
Paul, B., Mandal, S. B., Sugisaki, K., & Das, B. P. (2026) · Journal of Computational Chemistry · doi:10.1002/jcc.70355
QEMI: A Quantum Software Stacks Testing Framework via Equivalence Modulo Inputs
Luo, J., Xia, S., Zhang, F., & Zhao, J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-22774-4_8
Online Scheduling and Distribution of Quantum Circuits Over Quantum Networks
Yang, Y., & Gupta, H. (2026) · International Conference on Quantum Communications, Networking, and Computing (QCNC) · doi:10.1109/qcnc69040.2026.00045
Burgholzer, L., & Wille, R. (2026) · Springer eBooks · doi:10.1007/978-3-032-06770-8_9
Software Between Quantum and Machine Learning - and Down to Pulses
Franz, M., Strobl, M., Hunz, J., Scheller, L., van der Horst, L., Kuehn, E., Streit, A., & Mauerer, W. (2026) · IEEE International Conference on Quantum Software (QSW) · doi:10.1109/qsw72780.2026.00017 · Cited by 2
Quantum Circuit Transpilation: A Comprehensive Overview
Karmakar, D., Chatterjee, K., & Kole, D. K. (2026) · Lecture notes in networks and systems · doi:10.1007/978-981-95-0497-8_43
Graph-Optimal T-Layering versus Replayable Local Realizability in Clifford+T CircuitsPreprint
Bíró, C. (2026) · Research Square · doi:10.21203/rs.3.rs-9732418/v1
Optimal Compilation of Syndrome Extraction Circuits for General Quantum LDPC Codes
Zhang, K., Gao, D., Yang, Z., Zhou, R., Liu, F., Ji, Z., & Chen, J. (2026) · Design, Automation, and Test in Europe · doi:10.23919/date69613.2026.11539585 · Cited by 7
Kuo, Y.-H., Yang, H.-C., Chen, H.-Y., & Huang, C.-Y. R. (2026) · Design, Automation, and Test in Europe · doi:10.23919/date69613.2026.11539330
Prediction of Compilation Options for Quantum Circuits via Adaptive Deep Residual Neural Network
He, S., Wen, L., Li, Y., Li, M., Chen, K., & Lu, Y. (2026) · Quantum Information and Computation · doi:10.2478/qic-2026-0003
Equality Saturation for Quantum Circuit Optimization
Yang, G., Raun, P., Tao, R., & Gu, R. (2026) · Proceedings of the ACM on Programming Languages · doi:10.1145/3808254
Calibration-Aware Bit-Flip Averaging For Quantum Error Mitigation
Choptiany, D. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-29254-4_33
CLOAQ: Combined Logic and Angle Obfuscation for Quantum Circuits
Langford, V., Zhao, S., Zhang, H., Dong, B., Wang, Q., Rehman, A., & Liu, Y. (2026) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas66217.2026.11562376
QC-MM: A Metadata and Schema Model for Traceable Quantum-Circuit Experiments
Huenchuleo, N., Sepúlveda, S., & Fernández, A. (2026) · Applied Sciences · doi:10.3390/app16136346 · Cited by 1
AI-Empowered Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospects
Yan, G., Wu, W., Chen, Y., Pan, K., Lü, X., Zh, Z., Wang, Y., Wang, R. L., & Yan, J. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3715237 · Cited by 90
Eliminating vendor lock-in in quantum machine learning via framework-agnostic neural networks
Kumaresan, P., Singaravelu, S., Rajendran, L., & Sivasubramani, S. (2026) · Frontiers in Quantum Science and Technology · doi:10.3389/frqst.2026.1862700
How Many Quantum Circuit Identities Are Needed to Generate All Others?
Ding, Y., Yu, N., & Qiu, X. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-32537-2_14
Comparative analysis of quantum software development kits using Halstead complexity metricsPreprint
K, A. K. T., & Hazra, I. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7197760
Quantora: A Quantum-Aware Machine Learning Simulator for Enterprise-Scale AI Experimentation
Routray, S., Raja, G., Rao, G. S. V., Janani, A., Sadhasivam, T., Hari Krishnan, P., Rout, S., & Saha, S. D. (2025) · International Academic Symposium on Electronic Information and Signal Processing (EISP) · doi:10.1109/eisp70220.2025.00021
Simulation of vibrational dynamics using qubits and qudits
Anonymous, & Yamanouchi, K. (2026) · Physical Review A · doi:10.1103/q69k-y623 · Cited by 1
Distributed Quantum Circuit Optimisation: Evaluating Global and Local Encodings
Garces, M. G., & Haghparast, M. (2026) · IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW) · doi:10.1109/icdcsw72724.2026.00041
CARL: Crosstalk-Aware Quantum Compilation Based on Reinforcement Learning
Zhang, X., Liu, Y., Mao, Y., Xu, X., & Yang, Y. (2026) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/2575-8411.2026.00043
Hybrid Classical-Quantum Systems: The Unsung Heroes of the NISQ EraPreprint
Amiri, S. M. H., & Zahan, A. (2026) · Preprints.org · doi:10.20944/preprints202608.1044.v1
QPanda 3.0: A scalable and high performance compilation framework for large scale quantum programs
Zou, T.-R., Yuan, F., Wang, J., Fu, J., Zhao, S.-B., Yu, L., Zhao, D.-Y., Wang, H., Zhao, C.-Y., Ma, Y., et al. (2026) · Quantum Review Letters · doi:10.1016/j.qrl.2026.08.001
Robust Shuttling Compilation for Trapped-Ion Quantum Computers
Schoenberger, D., & Wille, R. (2026) · IEEE International Conference on Quantum Software (QSW) · doi:10.1109/qsw72780.2026.00036
Quantum anomaly for benchmarking quantum computing
Anonymous, & Yamamoto, A. (2026) · Physical Review Research · doi:10.1103/l8w6-pk4m
Generalized quantum neural modules for high-dimensional local quantum feature learning
Yu, W., Fan, Y., Wang, Y., Yang, Z., Li, R., Cheng, H., Zhu, K., & Chen, Y. (2026) · Machine Learning Science and Technology · doi:10.1088/2632-2153/aea3a1
IterTestQ: Assembly-Level, Cross-Platform Testing of Quantum Computing Platforms
Paltenghi, M., & Pradel, M. (2026) · Proceedings of the ACM on software engineering. · doi:10.1145/3832211
GPU-Accelerated Host-Aware Dead-Measurement Detection in Hybrid Quantum–Classical Programs
Chen, Y., Liu, Q., Wang, Y., Mendl, C. B., & Seidl, H. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-40374-2_19
Beyond the Hoopla: What Quantum AI Actually Delivers
Feretzakis, G., & Verykios, V. S. (2026) · Pan-Hellenic Conference on Progress in Computing and Informatics (PCI) · doi:10.1145/3829216.3829326
NEAT: A Neutral-Atom Transpiler for Joint Mapping and Scheduling of Syndrome Extraction Circuits
Gao, D., Zhang, K., Li, S., Ying, S., Liu, F., & Chen, J. (2026) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/3770743.3804083
QBET: Quantum Based Enhanced Transformer For Efficient Language Modeling
Sharma, V., Shingade, S., & Udmale, S. S. (2026) · Evolving Systems · doi:10.1007/s12530-026-09890-2
Chancellor, N. (2017) · Durham Research Online (Durham University) · doi:10.15128/r1bn999672k
Hua, C.-Y., Kuo, S.-Y., Chou, Y.-H., & Kung, S.-Y. (2026) · IEEE Congress on Evolutionary Computation · doi:10.1109/cec69861.2026.11727377
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