t|ket⟩: a retargetable compiler for NISQ devices

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

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Quantum supremacy using a programmable superconducting processor

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A single quantum cannot be cloned

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A 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

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Quantum simulation

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

ArQTiC: A Full-stack Software Package for Simulating Materials on Quantum Computers

Oftelie, L. B., Powers, C., & de Jong, W. A. (2022) · ACM Transactions on Quantum Computing · doi:10.1145/3511715 · Cited by 34

Chemically aware unitary coupled cluster with ab initio calculations on an ion trap quantum computer: A refrigerant chemicals’ application

Khan, I. T., Tudorovskaya, M., Kirsopp, J. J. M., Ramo, D. M., Warrier, P. W., Papanastasiou, D. K., & Singh, R. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0144680 · Cited by 25

Influence of HW-SW-Co-Design on Quantum Computing Scalability

Safi, H., Wintersperger, K., & Mauerer, W. (2023) · IEEE International Conference on Quantum Software (QSW) · doi:10.1109/qsw59989.2023.00022 · Cited by 28

Simulating molecules on a cloud-based 5-qubit IBM-Q universal quantum computer

Leontica, S., Tennie, F., & Farrow, T. (2021) · Communications Physics · doi:10.1038/s42005-021-00616-1 · Cited by 27

Benchmarking the performance of quantum computing software for quantum circuit creation, manipulation and compilation

Nation, P. D., Saki, A. A., Brandhofer, S., Bello, L., Garion, S., Treinish, M., & Javadi-Abhari, A. (2025) · Nature Computational Science · doi:10.1038/s43588-025-00792-y · Cited by 22

Grammar-Aware Question-Answering on Quantum Computers.Preprint

Meichanetzidis, K., Toumi, A., de Felice, G., & Coecke, B. (2020) · arXiv (Cornell University) · Cited by 24

QAOA of the Highest Order

Campbell, C., & Dahl, E. D. (2022) · IEEE International Conference on Software Architecture Companion (ICSA-C) · doi:10.1109/icsa-c54293.2022.00035 · Cited by 21

Efficient variational synthesis of quantum circuits with coherent multi-start optimization

Nemkov, N. A., Kiktenko, E. O., Luchnikov, I. A., & Fedorov, A. K. (2023) · Quantum · doi:10.22331/q-2023-05-04-993 · Cited by 27

Reducing 2-QuBit Gate Count for ZX-Calculus based Quantum Circuit Optimization

Staudacher, K., Guggemos, T., Grundner-Culemann, S., & Gehrke, W. (2023) · Electronic Proceedings in Theoretical Computer Science · doi:10.4204/eptcs.394.3 · Cited by 22

Platinum-based catalysts for oxygen reduction reaction simulated with a quantum computer

Paola, C. D., Plekhanov, E. A., Krompiec, M., Kumar, C., Marsili, E., Du, F., Weber, D., Krauser, J. S., Shishenina, E., & Ramo, D. M. (2024) · npj Computational Materials · doi:10.1038/s41524-024-01460-x · Cited by 21

Advances in Quantum Computation and Quantum Technologies: A Design Automation Perspective

Micheli, G. D., Jiang, J.-H. R., Rand, R. W., Smith, K. N., & Soeken, M. (2022) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2022.3205174 · Cited by 23

A Tool For Debugging Quantum Circuits

Metwalli, S. A., & Meter, R. V. (2022) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce53715.2022.00085 · Cited by 24

OneQ: A Compilation Framework for Photonic One-Way Quantum Computation

Zhang, H., Wu, A., Wang, Y., Li, G., Shapourian, H., Shabani, A., & Ding, Y. (2023) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3579371.3589047 · Cited by 24

Error mitigation for quantum approximate optimization

Weidinger, A., Mbeng, G. B., & Lechner, W. (2023) · Physical Review A · doi:10.1103/physreva.108.032408 · Cited by 24

A Verified Optimizer for Quantum Circuits

Hietala, K., Rand, R., Li, L., Hung, S.-H., Wu, X., & Hicks, M. (2023) · ACM Transactions on Programming Languages and Systems · doi:10.1145/3604630 · Cited by 28

Quantum Circuit Transformation: A Monte Carlo Tree Search Framework

Zhou, X., Feng, Y., & Li, S. (2022) · ACM Transactions on Design Automation of Electronic Systems · doi:10.1145/3514239 · Cited by 49

Quantum Advantage Seeker with Kernels (QuASK): a software framework to speed up the research in quantum machine learning

Marcantonio, F. D., Incudini, M., Tezza, D., & Grossi, M. (2023) · Quantum Machine Intelligence · doi:10.1007/s42484-023-00107-2 · Cited by 26

isQ: An Integrated Software Stack for Quantum Programming

Guo, J., Lou, H., Yu, J., Li, R., Fang, W. S., Liu, J., Long, P., Ying, S., & Ying, M. (2023) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2023.3275868 · Cited by 19

Towards an Automated Framework for Realizing Quantum Computing Solutions

Quetschlich, N., Burgholzer, L., & Wille, R. (2023) · IEEE International Symposium on Multiple-Valued Logic (ISMVL) · doi:10.1109/ismvl57333.2023.00035 · Cited by 19

Testing and Debugging Quantum Circuits

Metwalli, S. A., & Meter, R. V. (2024) · IEEE Transactions on Quantum Engineering · doi:10.1109/tqe.2024.3374879 · Cited by 28

Automated Quantum Hardware Selection for Quantum Workflows

Weder, B., Barzen, J., Leymann, F., & Salm, M. (2021) · Electronics · doi:10.3390/electronics10080984 · Cited by 19

Reoptimization of Quantum Circuits via Hierarchical Synthesis

Wu, X.-C., Davis, M. G., Chong, F. T., & Iancu, C. (2021) · IEEE International Conference on Rebooting Computing (ICRC) · doi:10.1109/icrc53822.2021.00016 · Cited by 23

Robust Qubit Mapping Algorithm via Double-Source Optimal Routing on Large Quantum Circuits

Cheng, C.-Y., Yang, C.-Y., Kuo, Y.-H., Wang, R.-C., Cheng, H.-C., & Huang, C.-Y. (. (2024) · ACM Transactions on Quantum Computing · doi:10.1145/3680291 · Cited by 19

Quanto: optimizing quantum circuits with automatic generation of circuit identities

Pointing, J., Padon, O., Jia, Z., Ma, H., Hirth, A., Palsberg, J., & Aiken, A. (2024) · Quantum Science and Technology · doi:10.1088/2058-9565/ad5b16 · Cited by 18

SpinQ: Compilation Strategies for Scalable Spin-Qubit Architectures

Paraskevopoulos, N., Sebastiano, F., Almudéver, C. G., & Feld, S. (2023) · ACM Transactions on Quantum Computing · doi:10.1145/3624484 · Cited by 21

Solving linear systems on quantum hardware with hybrid HHL++

Yalovetzky, R., Minssen, P., Herman, D., & Pistoia, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-69077-0 · Cited by 27

Parity Quantum Optimization: Benchmarks

FELLNER, M. J., Ender, K., ter Hoeven, R., & Lechner, W. (2023) · Quantum · doi:10.22331/q-2023-03-17-952 · Cited by 25

Domain-Specific Quantum Architecture Optimization

Lin, W.-H., Tan, D. B., Niu, M. Y., Kimko, J., & Cong, J. (2022) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2022.3202870 · Cited by 20

A SAT Approach to the Initial Mapping Problem in SWAP Gate Insertion for Commuting Gates

Matsuo, A., Yamashita, S., & Egger, D. J. (2023) · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences · doi:10.1587/transfun.2022eap1159 · Cited by 22

Challenges and Opportunities in Quantum Software Architecture

Yue, T., Mauerer, W., Ali, S., & Taibi, D. (2023) · Springer eBooks · doi:10.1007/978-3-031-36847-9_1 · Cited by 17

Diagrammatic Differentiation for Quantum Machine Learning

Toumi, A., Yeung, R. W. K., & de Felice, G. (2021) · Electronic Proceedings in Theoretical Computer Science · doi:10.4204/eptcs.343.7 · Cited by 17

QProv: A provenance system for quantum computing

Weder, B., Barzen, J., Leymann, F., Salm, M., & Wild, K. (2021) · IET Quantum Communication · doi:10.1049/qtc2.12012 · Cited by 22

Simulations of Quantum Circuits with Approximate Noise using qsim and CirqPreprint

Isakov, S. V., Kafri, D., Martin, O., Heidweiller, C. V., Mruczkiewicz, W., Harrigan, M. P., Rubin, N. C., Thomson, R., Broughton, M., Kissell, K., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.02396 · Cited by 19

Wide Quantum Circuit Optimization with Topology Aware Synthesis

Weiden, M., Kalloor, J., Kubiatowicz, J. D., Younis, E., & Iancu, C. (2022) · IEEE/ACM International Workshop on Quantum Computing Software (QCS) · doi:10.1109/qcs56647.2022.00006 · Cited by 24

Noise Adaptive Quantum Circuit Mapping Using Reinforcement Learning and Graph Neural Network

Saravanan, V., & Saeed, S. M. (2023) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2023.3340608 · Cited by 17

Qutrit and qubit circuits for three-flavor collective neutrino oscillations

Turro, F., Chernyshev, I. A., Bhaskar, R., & Illa, M. (2025) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.111.043038 · Cited by 16

Reinforcement Learning and DEAR Framework for Solving the Qubit Mapping Problem

Huang, C., Lien, C.-H., & Mak, W.-K. (2022) · International Conference on Computer Aided Design · doi:10.1145/3508352.3549472 · Cited by 16

A compiler for universal photonic quantum computers

Zilk, F., Staudacher, K., Guggemos, T., Fürlinger, K., Kranzlmüller, D. A., & Walther, P. J. (2022) · IEEE/ACM International Workshop on Quantum Computing Software (QCS) · doi:10.1109/qcs56647.2022.00012 · Cited by 20

Practical randomness amplification and privatisation with implementations on quantum computers

Foreman, C., Wright, S., Edgington, A., Berta, M., & Curchod, F. J. (2023) · Quantum · doi:10.22331/q-2023-03-30-969 · Cited by 20

Volumetric Benchmarking of Error Mitigation with Qermit

Cîrstoiu, C., Dilkes, S., Mills, D., Sivarajah, S., & Duncan, R. (2023) · Quantum · doi:10.22331/q-2023-07-13-1059 · Cited by 16

PCOAST: A Pauli-Based Quantum Circuit Optimization Framework

Paykin, J., Schmitz, A. T., Ibrahim, M. M. A., Wu, X.-C., & Matsuura, A. Y. (2023) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce57702.2023.00087 · Cited by 15

Probing Critical States of Matter on a Digital Quantum Computer

Haghshenas, R., Chertkov, E., DeCross, M. P., Gatterman, T. M., Gerber, J. A., Gilmore, K., Gresh, D., Hewitt, N., Horst, C. V., Matheny, M., et al. (2024) · Physical Review Letters · doi:10.1103/physrevlett.133.266502 · Cited by 23

On Decision Support for Quantum Application Developers: Categorization, Comparison, and Analysis of Existing Technologies

Vietz, D., Barzen, J., Leymann, F., & Wild, K. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77980-1_10 · Cited by 15

Kindergarden quantum mechanics graduates ...or how I learned to stop gluing LEGO together and love the ZX-calculus

Coecke, B., Horsman, D. C., Kissinger, A., & Wang, Q. (2021) · Theoretical Computer Science · doi:10.1016/j.tcs.2021.07.024 · Cited by 15

Prioritization of Compiled Quantum Circuits for Different Quantum Computers

Salm, M., Barzen, J., Leymann, F., & Weder, B. (2022) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner53432.2022.00150 · Cited by 15

High-Round QAOA for MAX $k$-SAT on Trapped Ion NISQ Devices

Pelofske, E., Bärtschi, A., Golden, J., & Eidenbenz, S. (2023) · 2023 IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce57702.2023.00064 · Cited by 15

Practical application of quantum neural network to materials informatics

Hirai, H. (2024) · Scientific Reports · doi:10.1038/s41598-024-59276-0 · Cited by 18

Optimizing quantum algorithms on bipotent architectures

Ji, Y., Koenig, K. F., & Polian, I. (2023) · Physical Review A · doi:10.1103/physreva.108.022610 · Cited by 14

Highly optimized quantum circuits synthesized via data-flow engines

Rakyta, P., Morse, G., Nádori, J., Majnay-Takács, Z., Mencer, O., & Zimborás, Z. (2024) · Journal of Computational Physics · doi:10.1016/j.jcp.2024.112756 · Cited by 13

Non-unitary Trotter circuits for imaginary time evolution

Leadbeater, C., Fitzpatrick, N., Ramo, D. M., & Thom, A. J. W. (2024) · Quantum Science and Technology · doi:10.1088/2058-9565/ad53fb · Cited by 19

Performance of quantum approximate optimization with quantum error detection

He, Z., Amaro, D., Shaydulin, R., & Pistoia, M. (2025) · Communications Physics · doi:10.1038/s42005-025-02136-8 · Cited by 33

Quantum state preparation for multivariate functions

Rosenkranz, M., Brunner, E., Marin-Sanchez, G., Fitzpatrick, N., Dilkes, S., Tang, Y., Kikuchi, Y., & Benedetti, M. (2025) · Quantum · doi:10.22331/q-2025-04-09-1703 · Cited by 25

Hardware-aware Toffoli gate decomposition via echoed cross-resonance gates

AbuGhanem, M. (2025) · Quantum Studies Mathematics and Foundations · doi:10.1007/s40509-025-00369-4 · Cited by 18

Simulation of Quantum Computers: Review and Acceleration Opportunities

Cicero, A., Maleki, M. A., Azhar, M. W., Kockum, A. F., & Trancoso, P. (2025) · ACM Transactions on Quantum Computing · doi:10.1145/3762672 · Cited by 26

About a criterion of successfully executing a circuit in the NISQ era: what wd ≪ 1/𝜖 eff really means

Salm, M., Barzen, J., Leymann, F., & Weder, B. (2020) · doi:10.1145/3412451.3428498 · Cited by 14

Graph optimization perspective for low-depth Trotter-Suzuki decomposition

Schmitz, A. T., Sawaya, N. P. D., Johri, S., & Matsuura, A. Y. (2024) · Physical Review A · doi:10.1103/physreva.109.042418 · Cited by 28

Supervised Learning Enhanced Quantum Circuit Transformation

Zhou, X., Feng, Y., & Li, S. (2022) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2022.3179223 · Cited by 14

Tierkreis: a Dataflow Framework for Hybrid Quantum-Classical Computing

Sivarajah, S., Heidemann, L., Lawrence, A. J., & Duncan, R. (2022) · IEEE/ACM International Workshop on Quantum Computing Software (QCS) · doi:10.1109/qcs56647.2022.00007 · Cited by 12

Quantum computing simulation of a mixed spin-boson Hamiltonian and its performance for a cavity quantum electrodynamics problem

Tudorovskaya, M., & Ramo, D. M. (2024) · Physical Review A · doi:10.1103/physreva.109.032612 · Cited by 16

Efficient Qubit Routing Using a Dynamically Extract-and-Route Framework

Huang, C.-Y., & Mak, W.-K. (2024) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2024.3387290 · Cited by 15

MECH: Multi-Entry Communication Highway for Superconducting Quantum Chiplets

Zhang, H., Yin, K., Wu, A., Shapourian, H., Shabani, A., & Ding, Y. (2024) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3620665.3640377 · Cited by 24

Algorithm-oriented qubit mapping for variational quantum algorithms

Ji, Y., Chen, X., Polian, I., & Ban, Y. (2025) · Physical Review Applied · doi:10.1103/physrevapplied.23.034022 · Cited by 19

Optimizing Gate Decomposition for High-Level Quantum Programming

Rosa, E. C. R., Duzzioni, E. I., & de Santiago, R. (2025) · Quantum · doi:10.22331/q-2025-03-12-1659 · Cited by 18

Excited-state molecular dynamics simulation based on variational quantum algorithms

Hirai, H. (2023) · Chemical Physics Letters · doi:10.1016/j.cplett.2023.140404 · Cited by 12

Annealing Optimisation of Mixed ZX Phase Circuits

Gogioso, S., & Yeung, R. W. K. (2023) · Electronic Proceedings in Theoretical Computer Science · doi:10.4204/eptcs.394.20 · Cited by 12

Quantum Natural Language Generation on Near-Term Devices

Karamlou, A., Wootton, J. R., & Pfaffhauser, M. (2022) · International Conference on Natural Language Generation · doi:10.18653/v1/2022.inlg-main.22 · Cited by 19

Quantifying T -gate-count improvements for ground-state-energy estimation with near-optimal state preparation

Pathak, S., Russo, A., Seritan, S., & Baczewski, A. (2023) · Physical Review A · doi:10.1103/physreva.107.l040601 · Cited by 12

QFactor: A Domain-Specific Optimizer for Quantum Circuit Instantiation

Kukliansky, A., Younis, E., Cincio, Ł., & Iancu, C. (2023) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce57702.2023.00096 · Cited by 11

Tetris: A Compilation Framework for VQA Applications in Quantum Computing

Jin, Y., Li, Z., Hua, F., Hao, T., Zhou, H., Huang, Y., & Zhang, E. Z. (2024) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1109/isca59077.2024.00029 · Cited by 21

QDMI - Quantum Device Management Interface: Hardware-Software Interface for the Munich Quantum Software Stack

Wille, R., Schmid, L., Stade, Y., Echavarria, J., Schulz, M., Schulz, L. B., & Burgholzer, L. (2024) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce60285.2024.10411 · Cited by 20

Floquet evolution of the q -deformed SU ( 3 ) 1 Yang-Mills theory on a two-leg ladder

Hayata, T., & Hidaka, Y. (2025) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.111.034513 · Cited by 11

Relativistic variational-quantum-eigensolver calculations of molecular electric dipole moments on quantum hardware

Chawla, P., Shweta, Swain, K. R., Patel, T., Bala, R., Shetty, D., Sugisaki, K., Mandal, S. B., Riu, J., Nogué, J., et al. (2025) · Physical Review A · doi:10.1103/physreva.111.022817 · Cited by 14

F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits

Leadbeater, C., Sharrock, L., Coyle, B., & Benedetti, M. (2021) · Entropy · doi:10.3390/e23101281 · Cited by 14

Handling Non-unitaries in Quantum Circuit Equivalence Checking

Burgholzer, L., & Wille, R. (2022) · Springer eBooks · doi:10.1007/978-3-032-06770-8_18 · Cited by 16

QAOAwith $N\cdot p\geq 200$

Shaydulin, R., & Pistoia, M. (2023) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce57702.2023.00121 · Cited by 16

Differentiating and Integrating ZX Diagrams with Applications to Quantum Machine Learning

Wang, Q., Yeung, R. W. K., & Koch, M. E. (2024) · Quantum · doi:10.22331/q-2024-10-04-1491 · Cited by 11

The T-Complexity Costs of Error Correction for Control Flow in Quantum Computation

Yuan, C., & Carbin, M. (2024) · Proceedings of the ACM on Programming Languages · doi:10.1145/3656397 · Cited by 10

Quantum Computed Green's Functions using a Cumulant Expansion of the Lanczos Method

Greene‐Diniz, G., Manrique, D. Z., Yamamoto, K., Plekhanov, E. A., Fitzpatrick, N., Krompiec, M., Sakuma, R., & Ramo, D. M. (2024) · Quantum · doi:10.22331/q-2024-06-20-1383 · Cited by 12

QFlowS: Quantum simulator for fluid flows

Bharadwaj, S. S. (2024) · Physics of Fluids · doi:10.1063/5.0226074 · Cited by 11

Floquet prethermalization of Z 2 lattice gauge theory on superconducting qubits

Hayata, T., Seki, K., & Yamamoto, A. (2024) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.110.114503 · Cited by 10

SCIM MILQ: An HPC Quantum Scheduler

Seitz, P., Geiger, M., Ufrecht, C., Plinge, A., Mutschler, C., Scherer, D. D., & Mendl, C. B. (2024) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce60285.2024.10294 · Cited by 10

Calculating the Energy Profile of an Enzymatic Reaction on a Quantum Computer

Ettenhuber, P., Hansen, M. B., Poier, P. P., Shaik, I., Rasmussen, S. E., Madsen, N. K., Majland, M., Jensen, F., Olsen, L., & Zinner, N. T. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c00022 · Cited by 15

E-LoQ: Enhanced Locking for Quantum Circuit IP Protection

Liu, Y., John, J., & Wang, Q. (2025) · IEEE International Symposium on Hardware Oriented Security and Trust (HOST) · doi:10.1109/host64725.2025.11050014 · Cited by 14

Quantum Gate Pattern Recognition and Circuit Optimization for Scientific Applications

Jang, W., Terashi, K., Saito, M., Bauer, C., Nachman, B. P., Iiyama, Y., Kishimoto, T. E., Okubo, R., Sawada, R., & Tanaka, J. (2021) · EPJ Web of Conferences · doi:10.1051/epjconf/202125103023 · Cited by 9

Quafu-Qcover: Explore combinatorial optimization problems on cloud-based quantum computers

Xu, H.-Z., Zhuang, W.-F., 王, Z.-A. 正. W., Huang, K., 时, Y.-H. 运. S., 马, W.-G. 卫. M., 李, T.-M. 天. L., Chen, C.-T., 许, K. 凯. X., 冯, Y.-L. 玉. F., et al. (2023) · Chinese Physics B · doi:10.1088/1674-1056/ad18ab · Cited by 18

Circuit Decompositions and Scheduling for Neutral Atom Devices with Limited Local Addressability

Nottingham, N., Perlin, M. A., Shah, D., White, R., Bernien, H., Chong, F. T., & Baker, J. M. (2024) · IEEE International Conference on Quantum Computing and Engineering (QCE) · doi:10.1109/qce60285.2024.00105 · Cited by 16

Graph decomposition techniques for solving combinatorial optimization problems with variational quantum algorithms

Ponce, M., Herrman, R., Lotshaw, P. C., Powers, S., Siopsis, G., Humble, T. S., & Ostrowski, J. (2025) · Quantum Information Processing · doi:10.1007/s11128-025-04675-z · Cited by 30

Fast simulation of quantum algorithms using circuit optimization

Guerreschi, G. G. (2022) · Quantum · doi:10.22331/q-2022-05-03-706 · Cited by 10

Deterministic algorithms for compiling quantum circuits with recurrent patterns

Ferrari, D., Tavernelli, I., & Amoretti, M. (2021) · Quantum Information Processing · doi:10.1007/s11128-021-03150-9 · Cited by 11

Bayesian learning of parameterised quantum circuits

Duffield, S., Benedetti, M., & Rosenkranz, M. (2023) · Machine Learning Science and Technology · doi:10.1088/2632-2153/acc8b7 · Cited by 17

QGo: Scalable Quantum Circuit Optimization Using Automated SynthesisPreprint

Wu, X.-C., Davis, M. G., Chong, F. T., & Iancu, C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.09835 · Cited by 52

NISQ-HHL: Portfolio Optimization for Near-Term Quantum HardwarePreprint

Yalovetzky, R., Minssen, P., Herman, D., & Pistoia, M. (2021) · arXiv (Cornell University) · Cited by 11

Position Papers for the ASCR Workshop on Basic Research Needs in Quantum Computing and Networking

Lougovski, P., Parekh, O., Broz, J., Byrd, M., Chembo, Y. K., de Jong, W. A., Figueroa, E. V., Humble, T. S., Larson, J. M., Quiroz, G., et al. (2023) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.2172/2283320 · Cited by 10

Quantum Constant Propagation

Chen, Y., & Stade, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44245-2_9 · Cited by 10

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Classical Data in Quantum Machine Learning Algorithms: Amplitude Encoding and the Relation Between Entropy and Linguistic Ambiguity

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Quantum Computing Applications in Future Colliders

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Minimizing Deployment Cost of Hybrid Applications

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Compositionality as We See It, Everywhere Around Us

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An open-source, industrial-strength optimizing compiler for quantum programs

Smith, R., Peterson, E., Skilbeck, M. G., & Davis, E. (2020) · Quantum Science and Technology · doi:10.1088/2058-9565/ab9acb · Cited by 42

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Southall, L., Ammermann, J., Kelmendi, R., Eichhorn, D., & Schaefer, I. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2601.16779

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Tough Errors Are no Match (TEAM): Optimizing the quantum compiler for noise resilience

Shammah, N., CHO Chicago Office, A., Stemen, N. T., Mari, A., Russo, V., Wahl, M., & Zeng, W. J. (2025) · doi:10.2172/2569159