Machine Learning for Quantum Circuit Synthesis and Optimization: A Paper List

We would like to maintain a list of resources that utilize machine learning approaches to solve quantum circuit problems.

Works contributed by ReThinklab are marked with ⭐.

This repo is an extension of the survey paper Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospects, Ge Yan, Wenjie Wu, Yuheng Chen, Kaisen Pan, Xudong Lu, Zixiang Zhou, Yuhan Wang, Ruocheng Wang, Junchi Yan, arXiv:2407.00736

Content

• I. Quantum Circuit Representation
  • Quantum Gate Model
  • Directed Acyclic Graph
  • Circuit Polynomial
  • Tensor Networks
  • ZX Diagrams
• II. Quantum Logic Circuit Synthesis
  • Applications
  • Methods
• III.Quantum Logic Circuit Optimization
  • Optimization Targets
  • Optimization Methods
  • Circuit Optimization for VQAs
• IV. Qubit Mapping and Routing
  • Targets
  • Methods

I. Quantum Circuit Representation

A. Quantum Gate Model

• [Quantum Computation and Quantum Information: 10th Anniversary Edition] M. A. Nielsen and 1. L. Chuang., (Cambridge University Press 2010).
• [Exact synthesis of single-qubit unitaries over clifford-cyclotomic gate sets] S. Forest, D. Gosset, V. Kliuchnikov, and D. McKinnon., (Journal of Mathematical Physics 2015).
• [Stabilizer codes and quantum error correction] D. Gottesman., (California Institute of Technology 1997).
• [Theory of fault-tolerant quantum computation] D. Gottesman., (Physical Review A 1998).
• [The heisenberg representation of quantum computers] D. Gottesman., (arXiv preprint quant-ph/9807006 1998).
• [Quantum circuit simplification and level compaction] D. Maslov, G. W. Dueck. D. M. Miller, and C. Negrevergne., (IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2008).
• [Elementary quantum gate realizations for multiple-control toffoli gates] D. M. Miller, R. Wille, and Z. Sasanian., (International symposium on multiple-valued logic 2011).
• [Simplification of toffoli networks via templates] D. Maslov, G. W. Dueck, and D. M. Miller., (Symposium on Integrated Circuits and Systems Design 2003).
• [Quantum circuit simplification using templates] D. Maslov, C. Young, D. M. Miller, and G. W. Dueck., (Design, Automation and Test in Europe 2005).
• [Observing ground-state properties of the fermi-hubbard model using a scalable algorithm on a quantum computer] S. Stanisic, J. L. Bosse, F. M. Gambetta, R. A. Santos, W. Mruczkiewicz, T. E. O'Brien, E. Ostby, and A. Montanaro., (Nature communications 2022).

B. Directed Acyclic Graph

• [Gsqas: graph self-supervised quantum architecture search] Z. He, M. Deng. S. Zheng, L. Li, and H. Situ., (Physica A: Statistical Mechanics and its Applications 2023).
• [A gnn-based predictor for quantum architecture search] Z. He, X. Zhang, C. Chen, Z. Huang, Y. Zhou, and H. Situ., (Quantum Information Processing 2023).
• [Tackling the qubit mapping problem for nisq-era quantum devices] G. Li Y. Ding, and Y. Xie., (International Conference on Architectural Support for Programming Languages and Operating Systems 2019).
• [Altgraph: Redesigning quantum circuits using generative graph models for efficient optimization] C. Beaudoin, K. Phalak, and S. Ghosh., (Proceedings of the Great Lakes Symposium on VLSI 2024 2024).
• [Qmdds: Efficient quantum function representation and manipulation] P. Niemann, R. Wille, D. M. Miller, M. A. Thornton, and R. Drechsler., (IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2015).

C. Circuit Polynomial

• [On the controlled-not complexity of controlled-not-phase circuits] M. Amy, P. Azimzadeh, and M. Mosca., (Quantum Science and Technology 2018).
• [Phase polynomials synthesis algorithms for nisq architectures and beyond] V. Vandaele, S. Martiel, and T. G. de Brugière., (Quantum Science and Technology 2022).
• [A meet-in-the-middle algorithm for fast synthesis of depth-optimal quantum circuits] M. Amy, D. Maslov, M. Mosca, and M. Roetteler., (IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2013).
• [Automated optimization of large quantum circuits with continuous parameters] Y. Nam, N. J. Ross, Y. Su, A. M. Childs, and D. Maslov., (npj Quantum Information 2018).
• [Sat-based {CNOT, T} quantum circuit synthesis] G. Meuli, M. Soeken, and G. De Micheli., (Reversible Computation: 10th International Conference, RC 2018, Leicester, UK, September 12-14, 2018, Proceedings 10 2018).
• [Quantum circuit optimization with alphatensor] F. J. Ruiz, T. Laakkonen, J. Bausch, M. Balog. et al., (Nature Machine Intelligence 2025).

D. Tensor Networks

• [Quantum theory, the church-turing principle and the universal quantum computer] D. Deutsch., (A. Mathematical and Physical Sciences 1985).
• [Tensor networks in a nutshell] J. Biamonte and V. Bergholm., (arXiv preprint arXiv:1708.00006 2017).
• [Applications of negative dimensional tensors] R. Penrose., (Combinatorial mathematics and its applications 1971).
• [A practical introduction to tensor networks: Matrix product states and projected entangled pair states] R. Orús., (Annals of physics 2014).
• [Simulating quantum computation by contracting tensor networks] 1. L. Markov and Y. Shi., (SIAM Journal on Computing 2008).
• [On optimizing a class of multi-dimensional loops with reduction for parallel execution] L. Chi-Chung, P. Sadayappan, and R. Wenger., (Parallel Processing Letters 1997).
• [Simple heuristics for efficient parallel tensor contraction and quantum circuit simulation] R. Schutski, T. Khakhulin, 1. Oseledets, and D. Kolmakov., (Physical Review A 2020).
• [Efficient parallelization of tensor network contraction for simulating quantum computation] C. Huang, F. Zhang, M. Newman, X. Ni, D. Ding, J. Cai. X. Gao, T. Wang. F. Wu. et al., (Nature Computational Science 2021).
• [Jet: Fast quantum circuit simulations with parallel task-based tensor-network contraction] T. Vincent, L. J. O'Riordan, M. Andrenkov, J. Brown, N. Killoran, H. Qi, and I. Dhand., (Quantum 2022).
• [An efficient tensor transpose algorithm for multicore cpu, intel xeon phi, and nvidia tesla gpu] D. 1. Lyakh., (Computer Physics Communications 2015).
• [Towards quantum machine learning with tensor networks] W. Huggins, P. Patil, B. Mitchell, K. B. Whaley, and E. M. Stoudenmire., (Quantum Science and technology 2019).
• [Variational power of quantum circuit tensor networks] R. Haghshenas, J. Gray, A. C. Potter, and G. K.-L., Chan., (Physical Review X 2022).

E. ZX Diagrams

• [Interacting quantum observables] B. Coecke and R. Duncan., (International Colloquium on Automata, Languages, and Programming 2008).
• [PyZX: Large scale automated diagrammatic reasoning] A. Kissinger and J. van de Wetering., (Electronic Proceedings in Theoretical Computer Science 2020).
• [Diagrammatic Design and Study of Ansätze for Quantum Machine Learning] R. Yeung., (arXiv preprint arXiv:2011.11073 2020).
• [An efficient quantum compiler that reduces T count] L. E. Heyfron and E. T. Campbell., (Quantum Science and Technology 2018).
• [Graph-theoretic simplification of quantum circuits with the ZX-calculus] R. Duncan, A. Kissinger, S. Perdrix, and J. van de Wetering., (Quantum 2020).
• [Optimising clifford circuits with quantomatic] A. Fagan and R. Duncan., (Electronic Proceedings in Theoretical Computer Science 2019).
• [AND-gates in ZX-calculus: Spider nest identities and QBC-completeness] A. Munson, B. Coecke, and Q. Wang., (Electronic Proceedings in Theoretical Computer Science 2021).
• [Constructing quantum circuits with global gates] J. van de Wetering., (New Journal of Physics 2021).
• [Techniques to reduce /4-parity-phase circuits, motivated by the ZX calculus] N. de Beaudrap. X. Bian, and Q. Wang., (Electronic Proceedings in Theoretical Computer Science 2020).
• [Reducing the number of non-clifford gates in quantum circuits] A. Kissinger and J. van de Wetering., (Physical Review A 2020).
• [Annealing optimisation of mixed zx phase circuits] S. Gogioso and R. Yeung., (arXiv preprint arXiv:2206.11839 2022).
• [Cnot circuit extraction for topologically constrained quantum memories] A. Kissinger and A. M.-v. de Griend., (arXiv preprint arXiv:1904.00633 2019).

III. Quantum Logic Circuit Synthesis

A. Applications of Quantum Architecture Search

• [A fast quantum mechanical algorithm for database search] L. K. Grover., (ACM symposium on Theory of computing 1996).
• [Quantum Computation and Quantum Information: 10th Anniversary Edition] M. A. Nielsen and 1. L. Chuang., (Cambridge University Press 2010).
• [Implementing grover oracle for lightweight block ciphers under depth constraints] S. Bijwe, A. K. Chauhan, and S. K. Sanadhya., (Australasian Conference on Information Security and Privacy 2022).
• [Grover on katan: Quantum resource estimation] M. Rahman and G. Paul., (IEEE Transactions on Quantum Engineering 2022).
• [Numerical analysis of quantum circuits for state preparation and unitary operator synthesis] S. Ashhab, N. Yamamoto, F. Yoshihara, and K. Semba., (Physical Review A 2022).
• [Quantum circuit synthesis via a random combinatorial search] S. Ashhab, F. Yoshihara, M. Tsuji, M. Sato, and K. Semba., (Physical Review A 2024).
• [Evolving quantum oracles with hybrid quantum-inspired evolutionary algorithm] S. Ding. Z. Jin, and Q. Yang., (arXiv preprint quant-ph/0610105 2006).
• [Approximate quantum adders with genetic algorithms: an ibm quantum experience] R. Li, U. Alvarez-Rodriguez, L. Lamata, and E. Solano., (Quantum Measurements and Quantum Metrology 2017).
• [Design of a ternary reversible/quantum adder using genetic algorithm] V. G. Deibuk and A. V. Biloshytskyi., (International Journal of Information Technology and Computer Science 2015).
• [Quantum autoencoders via quantum adders with genetic algorithms] L. Lamata, U. Alvarez-Rodriguez, J. D. Martin-Guerrero, M. Sanz, and E. Solano., (Quantum Science and Technology 2018).
• [A variational eigenvalue solver on a photonic quantum processor] A. Peruzzo, J. McClean, P. Shadbolt, M.-H. Yung, X.-Q. et al., (Nature communications 2014).
• [The method of second quantization, vol. 24] F. Berazin., (Elsevier 2012).
• [Über das paulische äquivalenzverbot] P. Jordan and E. Wigner., (Zeitschrift für Physik 1928).
• [New perspectives on unitary coupled-cluster theory] A. G. Taube and R. J. Bartlett., (International journal of quantum chemistry 2006).
• [Quantum algorithms for electronic structure calculations: Particle-hole hamiltonian and optimized wave-function expansions] P. K. Barkoutsos, J. F. Gonthier. 1. Sokolov, N. Moll, G. Salis, A. Fuhrer, et al., (Physical Review A 2018).
• [An adaptive variational algorithm for exact molecular simulations on a quantum computer] H. R. Grimsley, S. E. Economou, E. Barnes, and N. J. Mayhall., (Nature communications 2019).
• [Variational quantum eigensolver techniques for simulating carbon monoxide oxidation] M. D. Sapova and A. K. Fedorov., (Communications Physics 2022).
• [Reinforcement learning for optimization of variational quantum circuit architectures] M. Ostaszewski, L. M. Trenkwalder, W. Masarczyk, E. Scerri, and V. Dunjko., (NeurIPS 2021).
• [Quantumnas: Noise-adaptive search for robust quantum circuits] H. Wang, Y. Ding, J. Gu, Y. Lin, D. Z. Pan, F. T. Chong, and S. Han., (HPCA 2022).
• ⭐ [Quantumdarts: differentiable quantum architecture search for variational quantum algorithms] W. Wu. G. Yan, X. Lu, K. Pan, and J. Yan., (ICML 2023).
• [A quantum approximate optimization algorithm] E. Farhi, J. Goldstone, and S. Gutmann., (arXiv preprint arXiv: 1411,4028 2014).
• [Depth optimized ansatz circuit in quoa for max-cut] R. Majumdar, D. Bhoumik, D. Madan, D. Vinayagamurthy, S. Raghunathan, and S. Sur-Kolay., (arXiv preprint arXiv:2110.04637 2021).
• [Optimizing ansatz design in qaoa for max-cut] R. Majumdar, D. Madan, D. Bhoumik, D. Vinayagamurthy. S. Raghunathan, and S. Sur-Kolay., (arXiv preprint arXiv:2106.02812 2021).
• [Differentiable quantum architecture search] S. Zhang, C. Hsieh, S. Zhang, and H. Yao., (Quantum Science and Technology 2022).
• [Deep learning] Y. LeCun, Y. Bengio, and G. Hinton., (nature 2015).
• [Recent advances in convolutional neural networks] J. Gu. Z. Wang, J. Kuen, L. Ma, A. Shahroudy. et al., (Pattern recognition 2018).
• [Quantum convolutional neural networks] 1. Cong. S. Choi, and M. D. Lukin., (Nature Physics 2019).
• [Recurrent quantum neural networks] J. Bausch., (Advances in neural information processing systems 2020).
• [Quantum advantage in learning from experiments] H.-Y. Huang. M. Broughton, J. Cotler, S. Chen, J. Li, M. Mohseni, H. Neven, R. Babbush, R. Kueng, J. Preskill, et al., (Science 2022).
• [Neural predictor based quantum architecture search] S.-X. Zhang, C.-Y. Hsieh, S. Zhang, and H. Yao., (Machine Learning: Science and Technology 2021).
• [Quantum neural architecture search with quantum circuits metric and bayesian optimization] T. Duong, S. T. Truong, M. Tam, B. Bach, J.-Y. Ryu, and J.-K. K. Rhee., (arXiv preprint arXiv:2206.14115 2022).
• [Bosonic quantum error correction codes in superconducting quantum circuits] W. Cai, Y. Ma, W. Wang, C.-L. Zou, and L. Sun., (Fundamental Research 2021).
• [Non-linear transformations of quantum amplitudes: Exponential improvement, generalization, and applications] A. G. Rattew and P. Rebentrost., (arXiv preprint arXiv:2309.09839 2023).
• [Nonlinear transformation of complex amplitudes via quantum singular value transformation] N. Guo, K. Mitarai, and K. Fujii., (Physical Review Research 2024).
• [The quest for a quantum neural network] M. Schuld. I. Sinayskiy, and F. Petruccione., (Quantum Information Processing 2014).
• [Heuristics in quantum error correction] A. Rigby., (University of Tasmania 2021).
• [Machine learning logical gates for quantum error correction] H. Chen, M. Vasmer, N. P. Breuckmann, and E. Grant., (arXiv preprint arXiv: 1912.10063 2019).
• [Optimizing quantum error correction codes with reinforcement learning] H. P. Nautrup. N. Delfosse. V. Dunjko, H. J. Briegel, and N. Friis., (Quantum 2019).
• [Approximate autonomous quantum error correction with reinforcement learning] Y. Zeng, Z.-Y. Zhou, E. Rinaldi, C. Gneiting, and F. Nori., (Physical Review Letters 2023).

B. Quantum Architecture Search Methods

• [Automated design of quantum circuits] C. P. Williams and A. G. Gray., (NASA International Conference on Quantum Computing and Quantum Communications 1999).
• [Evolving quantum circuits and programs through genetic programming] P. Massey, J. A. Clark, and S. Stepney., (Genetic and Evolutionary Computation Conference 2004).
• [A genetic-algorithm-based method to find unitary transformations for any desired quantum computation and application to a one-bit oracle decision problem] J. Bang and S. Yoo., (Journal of the Korean Physical Society 2014).
• [Genetic algorithms for digital quantum simulations] U. Las Heras, U. Alvarez-Rodriguez, E. Solano, and M. Sanz., (Physical review letters 2016).
• [Quantum autoencoders via quantum adders with genetic algorithms] L. Lamata, U. Alvarez-Rodriguez, J. D. Martin-Guerrero, M. Sanz, and E. Solano., (Quantum Science and Technology 2018).
• [Multi-objective evolutionary algorithms for quantum circuit discovery] V. Potoček, A. P. Reynolds, A. Fedrizzi, and D. W. Corne., (arXiv preprint arXiv: 1812.04458 2018).
• [Quantum-assisted quantum compiling] S. Khatri, R. LaRose, A. Poremba, L. Cincio, A. T. Sornborger, and P. J. Coles., (Quantum 2019).
• [Quantum architecture search via deep reinforcement learning] E.-J. Kuo, Y.-L., L. Fang, and S. Y.-C. Chen., (arXiv preprint arXiv: 2104.07715 2021).
• [Quantum architecture search via continual reinforcement learning] E. Ye and S. Y.-C. Chen., (arXiv preprint arXiv:2112.05779 2021).
• [Reinforcement learning for optimization of variational quantum circuit architectures] M. Ostaszewski, L. M. Trenkwalder, W. Masarczyk, E. Scerri, and V. Dunjko., (NeurIPS 2021).
• [Curriculum reinforcement learning for quantum architecture search under hardware errors] Y. J. Patel, A. Kundu, M. Ostaszewski, X. Bonet-Monroig. et al., (arXiv preprint arXiv:2402.03500 2024).
• [Enhancing variational quantum state diagonalization using reinforcement learning techniques] A. Kundu, P. Bedełek, M. Ostaszewski, O. Danaci, Y. J. Patel, V. Dunjko, and J. A. Miszczak., (New Journal of Physics 2024).
• [A quantum information theoretic analysis of reinforcement learning-assisted quantum architecture search] A. Sadhu, A. Sarkar, and A. Kundu., (Quantum Machine Intelligence 2024).
• [Kanqas: Kolmogorov-arnold network for quantum architecture search] A. Kundu, A. Sarkar, and A. Sadhu., (EPJ Quantum Technology 2024).
• [Differentiable quantum architecture search] S. Zhang, C. Hsieh, S. Zhang, and H. Yao., (Quantum Science and Technology 2022).
• [Quantum circuit architecture search for variational quantum algorithms] Y. Du, T. Huang. S. You, M.-H. Hsieh, and D. Tao., (npj Quantum Information 2022).
• [Quantumnas: Noise-adaptive search for robust quantum circuits] H. Wang, Y. Ding, J. Gu, Y. Lin, D. Z. Pan, F. T. Chong, and S. Han., (HPCA 2022).
• ⭐ [Qas-bench: rethinking quantum architecture search and a benchmark] X. Lu, K. Pan, G. Yan, J. Shan, W. Wu, and J. Yan., (ICML 2023).
• ⭐ [Quantumdarts: differentiable quantum architecture search for variational quantum algorithms] W. Wu. G. Yan, X. Lu, K. Pan, and J. Yan., (ICML 2023).
• [Statistical theory of extreme values and some practical applications] E. Gumbel., (NBS Applied Mathematics Series 1954).
• [Estimating or propagating gradients through stochastic neurons for conditional computation] Y. Bengio, N. Leonard, and A. Courville., (arXiv preprint arXiv: 1308.3432 2013).
• [Categorical reparameterization with gumbel-softmax] E. Jang, S. Gu, and B. Poole., (ICLR 2017).
• [Deep unsupervised learning using nonequilibrium thermodynamics] J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli., (ICML 2015).
• [High-resolution image synthesis with latent diffusion models] R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer., (Proceedings of the IEEE/CVF conference on computer vision and pattern recognition 2022).
• [Quantum circuit synthesis with diffusion models] F. Fürrutter, G. Muñoz-Gil, and H. J. Briegel., (Nature Machine Intelligence 2024).
• [Neural predictor based quantum architecture search] S.-X. Zhang, C.-Y. Hsieh, S. Zhang, and H. Yao., (Machine Learning: Science and Technology 2021).
• [Training-free quantum architecture search] Z. He, M. Deng, S. Zheng, L. Li, and H. Situ., (AAAI 2024).

IV. Quantum Logic Circuit Optimization

A. Quantum Logic Circuit Optimization Targets

• [Optimization of clifford circuits] V. Kliuchnikov and D. Maslov., (Physical Review A 2013).
• [Data structures and algorithms for simplifying reversible circuits] A. K. Prasad, V. V. Shende. et al., (ACM Journal on Emerging Technologies in Computing Systems 2006).
• [A SAT encoding for optimal clifford circuit synthesis] S. Schneider, L. Burgholzer, and R. Wille., (Asia and South Pacific Design Automation Conference 2023).
• [Quartz: superoptimization of quantum circuits] M. Xu, Z. Li, O. Padon, S. Lin, J. Pointing, A. Hirth, H. Ma, J. Palsberg, A. Aiken, U. A. Acar, et al., (Proceedings of the 43rd ACM SIGPLAN International Conference on Programming Language Design and Implementation 2022).
• [Simplification of toffoli networks via templates] D. Maslov, G. W. Dueck, and D. M. Miller., (Symposium on Integrated Circuits and Systems Design 2003).
• [Elementary quantum gate realizations for multiple-control toffoli gates] D. M. Miller, R. Wille, and Z. Sasanian., (International symposium on multiple-valued logic 2011).
• [Quantum circuit optimization using quantum karnaugh map] J.-H. Bae, P. M. Alsing, D. Ahn, and W. A. Miller., (Scientific reports 2020).
• [Quantum circuit simplification using templates] D. Maslov, C. Young, D. M. Miller, and G. W. Dueck., (Design, Automation and Test in Europe 2005).
• [Quantum circuit simplification and level compaction] D. Maslov, G. W. Dueck. D. M. Miller, and C. Negrevergne., (IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2008).
• [Jastrow-type decomposition in quantum chemistry for low-depth quantum circuits] Y. Matsuzawa and Y. Kurashige., (Journal of chemical theory and computation 2016).
• [Quantum circuit optimization with deep reinforcement learning] T. Fösel, M. Y. Niu. F. Marquardt, and L. Li., (arXiv preprint arXiv: 2103.07585 2021).
• [Quarl: A learning-based quantum circuit optimizer] Z. Li, J. Peng, Y. Mei, S. Lin, Y. Wu, O. Padon, and Z. Jia., (Proceedings of the ACM on Programming Languages 2024).
• [A meet-in-the-middle algorithm for fast synthesis of depth-optimal quantum circuits] M. Amy, D. Maslov, M. Mosca, and M. Roetteler., (IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2013).
• [Edge coloring lattice graphs] J. Kattemölle., (Journal of Mathematical Physics 2025).
• [Asymptotically optimal circuit depth for quantum state preparation and general unitary synthesis] X. Sun. G. Tian, S. Yang, P. Yuan, and S. Zhang., (IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2023).
• [Optimal (controlled) quantum state preparation and improved unitary synthesis by quantum circuits with any number of ancillary qubits] P. Yuan and S. Zhang., (Quantum 2023).
• [Does qubit connectivity impact quantum circuit complexity?] P. Yuan, J. Allcock, and S. Zhang., (IEEE Transactions on computer-aided design of integrated circuits and systems 2023).
• [Full characterization of the depth overhead for quantum circuit compilation with arbitrary qubit connectivity constraint] P. Yuan and S. Zhang., (Quantum 2025).
• [Globally optimizing qaoa circuit depth for constrained optimization problems] R. Herrman, L. Treffert. J. Ostrowski, P. C. Lotshaw, T. S. Humble, and G. Siopsis., (Algorithms 2021).
• [Multi-angle quantum approximate optimization algorithm] R. Herrman, P. C. Lotshaw, J. Ostrowski, T. S. Humble, and G. Siopsis., (Scientific Reports 2022).
• [Digitized-counterdiabatic quantum approximate optimization algorithm] P. Chandarana, N. N. Hegade, K. Paul, F. Albarrán-Arriagada, E. Solano, A. Del Campo, and X. Chen., (Physical Review Research 2022).
• [Quantum simulation of electronic structure with linear depth and connectivity] 1. D. Kivlichan, J. McClean. N. Wiebe, C. Gidney. A. Aspuru-Guzik, G. K.-L. et al., (Physical review letters 2018).
• [Elementary gates for quantum computation] A. Barenco, C. H. Bennett, R. Cleve, D. P. DiVincenzo, N. Margolus, P. Shor, et al., (Physical review A 1995).
• [Reducing quantum computations to elementary unitary operations] G. Cybenko., (Computing in science & engineering 2001).
• [Approximation by quantum circuits] E. Knill., (arXiv preprint quant-ph/9508006 1995).
• [Efficient decomposition of quantum gates] J. J. Vartiainen, M. Möttönen, and M. M. Salomaa., (Physical review letters 2004).
• [Quantum circuits for general multiqubit gates] M. Möttönen, J. J. Vartiainen, V. Bergholm, and M. M. Salomaa., (Physical review letters 2004).
• [Synthesis of quantum logic circuits] V. V. Shende, S. S. Bullock, and I. L. Markov., (Proceedings of the 2005 Asia and South Pacific Design Automation Conference 2005).
• [Decompositions of general quantum gates] M. Möttönen and J. J. Vartiainen., (Trends in quantum computing research 2006).
• [On the cnot-cost of toffoli gates] V. V. Shende and 1. L. Markov., (arXiv preprint arXiv:0803.2316 2008).
• [Asymptotically optimal circuits for arbitrary n-qubit diagonal computations] S. S. Bullock and 1. L. Markov., (arXiv preprint quant-ph/0303039 2008).
• [Optimal space-depth trade-off of cnot circuits in quantum logic synthesis] J. Jiang, X. Sun, S.-H. Teng, B. Wu. K. Wu, and J. Zhang., (in ACM-SIAM Symposium on Discrete Algorithms 2020).
• [Near-term quantum computing techniques: Variational quantum algorithms, error mitigation, circuit compilation, benchmarking and classical simulation] H.-L. Huang, X.-Y. Xu, C. Guo, G. Tian, S.-J. Wei, X. Sun, W.-S. Bao, and G.-L. Long., (Science China Physics, Mechanics & Astronomy 2023).
• [Reducing 2-qubit gate count for zx-calculus based quantum circuit optimization] K. Staudacher, T. Guggemos, S. Grundner-Culemann, and W. Gehrke., (arXiv preprint arXiv:2311.08881 2023).
• [6-qubit optimal clifford circuits] S. Bravyi, J. A. Latone, and D. Maslov., (npj Quantum Information 2022).
• [Heuristics for quantum compiling with a continuous gate set] M. G. Davis, E. Smith, A. Tudor, K. Sen, I. Siddiqi, and C. Iancu., (arXiv preprint arXiv: 1912.02727 2019).
• [Qgo: Scalable quantum circuit optimization using automated synthesis] X.-C. Wu, M. G. Davis, F. T. Chong, and C. lancu., (arXiv preprint arXiv:2012.09835 2020).
• [Robust and resource-efficient quantum circuit approximation] T. Patel, E. Younis, C. Iancu, W. de Jong, and D. Tiwari., (arXiv preprint arXiv:2108.12714 2021).
• [Relaxed peephole optimization: A novel compiler optimization for quantum circuits] J. Liu, L. Bello, and H. Zhou., (International Symposium on Code Generation and Optimization 2021).
• [Reinforcement learning based quantum circuit optimization via zx-calculus] J. Riu, J. Nogué, G. Vilaplana, A. Garcia-Saez, and M. P. Estarellas., (Quantum 2025).
• [Reducing the enot count for clifford+ t circuits on nisq architectures] V. Gheorghiu, J. Huang, S. M. Li, M. Mosca, and P. Mukhopadhyay., (IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2022).
• [Quantum circuit optimizations for nisq architectures] B. Nash. V. Gheorghiu, and M. Mosca., (Quantum Science and Technology 2020).
• [Low-depth hamiltonian simulation by an adaptive product formula] Z.-J. Zhang, J. Sun, X. Yuan, and M.-H. Yung., (Physical Review Letters 2023).
• [Adaptive variational quantum dynamics simulations] Y.-X. Yao, N. Gomes, F. Zhang, C.-Z. Wang, K.-M. Ho, T. ladecola, and P. P. Orth., (PRX Quantum 2021).
• [An adaptive variational algorithm for exact molecular simulations on a quantum computer] H. R. Grimsley, S. E. Economou, E. Barnes, and N. J. Mayhall., (Nature communications 2019).
• [Linear-scaling quantum circuits for computational chemistry] 1. Magoulas and F. A. Evangelista., (Journal of Chemical Theory and Computation 2023).
• [Optimizing ansatz design in qaoa for max-cut] R. Majumdar, D. Madan, D. Bhoumik, D. Vinayagamurthy. S. Raghunathan, and S. Sur-Kolay., (arXiv preprint arXiv:2106.02812 2021).
• [Depth optimized ansatz circuit in quoa for max-cut] R. Majumdar, D. Bhoumik, D. Madan, D. Vinayagamurthy, S. Raghunathan, and S. Sur-Kolay., (arXiv preprint arXiv:2110.04637 2021).
• [qubit-adapt-vqe: An adaptive algorithm for constructing hardware-efficient ansätze on a quantum processor] H. L. Tang, V. Shkolnikov, G. S. Barron, H. R. Grimsley. N. J. Mayhall. E. Barnes, and S. E. Economou., (PRX Quantum 2021).
• [Methodology for quantum logic gate construction] X. Zhou, D. W. Leung, and I. L. Chuang., (Physical Review A 2000).
• [Quantum accuracy threshold for concatenated distance-3 codes] P. Aliferis, D. Gottesman, and J. Preskill., (arXiv preprint quant-ph/0504218 2005).
• [High-threshold universal quantum computation on the surface code] A. G. Fowler. A. M. Stephens, and P. Groszkowski., (Physical Review A 2009).
• [Universal quantum computation with ideal clifford gates and noisy ancillas] S. Bravyi and A. Kitaev., (Physical Review A 2005).
• [Roads towards fault-tolerant universal quantum computation] E. T. Campbell, B. M. Terhal, and C. Vuillot., (Nature 2017).
• [Quantum circuit optimization with alphatensor] F. J. Ruiz, T. Laakkonen, J. Bausch, M. Balog. et al., (Nature Machine Intelligence 2025).
• [Optimizing t gates in clifford+ t circuit as rotations around paulis] F. Zhang and J. Chen., (arXiv preprint arXiv: 1903.12456 2019).
• [Approximate quantum fourier transform with o (n log (n)) t gates] Y. Nam, Y. Su, and D. Maslov., (NPJ Quantum Information 2020).
• [T-count optimization and reed-muller codes] M. Amy and M. Mosca., (IEEE Transactions on Information Theory 2019).
• [Quantum circuit optimization by hadamard gate reduction] N. Abdessaied, M. Soeken, and R. Drechsler., (Reversible Computation: 6th International Conference, RC 2014, Kyoto, Japan, July 10-11, 2014. Proceedings 6 2014).
• [Polynomial-time t-depth optimization of clifford+ t circuits via matroid partitioning] M. Amy. D. Maslov, and M. Mosca., (IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2014).

B. Quantum Logic Circuit Optimization Methods

• [A meet-in-the-middle algorithm for fast synthesis of depth-optimal quantum circuits] M. Amy, D. Maslov, M. Mosca, and M. Roetteler., (IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2013).
• [Optimization of clifford circuits] V. Kliuchnikov and D. Maslov., (Physical Review A 2013).
• [6-qubit optimal clifford circuits] S. Bravyi, J. A. Latone, and D. Maslov., (npj Quantum Information 2022).
• [Quartz: superoptimization of quantum circuits] M. Xu, Z. Li, O. Padon, S. Lin, J. Pointing, A. Hirth, H. Ma, J. Palsberg, A. Aiken, U. A. Acar, et al., (Proceedings of the 43rd ACM SIGPLAN International Conference on Programming Language Design and Implementation 2022).
• [Quanto: Optimizing quantum circuits with automatic generation of circuit identities] J. Pointing, O. Padon, Z. Jia, H. Ma, A. Hirth, J. Palsberg, and A. Aiken., (Quantum Science and Technology 2024).
• [Peephole optimization] W. M. McKeeman., (Communications of the ACM 1965).
• [Relaxed peephole optimization: A novel compiler optimization for quantum circuits] J. Liu, L. Bello, and H. Zhou., (International Symposium on Code Generation and Optimization 2021).
• [Advantages of using relative-phase toffoli gates with an application to multiple control toffoli optimization] D. Maslov., (Physical Review A 2016).
• [Decompositions of n-qubit toffoli gates with linear circuit complexity] Y. He. M.-X. Luo, E. Zhang. H.-K. Wang, and X.-F. Wang., (International Journal of Theoretical Physics 2017).
• [tiket): a retargetable compiler for nisq devices] S. Sivarajah, S. Dilkes, A. Cowtan, W. Simmons, A. Edgington, and R. Duncan., (Quantum Science and Technology 2020).
• [Clifford circuit optimization with templates and symbolic pauli gates] S. Bravyi, R. Shaydulin, S. Hu, and D. Maslov., (Quantum 2021).
• [Quantum circuit optimization using quantum karnaugh map] J.-H. Bae, P. M. Alsing, D. Ahn, and W. A. Miller., (Scientific reports 2020).
• [Elementary quantum gate realizations for multiple-control toffoli gates] D. M. Miller, R. Wille, and Z. Sasanian., (International symposium on multiple-valued logic 2011).
• [Qgo: Scalable quantum circuit optimization using automated synthesis] X.-C. Wu, M. G. Davis, F. T. Chong, and C. lancu., (arXiv preprint arXiv:2012.09835 2020).
• [Towards optimal topology aware quantum circuit synthesis] M. G. Davis, E. Smith, A. Tudor, K. Sen. I. Siddiqi. and C. Iancu., (2020 IEEE International Conference on Quantum Computing and Engineering (QCE) 2020).
• [Data structures and algorithms for simplifying reversible circuits] A. K. Prasad, V. V. Shende. et al., (ACM Journal on Emerging Technologies in Computing Systems 2006).
• [Robust and resource-efficient quantum circuit approximation] T. Patel, E. Younis, C. Iancu, W. de Jong, and D. Tiwari., (arXiv preprint arXiv:2108.12714 2021).
• [Quantum circuit optimization by hadamard gate reduction] N. Abdessaied, M. Soeken, and R. Drechsler., (Reversible Computation: 6th International Conference, RC 2014, Kyoto, Japan, July 10-11, 2014. Proceedings 6 2014).
• [Automated optimization of large quantum circuits with continuous parameters] Y. Nam, N. J. Ross, Y. Su, A. M. Childs, and D. Maslov., (npj Quantum Information 2018).
• [A verified optimizer for quantum circuits] K. Hietala, R. Rand, S.-H. Hung, X. Wu, and M. Hicks., (Proceedings of the ACM on Programming Languages 2021).
• [Simplification of toffoli networks via templates] D. Maslov, G. W. Dueck, and D. M. Miller., (Symposium on Integrated Circuits and Systems Design 2003).
• [Quantum circuit simplification using templates] D. Maslov, C. Young, D. M. Miller, and G. W. Dueck., (Design, Automation and Test in Europe 2005).
• [Quantum circuit simplification and level compaction] D. Maslov, G. W. Dueck. D. M. Miller, and C. Negrevergne., (IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2008).
• [Reducing the number of non-clifford gates in quantum circuits] A. Kissinger and J. van de Wetering., (Physical Review A 2020).
• [Reducing 2-qubit gate count for zx-calculus based quantum circuit optimization] K. Staudacher, T. Guggemos, S. Grundner-Culemann, and W. Gehrke., (arXiv preprint arXiv:2311.08881 2023).
• [Polynomial-time t-depth optimization of clifford+ t circuits via matroid partitioning] M. Amy. D. Maslov, and M. Mosca., (IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2014).
• [T-count optimization and reed-muller codes] M. Amy and M. Mosca., (IEEE Transactions on Information Theory 2019).
• [An efficient quantum compiler that reduces T count] L. E. Heyfron and E. T. Campbell., (Quantum Science and Technology 2018).
• [Improved simulation of stabilizer circuits] S. Aaronson and D. Gottesman., (Physical Review A 2004).
• [A SAT encoding for optimal clifford circuit synthesis] S. Schneider, L. Burgholzer, and R. Wille., (Asia and South Pacific Design Automation Conference 2023).
• [On the controlled-not complexity of controlled-not-phase circuits] M. Amy, P. Azimzadeh, and M. Mosca., (Quantum Science and Technology 2018).
• [Heuristics for quantum compiling with a continuous gate set] M. G. Davis, E. Smith, A. Tudor, K. Sen, I. Siddiqi, and C. Iancu., (arXiv preprint arXiv: 1912.02727 2019).
• [Quantum circuit optimization with deep reinforcement learning] T. Fösel, M. Y. Niu. F. Marquardt, and L. Li., (arXiv preprint arXiv: 2103.07585 2021).
• [Quarl: A learning-based quantum circuit optimizer] Z. Li, J. Peng, Y. Mei, S. Lin, Y. Wu, O. Padon, and Z. Jia., (Proceedings of the ACM on Programming Languages 2024).
• [Reinforcement learning based quantum circuit optimization via zx-calculus] J. Riu, J. Nogué, G. Vilaplana, A. Garcia-Saez, and M. P. Estarellas., (Quantum 2025).
• [Discovering faster matrix multiplication algorithms with reinforcement learning] A. Fawzi. M. Balog, A. Huang. T. Hubert, B. Romera-Paredes, M. Barekatain, et al., (Nature 2022).
• [Quantum circuit optimization with alphatensor] F. J. Ruiz, T. Laakkonen, J. Bausch, M. Balog. et al., (Nature Machine Intelligence 2025).

C. Circuit Optimization for VQAs

• [The variational quantum eigensolver: a review of methods and best practices] J. Tilly. H. Chen, S. Cao, D. Picozzi, K. Setia, Y. Li, E. Grant, L. Wossnig, I. Rungger, G. H. Booth, et al., (Physics Reports 2022).
• [Vqe method: a short survey and recent developments] D. A. Fedorov, B. Peng, N. Govind, and Y. Alexeev., (Materials Theory 2022).
• [Variational quantum algorithms] M. Cerezo, A. Arrasmith, R. Babbush, S. C. Benjamin, S. Endo, K. Fujii, J. R. McClean, K. Mitarai, X. Yuan, L. Cincio, et al., (Nature Reviews Physics 2021).
• [A review on quantum approximate optimization algorithm and its variants] K. Blekos, D. Brand, A. Ceschini, C.-H. Chou, R.-H. Li, K. Pandya, and A. Summer., (Physics Reports 2024).
• [Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets] A. Kandala, A. Mezzacapo, K. Temme. M. Takita, M. Brink, J. M. Chow, and J. M. Gambetta., (nature 2017).
• [Strategies for quantum computing molecular energies using the unitary coupled cluster ansatz] J. Romero, R. Babbush, J. R. McClean, C. Hempel. P. J. Love, and A. Aspuru-Guzik., (Quantum Science and Technology 2018).
• [An adaptive variational algorithm for exact molecular simulations on a quantum computer] H. R. Grimsley, S. E. Economou, E. Barnes, and N. J. Mayhall., (Nature communications 2019).
• [qubit-adapt-vqe: An adaptive algorithm for constructing hardware-efficient ansätze on a quantum processor] H. L. Tang, V. Shkolnikov, G. S. Barron, H. R. Grimsley. N. J. Mayhall. E. Barnes, and S. E. Economou., (PRX Quantum 2021).
• [Qubit-excitation-based adaptive variational quantum eigensolver] Y. S. Yordanov, V. Armaos, C. H. Barnes, and D. R. Arvidsson-Shukur., (Communications Physics 2021).
• [Linear-scaling quantum circuits for computational chemistry] 1. Magoulas and F. A. Evangelista., (Journal of Chemical Theory and Computation 2023).
• [Exact electronic states with shallow quantum circuits from global optimisation] H. G. Burton, D. Marti-Dafcik, D. P. Tew, and D. J. Wales., (npj Quantum Information 2023).
• [TETRIS-ADAPT-VQE: An adaptive algorithm that yields shallower, denser circuit ansätze] P. G. Anastasiou, Y. Chen, N. J. Mayhall, E. Barnes, and S. E. Economou., (Physical Review Research 2024).
• [Machine learning assisted construction of a shallow depth dynamic ansatz for noisy quantum hardware] S. Halder, A. Dey, C. Shrikhande, and R. Maitra., (Chemical Science 2024).
• [A practical guide to training restricted boltzmann machines] G. E. Hinton., (in Neural Networks: Tricks of the Trade: Second Edition 2012).
• [A quantum approximate optimization algorithm] E. Farhi, J. Goldstone, and S. Gutmann., (arXiv preprint arXiv: 1411,4028 2014).
• [Circuit compilation methodologies for quantum approximate optimization algorithm] M. Alam, A. Ash-Saki, and S. Ghosh., (in IEEE/ACM MICRO 2020).
• [Quantum circuit compilation by genetic algorithm for quantum approximate optimization algorithm applied to maxcut problem] L. Arufe, M. A. González, A. Oddi, R. Rasconi, and R. Varela., (Swarm and Evolutionary Computation 2022).
• [Optimizing ansatz design in qaoa for max-cut] R. Majumdar, D. Madan, D. Bhoumik, D. Vinayagamurthy. S. Raghunathan, and S. Sur-Kolay., (arXiv preprint arXiv:2106.02812 2021).
• [Introduction to graph theory, vol. 2] D. B. West et al.., (Prentice hall Upper Saddle River 2001).
• [On an estimate of the chromatic class of a p-graph] V. G. Vizing., (Diskret analiz 1964).
• [Depth optimized ansatz circuit in quoa for max-cut] R. Majumdar, D. Bhoumik, D. Madan, D. Vinayagamurthy, S. Raghunathan, and S. Sur-Kolay., (arXiv preprint arXiv:2110.04637 2021).
• [Adaptive quantum approximate optimization algorithm for solving combinatorial problems on a quantum computer] L. Zhu, H. L. Tang, G. S. Barron, F. Calderon-Vargas, N. J. Mayhall, E. Barnes, and S. E. Economou., (Physical Review Research 2022).
• [Dynamic adaptive quantum approximate optimization algorithm for shallow, noise-resilient circuits] N. Yanakiev. N. Mertig, C. K. Long. and D. R. Arvidsson-Shukur., (Physical Review A 2024).
• [Globally optimizing qaoa circuit depth for constrained optimization problems] R. Herrman, L. Treffert. J. Ostrowski, P. C. Lotshaw, T. S. Humble, and G. Siopsis., (Algorithms 2021).
• [Multi-angle quantum approximate optimization algorithm] R. Herrman, P. C. Lotshaw, J. Ostrowski, T. S. Humble, and G. Siopsis., (Scientific Reports 2022).
• [An expressive ansatz for low-depth quantum approximate optimisation] V. Vijendran, A. Das. D. E. Koh, S. M. Assad, and P. K. Lam., (Quantum Science and Technology 2024).

V. Qubit Mapping and Routing

A. Qubit Mapping and Routing Targets

• [Tackling the qubit mapping problem for nisq-era quantum devices] G. Li Y. Ding, and Y. Xie., (International Conference on Architectural Support for Programming Languages and Operating Systems 2019).
• [Mapping quantum circuits to ibm qx architectures using the minimal number of swap and h operations] R. Wille, L.. Burgholzer, and A. Zulehner., (in Proceedings of the 56th Annual Design Automation Conference 2019 2019).
• [A hardware-aware heuristic for the qubit mapping problem in the nisq era] S. Niu, A. Suau, G. Staffelbach, and A. Todri-Sanial., (IEEE Transactions on Quantum Engineering 2020).
• [Qubit mapping and routing via maxsat] A. Molavi, A. Xu. M. Diges, L. Pick, S. Tannu, and A. Albarghouthi., (in MICRO 2022).
• [An efficient methodology for mapping quantum circuits to the ibm qx architectures] A. Zulehner. A. Paler, and R. Wille., (IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2018).
• [On the qubit routing problem] A. Cowtan, S. Dilkes, R. Duncan, A. Krajenbrink, W. Simmons, and S. Sivarajah., (arXiv preprint arXiv: 1902.08091 2019).
• [A dynamic look-ahead heuristic for the qubit mapping problem of nisq computers] P. Zhu, Z. Guan, and X. Cheng., (IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2020).
• [Qubit allocation, in International Symposium on Code Generation and Optimization] M. Y. Siraichi, V. F. d. Santos, C. Collange, and F. M. Q. Pereira., (pp. 113-125, 2018).
• [Not all swaps have the same cost: A case for optimization-aware qubit routing] J. Liu, P. Li, and H. Zhou., (in HPCA 2022).
• [Qubit mapping based on subgraph isomorphism and filtered depth-limited search] S. Li, X. Zhou, and Y. Feng., (IEEE Transactions on Computers 2020).
• [Quantum circuit transformation based on simulated annealing and heuristic search] X. Zhou, S. Li, and Y. Feng., (IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2020).
• [A Monte Carlo tree search framework for quantum circuit transformation] X. Zhou, Y. Feng, and S. Li., (in ICCAD 2020).
• [Reinforcement learning and dear framework for solving the qubit mapping problem] C.-Y. Huang, C.-H. Lien, and W.-K. Mak., (in ICCAD 2022).
• [A fast and scalable qubit-mapping method for noisy intermediate-scale quantum computers] S. Park, D. Kim, M. Kweon, J.-Y. Sim, and S. Kang., (in DAC 2022).
• [Qucloud: A new qubit mapping mechanism for multi-programming quantum computing in cloud environment] L. Liu and X. Dou., (in HPCA 2021).
• [Enabling multi-programming mechanism for quantum computing in the nisq era] S. Niu and A. Todri-Sanial., (Quantum 2023).
• [Optimal qubit assignment and routing via integer programming] G. Nannicini, L. S. Bishop, O. Günlük, and P. Jurcevic., (ACM Transactions on Quantum Computing 2022).
• [Using reinforcement learning to perform qubit routing in quantum compilers] M. G. Pozzi, S. J. Herbert, A. Sengupta, and R. D. Mullins., (ACM Transactions on Quantum Computing 2022).
• [Noise-adaptive compiler mappings for noisy intermediate-scale quantum computers] P. Murali, J. M. Baker, A. Javadi-Abhari, F. T. Chong, and M. Martonosi., (in PLOS 2019).
• [Time-optimal qubit mapping] C. Zhang. A. B. Hayes, L. Qiu, Y. Jin, Y. Chen, and E. Z. Zhang., (in ACM International Conference on Architectural Support for Programming Languages and Operating Systems 2021).
• [Not all qubits are created equal: A case for variability-aware policies for nisq-era quantum computers] S. S. Tannu and M. K. Qureshi., (in Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems 2019).
• [Qure: Qubit re-allocation in noisy intermediate-scale quantum computers] A. Ash-Saki, M. Alam, and S. Ghosh., (in Annual Design Automation Conference 2019).
• [A case for multi-programming quantum computers] P. Das, S. S. Tannu, P. J. Nair, and M. Qureshi., (in IEEE/ACM International Symposium on Microarchitecture 2019).
• ⭐ [On reducing the execution latency of superconducting quantum processors via quantum job scheduling] W. Wu, Y. Wang, G. Yan, Y. Zhao, B. Zhang, and J. Yan., (in ICCAD 2024).

B. Qubit Mapping and Routing Methods

• [Qubit allocation, in International Symposium on Code Generation and Optimization] M. Y. Siraichi, V. F. d. Santos, C. Collange, and F. M. Q. Pereira., (pp. 113-125, 2018).
• [An efficient methodology for mapping quantum circuits to the ibm qx architectures] A. Zulehner. A. Paler, and R. Wille., (IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2018).
• [Time-optimal qubit mapping] C. Zhang. A. B. Hayes, L. Qiu, Y. Jin, Y. Chen, and E. Z. Zhang., (in ACM International Conference on Architectural Support for Programming Languages and Operating Systems 2021).
• [Not all qubits are created equal: A case for variability-aware policies for nisq-era quantum computers] S. S. Tannu and M. K. Qureshi., (in Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems 2019).
• [Tackling the qubit mapping problem for nisq-era quantum devices] G. Li Y. Ding, and Y. Xie., (International Conference on Architectural Support for Programming Languages and Operating Systems 2019).
• [A dynamic look-ahead heuristic for the qubit mapping problem of nisq computers] P. Zhu, Z. Guan, and X. Cheng., (IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2020).
• [A hardware-aware heuristic for the qubit mapping problem in the nisq era] S. Niu, A. Suau, G. Staffelbach, and A. Todri-Sanial., (IEEE Transactions on Quantum Engineering 2020).
• [Not all swaps have the same cost: A case for optimization-aware qubit routing] J. Liu, P. Li, and H. Zhou., (in HPCA 2022).
• [A fast and scalable qubit-mapping method for noisy intermediate-scale quantum computers] S. Park, D. Kim, M. Kweon, J.-Y. Sim, and S. Kang., (in DAC 2022).
• [Enabling multi-programming mechanism for quantum computing in the nisq era] S. Niu and A. Todri-Sanial., (Quantum 2023).
• [Noise-adaptive compiler mappings for noisy intermediate-scale quantum computers] P. Murali, J. M. Baker, A. Javadi-Abhari, F. T. Chong, and M. Martonosi., (in PLOS 2019).
• [Mapping quantum circuits to ibm qx architectures using the minimal number of swap and h operations] R. Wille, L.. Burgholzer, and A. Zulehner., (in Proceedings of the 56th Annual Design Automation Conference 2019 2019).
• [Qubit mapping and routing via maxsat] A. Molavi, A. Xu. M. Diges, L. Pick, S. Tannu, and A. Albarghouthi., (in MICRO 2022).
• [Optimal qubit assignment and routing via integer programming] G. Nannicini, L. S. Bishop, O. Günlük, and P. Jurcevic., (ACM Transactions on Quantum Computing 2022).
• [Qure: Qubit re-allocation in noisy intermediate-scale quantum computers] A. Ash-Saki, M. Alam, and S. Ghosh., (in Annual Design Automation Conference 2019).
• [Qubit mapping based on subgraph isomorphism and filtered depth-limited search] S. Li, X. Zhou, and Y. Feng., (IEEE Transactions on Computers 2020).
• [Using reinforcement learning to perform qubit routing in quantum compilers] M. G. Pozzi, S. J. Herbert, A. Sengupta, and R. D. Mullins., (ACM Transactions on Quantum Computing 2022).
• [Qubit routing using graph neural network aided Monte Carlo tree search] A. Sinha, U. Azad, and H. Singh., (in AAAI 2022).
• [Reinforcement learning and dear framework for solving the qubit mapping problem] C.-Y. Huang, C.-H. Lien, and W.-K. Mak., (in ICCAD 2022).