References
- C. H. Bennett, S. J. Wiesner (1992). “Communication via 1-and 2-particle operators on Einstein-Podolsky-Rosen states”. Physical Review Letters 69, 2881–2884. https://doi.org/10.1103/PhysRevLett.69.2881.
- C. H. Bennett, G. Brassard, C. Crepeau, R. Jozsa, A. Peres, W. K. Wootters (1993). “Teleporting an unknown quantum state via dual classic and Einstein-Podolsky-Rosen channels”. Physical Review Letters 70, 1895–1899. https://doi.org/10.1103/PhysRevLett.70.1895.
- S. Imre and M. Berces (2021). “Entanglement-based competition resolution in distributed systems”. IEEE Access 9, 10253–10262. https://doi.org/10.1109/ACCESS.2021.3050271.
- P. W. Shor (2002). “Introduction to quantum algorithms”. AMS PSAPM 58, 143–159. https://doi.org/10.48550/arXiv.quant-ph/0005003.
- R. Jozsa (1998). “Quantum algorithms and the Fourier transform”. Proceedings of the Royal Society of London. Series A 454, 323–337. https://doi.org/10.1098/rspa.1998.0163
- S. Imre (2007). “Quantum existence testing and its application for finding extreme values in unsorted databases”. IEEE Transactions on Computers 56: 5, 706–710. https://doi.org/10.1109/TC.2007.1032.
- L. Gyongyosi, S. Imre, H. V. Nguyen (2018). “A survey on quantum channel capacities”. IEEE Communications Surveys and Tutorials 20: 2. https://doi.org/10.1109/COMST.2017.2786748.
- Q. A. Memon, M. Al Ahmad and M. Pecht, M (2024). “Quantum computing: Navigating the future of computation, challenges, and technological breakthroughs”. Quantum Reports, 6, 627663. https://doi.org/10.3390/quantum6040039.
- Z. Yang, M. Zolanvari, and R. Jain (2023). “A survey of important issues in quantum computing and communications”. IEEE Communications and Surveys Tutorials 25: 2, 1059–1094. https://doi.org/10.1109/COMST.2023.3254481.
- H. Sahu, H. P. Gupta (2023). “Quantum computing toolkit from nuts and bolts to sack of tools”. arXiv:2302.08884 https://doi.org/10.48550/arXiv.2302.08884.
- P. Singh, R. Dasgupta, A. Singh et al. (2024). “A survey on available tools and technologies enabling quantum computing”. IEEE Access 57974–57991. https://doi.org/10.1109/ACCESS.2024.3388005.
- L. Gyongyosi and S. Imre (2019). “A survey on quantum computing technology”. Computer Science Review 31, 51–71. https://doi.org/10.1016/j.cosrev.2018.11.002.
- S. S. Gill et al (2021). “Quantum computing: a taxonomy, systematic review and future directions”. Software: Practice and Experience https://doi.org/10.1002/spe.3039.
- E. Chae, J. Choi, J. Kim (2024). “An elementary review on basic principles and developments of qubits for quantum computing”. Nano Convergence 11, 11. https://doi.org/10.1186/s40580-024-00418-5.
- A. Bochkarev, R. Heese, S. Jager et al (2024). “Quantum computing for discrete optimization: a highlight of three technologies”. arXiv:2409.01373 https://doi.org/10.48550/arXiv.2409.01373.
- Y. Li, M. Tian, G. Liu, C. Peng, and L. Jiao (2020). “Quantum optimization and quantum learning: A survey”. IEEE Access 8, 23 568–23 593. https://doi.org/10.1109/ACCESS.2020.2970105.
- J. Kusyk, S. M. Saeed, and M. U. Uyar (2021). “Survey on quantum circuit compilation for noisy intermediatescale quantum computers: Artificial intelligence to heuristics”. IEEE Transactions on Quantum Engineering 2, 1–16. https://doi.org/10.1109/TQE.2021.3068355.
- P. Upama, M. J. Hossain Faruk, M. Nazim, M. Masum, H. Shahriar, G. Uddin, S. Barzanjeh, A. Rahman, and S. Ahamed (2022). “Evolution of quantum computing: a systematic survey on the use of quantum computing tools”. 2022 IEEE Computers, Software, and Applications Conference https://doi.org/10.1109/COMPSAC54236.2022.00096.
- F. V. Massoli, L. Vadicamo, G. Amato, and F. Falchi (2022). “A leap among quantum computing and quantum neural networks: A survey”. ACM Computing Surveys 55, 5. https://doi.org/10.1145/3529756.
- S. B. Ramezani, A. Sommers, H. K. Manchukonda, S. Rahimi, and A. Amirlatifi (2020). “Machine learning algorithms in quantum computing: A survey”. 2020 International Joint Conference on Neural Networks (IJCNN) 1–8. https://doi.org/10.1109/IJCNN48605.2020.9207714.
- D. Peral-Garcia et al (2024). “Systematic literature review: Quantum machine learning and its applications”. Computer Science Review https://doi.org/10.1016/j.cosrev.2024.100619.
- M. D. Garcia, A. M. Romero (2024). “Survey on computational applications of tensor network simulations”. IEEE Access DOI: 10.1109/ACCESS.2024.3519676. https://doi.org/10.1109/ACCESS.2024.3519676.
- T. Ayral, P. Besserve, D. Lacroix, E. A. R. Guzman (2023). “Quantum computing with and for many-body physics”. The European Physical Journal A 59, 227. https://doi.org/10.1140/epja/s10050–023-01141-1.
- E. Moguel, J. R. and V. Garcia-Alonso (2022). “Quantum service-oriented computing: current landscape and challenges”. Software Quality Journal 30, 9831002. https://doi.org/10.1007/s11219-022-09589-y.
- M. A. Serrano, J. A. Cruz-Lemus, R.-P. Castillo and M. Piattini (2022). “Quantum software components and platforms: overview and quality assessment”. ACM Computing Surveys 55: 8 1–31. https://doi.org/10.1145/3548679.
- A. A. Khan, A. Ahmad, M. Waseem, P. Liang, M. Fahmideh, T. Mikkonen, and P. Abrahamsson (2023). “Software architecture for quantum computing systems-A systematic review”. Journal of Systems and Software, 201, 111682. https://doi.org/10.1016/j.jss.2023.111682.
- L. Jimnez-Navajas, F. Bhler, F. Leymann et al (2024). “Quantum software development: a survey”. Quantum Information and Computation 24: 7–8, 0609–0642. https://doi.org/10.26421/qic24.7-8-4.
- S. Garhwal, M. Ghorani, and A. Ahmad (2021). “Quantum programming language: A systematic review of research topic and top cited languages”. Archives of Computational Methods in Engineering, 28, 89–310. https://doi.org/10.1007/s11831-019-09372-6.
- K. Dwivedi, M. Haghparast, T. Mikkonen (2024). “Quantum software engineering and quantum software development lifecycle: a survey”. Cluster Computing 27, 7127–7145. https://doi.org/10.1007/s10586-024-04362-1.
- D. Cuomo, M. Caleffi (2020). “Towards a distributed quantum computing ecosystem”. IET Quantum Communication, 1: 1, 38. https://doi.org/10.1049/iet-qtc.2020.0002.
- M. Caleffi, M. Amoretti, D. Ferrari et al (2024). “Distributed quantum computing: a survey”. Computer Networks 254, 110672. https://doi.org/10.1016/j.comnet.2024.110672.
- D. Barrala, F. J. Cardamab, G. Diaz et al (2024). “Review of distributed quantum computing. From single QPU to high performance quantum computing”. arXiv:2404.01265v1 https://doi.org/10.48550/arXiv.2404.01265.
- J. C. Boschero, N. M. P. Neumann, W. van der Schoot, T. Sijpesteijn, R. Wezeman (2024). “Distributed quantum computing: applications and challenges”. arXiv:2410.00609 https://doi.org/10.48550/arXiv.2410.00609.
- G. M. Jones, H.-A. Jacobsen (2024). “Distributed quantum computing for chemical applications”. 2024 IEEE International Conference on Quantum Computing and Engineering (QCE) https://doi.org/10.1109/QCE60285.2024.10270.
- L. Pira and C. Ferrie (2023). “An invitation to distributed quantum neural networks”. Quantum Machine Intelligence https://doi.org/10.1007/s42484-023-00114-3.
- H. T. Nguyen, P. Krishnan, D. Krishnaswamy et al (2024). “Quantum cloud computing: a review, open problems, and future directions”. arXiv:2404.11420 https://doi.org/10.48550/arXiv.2404.11420.
- E. Moguel, J. Garcia-Alonso, J. M. Murillo (2024). “Development and deployment of quantum services”, in I. Exman et al. (eds), Quantum Software Springer. https://doi.org/10.1007/978-3-031-64136-7 8.
- F. Phillipson (2023). “Quantum computing in telecommunication-a survey”. Mathematics 11: 15, 3423. https://doi.org/10.3390/math11153423.
- Y. Baseri, V. Chouhan, A. Ghorbani (2024). “Cybersecurity in the quantum era: Assessing the impact of quantum computing on infrastructure”. arXiv:2404.10659 https://doi.org/10.48550/arXiv.2404.10659.
- H. Dutta, A. K. Bhuyan (2024). “Quantum communication: from fundamentals to recent trends, challenges and open problems”. arXiv:2406.04492 https://doi.org/10.48550/arXiv.2406.04492.
- Z. Li, K. Xue, J. Li, L. Chen, R. Li, Z. Wang, et al (2023). “Entanglement-assisted quantum networks: Mechanics, enabling technologies, challenges, and research directions”. IEEE Communications Surveys and Tutorials 25: 4, 2133–2189. https://doi.org/10.1109/COMST.2023.3294240.
- M. Mehic et al (2023). “Quantum cryptography in 5G networks: A comprehensive overview”. IEEE Communications Surveys and Tutorials 26: 1, https://doi.org/10.1109/COMST.2023.3309051.
- A. B. Popa, P. G. Popescu (2024). “The future of QKD networks”. arXiv:2407.00877 https://doi.org/10.48550/arXiv.2407.00877.
- A. Abane, M. Cubeddu, V. S. Mai, A. Battou (2025). “Entanglement routing in quantum networks: a comprehensive survey”. IEEE Transactions on Quantum Engineering 1–36. https://doi.org/10.1109/TQE.2025.3541123.
- L. Gyongyosi, S. Imre (2022). “Advances in the quantum internet”. Communications of the ACM 65: 8, 52–63. https://doi.org/10.1145/3524455.
- S. Wehner, D. Elkouss, R. Hanson (2018). “Quantum internet: A vision for the road ahead”. Science, 362, 6412. https://doi.org/10.1126/science.aam9288.
- Y. Li. et al (2024). “A survey of quantum internet protocols from a layered perspective”. IEEE Communications Surveys and Tutorials 26: 3. https://doi.org/10.1109/COMST.2024.3361662.
- J. Ang, G. Carini, Y. Chen, I. Chuang et al (2024). “Architectures for multinode superconducting quantum computers”. ACM Transactions on Quantum Computing 5: 3, 1–59. https://doi.org/10.1145/3674151.
- S. Barz et al (2012). “Demonstration of blind quantum computing”. Science 335: 6066, 303–308. https://doi.org/10.1126/science.1214707.
- S. Ruiting et al (2021). “Verifiable multiparty universal blind quantum computing in distributed network”s. Chinese Journal of Electronics 30: 4, 712–718. https://dx.doi.org/10.1049/cje.2021.05.013.
- A. Mantri, C. Perez-Delgado and J. Fitzsimons (2013). “Optimal blind quantum computation”. Physical Review Letters 111: 23, 230502. https://doi.org/10.1103/PhysRevLett.111.230502.
- J. I. Cirac, A. K. Ekert, S. F. Huelga, C. Macchiavello (1999). “Distributed quantum computation over noisy channels”. Physical Review A, 59, 4249–4254. https://doi.org/10.1103/PhysRevA.59.4249.
- D. Collins, N. Linden, S. Popescu (2001). “Nonlocal content of quantum operations”. Physical Review A 64, 7. https://doi.org/10.1103/PhysRevA.64.032302.
- J. Eisert, K. Jacobs, P. Papadopoulos, M. B. Plenio (2000). “Optimal local implementation of nonlocal quantum gates”. Physical Review A 62: 5. https://doi.org/10.1103/PhysRevA.62.052317.
- C. Gidney, M. Ekera (2021). “How to factor 2048 bit RSA integers in 8 hours using 20 million noisy qubits”. Quantum 5. https://doi.org/10.22331/q-2021-04-15-433.
- L. Gyongyosi, S. Imre (2018). “Multiple access multicarrier continuous-variable quantum key distribution”. Chaos, Solitons and Fractals 114, 491–505. https://doi.org/10.1016/j.chaos.2018.07.006.
- L. Gyongyosi, S. Imre (2020). “Resource prioritization and balancing for the quantum internet”. Scientific Reports https://doi.org/DOI: 10.1038/s41598-020-78960-5.
- L. Gyongyosi, S. Imre (2021). “Scalable distributed gate-model quantum computers”. Scientific Reports 11: 5172. https://doi.org/10.1038/s41598-020-76728-5.
- H. Li, D. Qiu, L. Luo (2023). “Distributed exact quantum algorithms for Deutsch-Jozsa problem”. arXiv.2303.10663. https://doi.org/10.48550/arXiv.2303.10663.
- N. M. Neumann, R. van Houte, T. Attema (2020). “Imperfect distributed quantum phase estimation”. Computational Science-ICCS 2020: 12142 of LNCS, Springer, pp. 605–615. https://doi.org/10.1007/978-3-030-50433-5 46
- Y. Shi, T. Nguyen, S. Stein, T. Stavenger, M. Warner, M. Roetteler et al (2023). “A reference implementation for a quantum message passing interface”. Proc. of the SC23 Workshops of The Int. Conf. on High Performance Computing Network, Storage, and Analysis, ACM, New York, pp. 1420–1425. https://doi.org/10.1145/3624062.3624212.
- J. Tan, L. Xiao, D. Qiu, L. Luo, P. Mateus (2022). “Distributed quantum algorithm for Simon’s problem”. Physical Review A 106: 3. https://doi.org/10.1103/PhysRevA.106.032417.
- R. Van Meter, W. J. Munro, K. Nemoto, K. M. Itoh (2008). “Arithmetic on a distributed-memory quantum multicomputer”. ACM Journal on Emerging Technologies in Computing Systems, 3: 4. https://doi.org/10.1145/1324177.1324179.
- L. Xiao, D. Qiu, L. Luo, P. Mateus (2023). “Distributed quantum-classical hybrid Shor’s algorithm”. arXiv.2304.12100 https://doi.org/10.48550/arXiv.2304.12100.
- Z. Zhang and Q. Zhuang (2021). “Distributed quantum sensing”. Quantum Science and Technology 6: 4, 043001. https://doi.org/10.1088/2058-9565/abd4c3.
- C. L. Degen, F. Reinhard, and P. Cappellaro (2017). “Quantum sensing”. Reviews of Modern Physics, 89: 3, 035002. https://doi.org/10.1103/RevModPhys.89.035002.
- X. Zhou, D. Qiu, L. Luo (2023). “Distributed Bernstein-Vazirani algorithm”. Physica A: Statistical Mechanics and its Applications 629, 129209. https://doi.org/10.1016/j.physa.2023.129209.
- X. Zhou, D. Qiu, L. Luo (2023). “Distributed exact Grovers algorithm”. Frontiers of Physics, 18: 5, 1–25. https://doi.org/10.1007/s11467-023-1327-x.
- B. He, D. Zhang, S. W. Loke, S. Lin, L. Lu (2024). “Building a hierarchical architecture and communication model for the quantum internet”. IEEE Journal on Selected Areas in Communications 42, 7. https://doi.org/10.1109/JSAC.2024.3380103.
- W. Kozlowski, S. Wehner, R. V. Meter, B. Rijsman, A. S. Cacciapuoti, M. Caleffi et al. (2023). RFC 9340: Architectural Principles for a Quantum Internet Internet Research Task Force (IRTF). https://doi.org/10.17487/RFC9340.
- J. Roffe (2019). “Quantum error correction: an introductory guide”. Contemporary Physics 60, 3. https://doi.org/10.1080/00107514.2019.1667078.
- A. Paler, S. J. Devitt (2015). “An introduction to fault-tolerant quantum computing”. DAC’15 Proceedings of the 52nd Annual Design Automation Conference 60. https://doi.org/10.1145/2744769.2747911.
- K. Heshami, D. G. England, P. C. Humphreys (2016). “Quantum memories: emerging applications and recent advances”. Journal of Modern Optics, 63, S3, S42–S65. https://doi.org/10.1080/09500340.2016.1148212.
- B. M. Terhal (2015). “Quantum error correction for quantum memories”. Reviews of Modern Physics, 87, 307. https://doi.org/10.1103/RevModPhys.87.307.
- S. Bravyi, D. Gosset, and R. Konig (2018). “Quantum advantage with shallow circuits”. Science, 362, 308. https://doi.org/10.1126/science.aar3106.
- J. D. Hidary (2021). Quantum Computing: an Applied Approach Springer, ISBN-10: 3030832732.
- T. D. Ladd, F. Jelezko, R. Laflamme, Y. Nakamura, C. Monroe, and J. L. O’Brien (2010). “Quantum computers”. Nature 464: 7285, 4553. https://doi.org/10.1038/nature08812.
- M. AbuGhanem (2024). “Photonic quantum computers”. arXiv:2409.08229 https://doi.org/10.48550/arXiv.2409.08229.
- M. Kjaergaard, M. E. Schwartz, J. Braumuller, P. Krantz, J. I.-J. Wang, S. Gustavsson, and W. D. Oliver (2020). “Superconducting qubits: Current state of play”. Annual Review of Condensed Matter Physics 11, 369395. https://doi.org/10.1146/annurev-conmatphys-031119-050605.
- F. Arute et al (2019). “Quantum supremacy using a programmable superconducting processor”. Nature 574. https://doi.org/10.1038/s41586-019-1666-5.
- R. Maurand, X. Jehl, D. Kotekar-Patil, A. Corna, H. Bohuslavskyi, R. Lavieville, L. Hutin, S. Barraud, M. Vinet, M. Sanquer et al (2016). “A cmos silicon spin qubit”. Nature Communications 7: 1, 13575. https://doi.org/10.1038/ncomms13575.
- L. Henriet, L. Beguin, A. Signoles, T. Lahaye, A. Browaeys, G.-O. Reymond, and C. Jurczak (2020). “Quantum computing with neutral atoms”. Quantum 4: 327. https://doi.org/10.22331/q-2020-09-21-327.
- R.-Y. Gong, Z.-Y. He, C.-H. Yu, G.-F. Zhang, F. Nori, Z.-L. Xiang (2024). “Tunable quantum router with giant atoms, implementing quantum gates, teleportation, non-reciprocity, and circulators”. arXiv:2411.19307 https://doi.org/10.48550/arXiv.2411.19307.
- C. D. Bruzewicz, J. Chiaverini, R. McConnell, and J. M. Sage (2019). “Trapped-ion quantum computing: Progress and challenges”. Applied Physics Reviews 6: 2, 021314. https://doi.org/10.1063/1.5088164.
- C. Nayak, S. H. Simon, A. Stern, M. Freedman, and S. D. Sarma (2008). “Non-abelian anyons and topological quantum computation”. Reviews of Modern Physics 80: 3, 1083. https://doi.org/10.1103/RevModPhys.80.1083.
- S. Pezzagna, J. Meijer (2021). “Quantum computer based on color centers in diamond”. Applied Physics Reviews, 8, 011308. https://doi.org/10.1063/5.0007444.
- L. M. K. Vandersypen et al (2001). “Experimental realization of Shor’s quantum factoring algorithm using nuclear magnetic resonance”. Nature, 414, 883–887. https://doi.org/10.1038/414883a.
- L. M. K. Vandersypen, I. L. Chuang (2005). “NMR techniques for quantum control and computation”. Reviews of Modern Physics 76, 1037. https://doi.org/10.1103/RevModPhys.76.1037.
- N. C. Jones, R. Van Meter, A. G. Fowler, P. L. McMahon, J. Kim, T. D. Ladd, Y. Yamamoto (2012). “Layered architecture for quantum computing”. Physical Review X 2, 031007. https://doi.org/10.1103/PhysRevX.2.031007.
- S. Rodrigo, S. Abadal, E. Alarcon et al (2021). “On double full-stack communication-enabled architectures for multicore quantum computers”. IEEE Micro 41, 4856. https://doi.org/10.1109/MM.2021.3092706.
- R. Van Meter (2014). Quantum Networking John Wiley and Sons Ltd.
- J. Illiano, M. Caleffi, A. Manzalini, A. S. Cacciapuoti (2022). “Quantum Internet protocol stack: A comprehensive survey”. Computer Networks 213, 109092. https://doi.org/10.1016/j.comnet.2022.109092.
- Z. Li, K. Xue, J. Li, N. Yu, J. Liu, D. S. L. Wei et al (2021). “Building a large scale and wide-area quantum Internet based on an OSI-alike model”. China Communications 18: 10, 1–14. https://doi.org/10.23919/JCC.2021.10.001.
- A. Dahlberg, M. Skrzypczyk, T. Coopmans, L. Wubben, F. Rozpundefineddek, M. Pompili et al (2019). “A link layer protocol for quantum networks”, in Proceedings of the ACM Special Interest Group on Data Communication ACM, New York, 159–173. https://doi.org/10.1145/3341302.3342070.
- A. Pirker, W. Dur (2019). “A quantum network stack and protocols for reliable entanglement-based networks”. New Journal of Physics, 21, 3. https://doi.org/10.1088/1367-2630/ab05f7.
- R. V. Meter, J. Touch (2013). “Designing quantum repeater networks. IEEE Communications Magazine, 51: 8, 64–71. https://doi.org/10.1109/MCOM.2013.6576340.
- R. Van Meter, R. Satoh, N. Benchasattabuse, K. Teramoto, T. Matsuo, M. Hajdusek et al. (2022). “A quantum Internet architecture”. 2022 IEEE Int. Conf. on Quantum Computing and Engineering (QCE) 341–352. https://doi.org/10.1109/QCE53715.2022.00055.
- J. Preskill (2018). “Quantum Computing in the NISQ era and beyond”. Quantum, 2, 79. https://doi.org/10.22331/q-2018-08-06-79.
- A. W. Harrow and A. Montanaro (2017). “Quantum computational supremacy”. Nature 549, 203–209. https://doi.org/10.1038/nature23458.
- S. Aaronson and L. Chen (2017). “Complexity-theoretic foundations of quantum supremacy experiments”, in Proceedings of the 32nd Computational Complexity Conference CCC ’17, pp. 22:1–22:67. https://doi.org/10.4230/LIPIcs.CCC.2017.22.
- E. Farhi, J. Goldstone, S. Gutmann and H. Neven (2017). “Quantum algorithms for fixed qubit architectures”. arXiv:1703.06199v1 https://doi.org/10.48550/arXiv.1703.06199.
- M. Mastriani (2024). “Explaining the results of quantum mechanics via entanglement closed loop”. Optical and Quantum Electronics 56, 801. https://doi.org/10.1007/s11082-024-06393-9.
- Y. Alexeev et al (2019). “Quantum computer systems for scientific discovery”. PRX Quantum 2, 017001. https://doi.org/10.1103/PRXQuantum.2.017001.
- M. Loncar et al (2019). “Development of quantum interconnects for next-generation information technologies”. PRX Quantum 2, 017002. https://doi.org/10.1103/PRXQuantum.2.017002
- B. Foxen et al (2020). “Demonstrating a continuous set of two-qubit gates for near-term quantum algorithms”. Physical Review Letters 125, 120504. https://doi.org/10.1103/PhysRevLett.125.120504.
- A. Ajagekar, T. Humble, and F. You (2020). “Quantum computing based hybrid solution strategies for large-scale discrete-continuous optimization problems”. Computers and Chemical Engineering 132, 106630. https://doi.org/10.1016/j.compchemeng.2019.106630.
- A. Ajagekar and F. You (2019). “Quantum computing for energy systems optimization: Challenges and opportunities”. Energy 179, 76–89. https://doi.org/10.1016/j.energy.2019.04.186.
- M. Harrigan et al (2020). “Quantum approximate optimization of non-planar using eigen energies graph problems on a planar superconducting processor”. Nature Physics 17, 332–336. https://doi.org/10.1038/s41567-020-01105-y.
- N. Rubin et al (2020). “Hartree-Fock on a superconducting qubit quantum computer”. Science 69: 6507, 1084–1089. https://doi.org/10.1126/science.abb9811.
- E. Farhi, J. Goldstone and S. Gutmann (2014). “A quantum approximate optimization algorithm”. arXiv:1411.4028v1 https://doi.org/10.48550/arXiv.1411.4028.
- E. Farhi, J. Goldstone, S. Gutmann and L. Zhou, L (2019). “The quantum approximate optimization algorithm and the Sherrington-Kirkpatrick model at infinite size”. Quantum 6, 759. https://doi.org/10.22331/q-2022-07-07-759.
- E. Farhi and H. Neven (2018). “Classification with quantum neural networks on near term processors”. arXiv:1802.06002v1 https://doi.org/10.48550/arXiv.1802.06002.
- I. Sax et al (2020). “Approximate approximation on a quantum annealer”. CF ‘20: Proceedings of the 17th ACM International Conference on Computing Frontiers pp. 108–117. https://doi.org/10.1145/3387902.3392635.
- K. A. Brown and T. Roser (2020). “Towards storage rings as quantum computers”. Physical Review Accelerators and Beams 23, 054701. https://doi.org/10.1103/PhysRevAccelBeams.24.049901.
- J. Bae, P. M. Alsing, D. Ahn et al (2020). “Quantum circuit optimization using quantum Karnaugh map”. Scientific Reports, 10, 15651, https://doi.org/10.1038/s41598-020-72469-7.
- S. Li et al (2020). “Programmable unitary operations for orbital angular momentum encoded states”. National Science Open 1: 2, 20220019. https://doi.org/10.1360/nso/20220019.
- S. Bugu, F. Ozaydin and T. Kodera (2020). “Surpassing the classical limit in magic square game with distant quantum dots coupled to optical cavities”. Scientific Reports, DOI: https://doi.org/10.1038/s41598-020-79295-x.
- A. Teplukhin, B. Kendrick and D. Babikov (2020). “Solving complex eigenvalue problems on a quantum annealer with applications to quantum scattering resonances”. Physical Chemistry Chemical Physics. https://doi.org/10.1039/D0CP04272B.
- S. Lloyd (2018). “Quantum approximate optimization is computationally universal”. arXiv:1812.11075 https://doi.org/10.48550/arXiv.1812.11075.
- Q. Zhu. et al (2022). “Quantum computational advantage via 60-qubit 24-cycle random circuit sampling”. Science Bulletin 67: 3, 240–245. https://doi.org/10.1016/j.scib.2021.10.017.
- S. Aaronson, A. Arkhipov (2013). “The computational complexity of linear optics”. Theory of Computing 9: 4, 143–252. https://doi.org/10.1145/1993636.1993682.
- H-S. Zhong et al (2020). “Quantum computational advantage using photons”. Science 370: 6523, 1460–1463. https://doi.org/10.1126/science.abe8770.
- L. S. Madsen et al (2022). “Quantum computational advantage with a programmable photonic processor”. Nature 606: 7912, 75–81. https://doi.org/10.1038/s41586-022-04725-x.
- M. Reiher, N. Wiebe, K. M. Svore et al (2017). “Elucidating reaction mechanisms on quantum computers”. Proceedings of the National Academy of Sciences, 114 7555–7560. https://doi.org/10.1073/pnas.1619152114.
- S. Mandra and H. G. Katzgraber (2018). “A deceptive step towards quantum speedup detection”. Quantum Science Technology 3 04LT. https://doi.org/10.1088/2058-9565/aac8b2.
- V. Moret-Bonillo (2015). “Can artificial intelligence benefit from quantum computing?” Progress in Artificial Intelligence 3 89–105. https://doi.org/10.1007/s13748-014-0059-0.
- T. Yamakawa and M. Zhandry (2022). “Verifiable quantum advantage without structure”, in 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS) 69–74. https://doi.org/10.1145/3658665.
- T. F. Ronnow, Z. Wang, J. Job et al (2014). “Defining and detecting quantum speedup”. Science 345, 420–424. https://doi.org/10.1126/science.1252319.
- S. Aaronson (2015). “Quantum machine learning algorithms: Read the fine print”, Nature Physics, 11, 291–293. https://doi.org/10.1038/nphys3272.
- K. Bharti, A. Cervera-Lierta, T. H. Kyaw, T. Haug, S. Alperin-Lea, A. Anand, M. Degroote, H. Heimonen, J. S. Kottmann, T. Menke, et al (2022). “Noisy intermediate-scale quantum (NISQ) algorithms”. Reviews of Modern Physics, 94, 015004. https://doi.org/10.1103/RevModPhys.94.015004.
- P. W. Shor (1994). “Algorithms for quantum computation: discrete logarithms and factoring”, in Proceedings 35th Annual Symposium on Foundations of Computer Science, 124–134. https://doi.org/10.1109/SFCS.1994.365700.
- V. S. Denchev, S. Boixo, S. V. Isakov et al (2016). “What is the computational value of finite-range tunneling?” Physical Review X, 6, 1–17. https://doi.org/10.1103/PhysRevX.6.031015.
- P. J. J. O’Malley et al (2016). “Scalable quantum simulation of molecular energies”. Physical Review X 6, 031007. https://doi.org/10.1103/PhysRevX.6.031007.
- F. Arute et al (2020). “Observation of separated dynamics of charge and spin in the Fermi-Hubbard model”. arXiv:2010.07965 https://doi.org/10.48550/arXiv.2010.07965.
- I. Aleiner et al (2020). “Accurately computing electronic properties of materials using eigenenergies”. arXiv:2012.00921 https://doi.org/10.1038/s41586-021-03576-2.
- E. Farhi, D. Gamarnik and S. Gutmann (2020). “The quantum approximate optimization algorithm needs to see the whole graph: a typical case”. arXiv:2004.09002v1 https://doi.org/10.48550/arXiv.2004.09002.
- E. Farhi, D. Gamarnik and S. Gutmann (2020). “The quantum approximate optimization algorithm needs to see the whole graph: worst case examples”. arXiv:2005.08747 https://doi.org/10.48550/arXiv.2005.08747.
- A. Peruzzo et al (2014). “A variational eigenvalue solver on a photonic quantum processor”. Nature Communications, 5, 4213. https://doi.org/10.1038/ncomms5213.
- D. Wecker, M. B. Hastings, and M. Troyer (2015). “Progress towards practical quantum variational algorithms”. Physical Review A, 92, 042303. https://doi.org/10.1103/PhysRevA.92.042303.
- J. R. McClean, J. Romero, R. Babbush, and A. Aspuru-Guzik (2016). “The theory of variational hybrid quantum-classical algorithms”. New Journal of Physics, 18. https://doi.org/10.1088/1367-2630/18/2/023023.
- X. Yuan et al (2019). “Theory of variational quantum simulation”. Quantum, 3, 191. https://doi.org/10.22331/q-2019-10-07-191.
- A. Kardashin et al (2021). “Benchmarking variational quantum simulation against an exact solution”, in 023 IEEE International Conference on Quantum Computing and Engineering (QCE) Bellevue, WA, USA, 518–523. https://doi.org/10.1109/QCE57702.2023.00065.
- K. Mitarai and K. Fujii (2021). “Constructing a virtual two-qubit gate by sampling single-qubit operations”. New Journal of Physics, 23. https://doi.org/10.1088/1367-2630/abd7bc
- T. Peng, A. W. Harrow, M. Ozols, and X. Wu (2020). “Simulating large quantum circuits on a small quantum computer”. Physical Review Letters 125, 150504. https://doi.org/10.1103/PhysRevLett.125.150504.
- L. Brenner, C. Piveteau, D. Sutter (2023). “Optimal wire cutting with classical communication”. arXiv.2302.03366 https://doi.org/10.48550/arXiv.2302.03366.
- P. Gokhale, O. Angiuli, Y. Ding, K. Gui, T. Tomesh, M. Suchara, et al. (2019). “Minimizing state preparations in variational quantum eigensolver by partitioning into commuting families”. arXiv.1907.13623
- J. Tilly, H. Chen, S. Cao, D. Picozzi, K. Setia, Y. Li, et al (2022). “The variational quantum eigensolver: A review of methods and best practices”. Physics Reports 986, 1128. https://doi.org/10.1016/j.physrep.2022.08.003.
- Y. Wang, L. M. Sager-Smith, D. A. Mazziotti (2023). “Quantum simulation of bosons with the contracted quantum eigensolver”. New Journal of Physics 25, 10. https://doi.org/10.1088/1367-2630/acf9c3.
- M. Schuld, V. Bergholm, C. Gogolin, J. Izaac, and N. Killoran (2019). “Evaluating analytic gradients on quantum hardware”. Physical Review A 99. https://doi.org/10.1103/PhysRevA.99.032331.
- A. J. McCaskey, D. I. Lyakh, E. F. Dumitrescu, S. S. Powers, T. S. Humble (2019). “XACC: A system-level software infrastructure for heterogeneous quantum-classical computing”. Quantum Science and Technology 5: 2. https://doi.org/10.1088/2058-9565/ab6bf6
- A. J. McCaskey, T. Nguyen, A. Santana, D. Claudino, T. Kharazi, H. Finkel (2021). “Extending C++ for heterogeneous quantum-classical computing”. ACM Transactions on Quantum Computing, 2: 2, 136. https://doi.org/10.1145/3462670.
- J. K. Lee, O. T. Brown, M. Bull, M. Ruefenacht, J. Doerfert, M. Klemm et al. (2023). “Quantum task offloading with the openmp api”. arXiv.2311.03210. https://doi.org/10.48550/arXiv.2311.03210.
- J. Gambetta (2022). Quantum-centric Supercomputing: The Next Wave of Computing https://www.ibm.com/quantum/blog/next-wave-quantum-centric-supercomputing
- J. Gambetta (2023). The Hardware and Software for the Era of Quantum Utility Is Here https://www.ibm.com/quantum/blog/quantum-roadmap-2033
- L. Liu, X. Dou (2021). “QuCloud: A new qubit mapping mechanism for multiprogramming quantum computing in cloud environment”, in IEEE Int. Symposium on High-performance Computer Architecture (HPCA) IEEE, pp. 167178. https://doi.org/10.1109/HPCA51647.2021.00024.
- L. Liu, X. Dou, QuCloud+ (2024). “A holistic qubit mapping scheme for single/multi-programming on 2D/3D NISQ quantum computers”. ACM Transactions on Architecture and Code Optimization 21: 1, 127. https://doi.org/10.1145/3631525.
- M. Bandic, C. G. Almudever, S. Feld (2023). “Interaction graph-based characterization of quantum benchmarks for improving quantum circuit mapping techniques”. Quantum Machine Intelligence 5, 130. https://doi.org/10.1007/s42484-023-00124-1.
- K. Li, D. Qiu, L. Li, S. Zheng, Z. Rong (2017). “Application of distributed semi-quantum computing model in phase estimation”. Information Processing Letters 120, 23–29. https://doi.org/10.1016/j.ipl.2016.12.002.
- T. Tanaka, Y. Suzuki, S. Uno, R. Raymond, T. Onodera, N. Yamamoto (2021). “Amplitude estimation via maximum likelihood on noisy quantum computer”. Quantum Information Processing 20, 1–29. https://doi.org/10.1007/s11128-021-03215-9.
- K. Zhang, P. Rao, K. Yu, H. Lim, V. Korepin (2021). “Implementation of efficient quantum search algorithms on NISQ computers”. Quantum Information Processing 20, 233. https://doi.org/10.1007/s11128-021-03165-2.
- G. Park, K. Zhang, K. Yu, V. Korepin (2023). “Quantum multi-programming for Grover’s search”. Quantum Information Processing 22, 1. https://doi.org/10.1007/s11128-022-03793-2.
- P. Das, S. S. Tannu, P. J. Nair, M. Qureshi (2019). “A case for multiprogramming quantum computers”, in Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture pp. 291–303. https://doi.org/10.1145/3352460.3358287.
- S. Niu, A. Todri-Sanial (2023). “Enabling multi-programming mechanism for quantum computing in the NISQ era”. Quantum 7. https://doi.org/10.22331/q-2023-02-16-925.
- A. Ash-Saki, M. Alam, S. Ghosh (2020). “Analysis of crosstalk in NISQ devices and security implications in multi-programming regime”, in Proceedings of the ACM/IEEE International Symposium on Low Power Electronics and Design pp. 2530. https://doi.org/10.1145/3370748.3406570.
- Y. Ohkura (2021). Crosstalk-aware NISQ Multi-programming Bachelor’s thesis, Faculty Policy Manage., Keio Univ., Tokyo, Japan (2021).
- R. Parekh, A. Ricciardi, A. Darwish, and S. DiAdamo (2021). “Quantum algorithms and simulation for parallel and distributed quantum computing”, in 2021 IEEE/ACM Second International Workshop on Quantum Computing Software (QCS) pp. 9–19. https://doi.ieeecomputersociety.org/10.1109/QCS54837.2021.00005.
- P. Andres-Martinez, C. Heunen (2019). “Automated distribution of quantum circuits via hypergraph partitioning”. Physical Review A 100: 3, 032308. https://doi.org/10.1103/PhysRevA.100.032308.
- A. Pastor, P. Escofet, S. B. Rached, E. Alarcon, P. Barlet-Ros, S. Abadal (2024). “Circuit partitioning for multi-core quantum architectures with deep reinforcement learning”. arXiv.2401.17976 https://doi.org/10.48550/arXiv.2401.17976.
- Y. Akhremtsev, T. Heuer, P. Sanders, S. Schlag (2017). “Engineering a direct k-way hypergraph partitioning algorithm”. Proceedings of the Meeting on Algorithm Engineering and Experiments (ALENEX) SIAM, pp. 28–42. https://doi.org/10.1137/1.9781611974768.3.
- S. Schlag, T. Heuer, L. Gottesburen, Y. Akhremtsev, C. Schulz, P. Sanders (2023). “High-quality hypergraph partitioning”. ACM Journal of Experimental Algorithmics 27, 1–39. https://doi.org/10.1145/3529090.
- J.-Y. Wu, K. Matsui, T. Forrer, A. Soeda, P. Andres-Martinez, D. Mills et al (2023). “Entanglement-efficient bipartite-distributed quantum computing”. Quantum 7, 1196. https://doi.org/10.22331/q-2023-12-05-1196.
- P. Andres-Martinez, T. Forrer, D. Mills, J.-Y. Wu, L. Henaut, K. Yamamoto et al (2023). “Distributing circuits over heterogeneous, modular quantum computing network architectures”. Quantum Science and Technology 9, 4. https://doi.org/10.1088/2058-9565/ad6734.
- R. G. Sundaram, H. Gupta, C. R. Ramakrishnan (2021). “Efficient distribution of quantum circuits”, in 35th Int. Symp. on Distributed Computing (DISC) 209 of Leibniz International Proceedings in Informatics (LIPIcs). https://doi.org/10.4230/LIPIcs.DISC.2021.41.
- R. G. Sundaram, H. Gupta, C. R. Ramakrishnan (2022). “Distribution of quantum circuits over general quantum networks”, in 2022 IEEE Int. Conf. on Quantum Computing and Engineering (QCE) pp. 415–425. https://doi.ieeecomputersociety.org/10.1109/QCE53715.2022.00063.
- R. G. Sundaram, H. Gupta (2023). “Distributing quantum circuits using teleportations”, in 2023 IEEE Int. Conf. on Quantum Software (QSW) pp. 186–192. https://doi.ieeecomputersociety.org/10.1109/QSW59989.2023.00030.
- Z. Davarzani, M. Zomorodi-Moghadam, M. Houshmand, M. Nouri-Baygi (2020). “A dynamic programming approach for distributing quantum circuits by bipartite graphs”. Quantum Information Processing 19, 9. https://doi.org/10.1007/s11128-020-02871-7.
- B. W. Kernighan, S. Lin (1970). “An efficient heuristic procedure for partitioning graphs”. The Bell System Technical Journal 49: 2, 291–307. https://doi.org/10.1002/j.1538-7305.1970.tb01770.x.
- M. Zomorodi-Moghadam, M. Houshmand, M. Houshmand (2018). “Optimizing teleportation cost in distributed quantum circuits”. International Journal of Theoretical Physics 57: 3, 848–861. https://doi.org/10.1007/s10773-017-3618-x.
- M. Houshmand, Z. Mohammadi, M. Zomorodi-Moghadam, M. Houshmand (2020). “An evolutionary approach to optimizing teleportation cost in distributed quantum computation”. International Journal of Theoretical Physics 59: 4. https://doi.org/10.1007/s10773-020-04409-0.
- O. Daei, K. Navi, M. Zomorodi-Moghadam (2020). “Optimized quantum circuit partitioning”. International Journal of Theoretical Physics 59, 12, https://doi.org/3804-3820.
- E. Nikahd, N. Mohammadzadeh, M. Sedighi, M. S. Zamani (2021). “Automated window-based partitioning of quantum circuits”. Physica Scripta 96: 3, 035102. 10.1088/1402-4896/abd57c
- C. M. Fiduccia, R. M. Mattheyses (1982). “A linear-time heuristic for improving network partitions”. Papers on Twenty-five Years of Electronic Design Automation pp. 175–181. https://doi.org/10.1109/DAC.1982.1585498.
- W. Cambiucci, R. Silveira, W. Ruggiero (2023). “Hypergraphic partitioning of quantum circuits for distributed quantum computing”, in 2023 IEEE Int. Conf. on Quantum Computing and Engineering (QCE) IEEE Computer Society, Los Alamitos, CA, USA, pp. 268–269. https://doi.org/10.1109/QCE57702.2023.10237.
- J. Clark, T. Humble, H. Thapliyal (2023). “TDAG: Tree-based directed acyclic graph partitioning for quantum circuits”, in Proceedings of the Great Lakes Symposium on VLSI 2023 GLSVLSI 23, ACM, New York, USA, p. 587–592. https://doi.org/10.1145/3583781.3590234.
- T. Park, C. Y. Lee (1995). “Algorithms for partitioning a graph”. Computers and Industrial Engineering 28: 4, 899909. https://doi.org/10.1016/0360-8352(95)00003-J.
- J. M. Baker, C. Duckering, A. Hoover, F. T. Chong (2020). “Time-sliced quantum circuit partitioning for modular architectures”, in 17th ACM Int. Conf. on Computing Frontiers 2020 Proceedings ACM, pp. 98–107. https://doi.org/10.1145/3387902.3392617.
- P. Escofet, A. Ovide, C. G. Almudever, E. Alarcon, S. Abadal (2023). “Hungarian qubit assignment for optimized mapping of quantum circuits on multi-core architectures”. IEEE Computer Architecture Letters https://doi.org/10.1109/LCA.2023.3318857.
- M. Bandic, L. Prielinger, J. Nublein, A. Ovide, S. Rodrigo, S. Abadal et al (2023). “Mapping quantum circuits to modular architectures with QUBO”. 2023 IEEE Int. Conf. on Quantum Computing and Engineering (QCE) 1, IEEE, pp. 790801. https://doi.org/10.1109/QCE57702.2023.00094.
- A. Ovide, S. Rodrigo, M. Bandic, H. Van Someren, S. Feld, S. Abadal et al. (2023). “Mapping quantum algorithms to multi-core quantum computing architectures”. IEEE Int. Symposium on Circuits and Systems (ISCAS) IEEE, pp. 1–5. https://doi.org/10.1109/ISCAS46773.2023.10181589.
- L. K. Grover (1997). “Quantum telecomputation”. arXiv.quant-ph/9704012 https://doi.org/10. 48550/arXiv.quant-ph/9704012.
- M. Gupta, A. Pathak (2007). “A scheme for distributed quantum search through simultaneous state transfer mechanism”. Annalen der Physik 16: 12, 791797. https://doi.org/10.1002/andp.200710265.
- A. Yimsiriwattana and S. J. Lomonaco (2004). “Distributed quantum computing: A distributed Shor algorithm”, in Quantum Information and Computation II 5436. International Society for Optics and Photonics, pp. 360372. DOI: https://doi.org/10.1117/12.546504.
- M. Ekera, J. Hastad (2017). “Quantum algorithms for computing short discrete logarithms and factoring RSA integers”, in T. Lange, T. Takagi (Eds.), Post-Quantum Cryptography, no. 10346 in LNCS, Springer, pp. 347–363. https://doi.org/10.1007/978-3-319-59879-6 20.
- Z.-Y. Chen, Q. Zhou, C. Xue, X. Yang, G.-C. Guo, and G.-P. Guo (2018). “64-Qubit Quantum Circuit Simulation”. Science Bulletin 63, 964. https://doi.org/10.1016/j.scib.2018.06.007.
- A. Eddins, M. Motta, T. P. Gujarati, S. Bravyi, A. Mezzacapo, C. Hadfield, and S. Sheldon (2022). “Doubling the size of quantum simulators by entanglement forging”. PRX Quantum 3. https://doi.org/10.1103/PRXQuantum.3.010309.
- M. A. Perlin, Z. H. Saleem, M. Suchara, and J. C. Osborn (2021). “Quantum circuit cutting with maximum likelihood tomography”. npj Quantum Information 7: 1, 64. https://doi.org/10.1038/s41534-021-00390-6.
- W. Tang, T. Tomesh, M. Suchara, J. Larson, and M. Martonosi (2021). “CutQC: using small Quantum computers for large Quantum circuit evaluations”, in Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS ’21) 473–486. https://doi.org/10.1145/3445814.3446758.
- Z. H. Saleem, T. Tomesh, M. A. Perlin, P. Gokhale, and M. Suchara (2021). “Divide and conquer for combinatorial optimization and distributed quantum computation”. arXiv:2107.07532 https://doi.org/10.48550/arXiv.2107.07532.
- A. Lowe et al (2023). “Fast quantum circuit cutting with randomized measurements”. Quantum 7, 934. https://doi.org/10.22331/q-2023-03-02-934.
- S. C. Marshall, C. Gyurik, and V. Dunjko (2022). “High dimensional quantum machine learning with small quantum computers”. arXiv:2203.13739 https://doi.org/10.22331/q-2023-08-09-1078.
- C. Piveteau and D. Sutter (2023). “Circuit knitting with classical communication”. IEEE Transactions on Information Theory DOI: 10.1109/TIT.2023.3310797. https://doi.org/10.1109/TIT.2023.3310797.
- C. Tuysuz et al (2021). “Classical splitting of parametrized quantum circuits”. Quantum Machine Intelligence 5, 34. https://doi.org/10.1007/s42484-023-00118-z.
- K. Mitarai and K. Fujii (2021). “Overhead for simulating a non-local channel with local channels by quasiprobability sampling”. Quantum 5, 388. https://doi.org/10.22331/q-2021-01-28-388.
- S. Bravyi, G. Smith, and J. A. Smolin (2016). “Trading classical and quantum computational resources”. Physical Review X 6. https://doi.org/10.1103/PhysRevX.6.021043.
- A. W. Harrow, A. Lowe (2025). “Optimal quantum circuit cuts with application to clustered Hamiltonian simulation”. PRX Quantum 6, 010316. https://doi.org/10.1103/PRXQuantum.6.010316.
- Z. Zhou, Y. Du, X. Tian, D. Tao (2023). “QAOA-in-QAOA: Solving large-scale max-cut problems on small quantum machines”. Physical Review Applied 19: 2, 024027. https://doi.org/10.1103/PhysRevApplied. 19.024027.
- K. Fujii, K. Mizuta, H. Ueda, K. Mitarai, W. Mizukami, Y. O. Nakagawa (2022). “Deep variational quantum eigensolver: A divide-and-conquer method for solving a larger problem with smaller size quantum computers”. PRX Quantum 3. https://doi.org/10.1103/PRXQuantum.3.010346.
- S. C. Marshall, C. Gyurik, V. Dunjko (2023). “High dimensional quantum machine learning with small quantum computers”. Quantum 7. https://doi.org/10.22331/q-2023-08-09-1078.
- D. Gottesman, I. L. Chuang (1999). “Demonstrating the viability of universal quantum computation using teleportation and single-qubit operations”. Nature 402, 6760, 390393. https://doi.org/10.1038/46503.
- Y. F. Huang, X. F. Ren, Y. S. Zhang, L. M. Duan, G. C. Guo (2004). “Experimental teleportation of a quantum controlled-NOT gate”. Physical Review Letters 93. https://doi.org/10.1103/PhysRevLett.93.240501.
- K. S. Chou, J. Z. Blumoff, C. S. Wang, P. C. Reinhold, C. J. Axline, Y. Y. Gao, et al. (2018). “Deterministic teleportation of a quantum gate between two logical qubits”. Nature 561: 7723, 368373. https://doi.org/10.1038/s41586-018-0470-y.
- Y. Wan, D. Kienzler, S. Erickson, K. Mayer, T. Tan, J. Wu, et al (2019). “Quantum gate teleportation between separated qubits in a trapped-ion processor”. Science 364, 875878. https://doi.org/10.1126/science.aaw9415.
- S. Daiss, S. Langenfeld, S. Welte, E. Distante, P. Thomas, L. Hartung, et al (2021). “A quantum-logic gate between distant quantum-network modules”. Science 371: 6529, 614617. https://doi.org/10.1126/science.abe3150.
- C. Qiao, Y. Zhao, G. Zhao, H. Xu (2022). “Quantum data networking for distributed quantum computing: opportunities and challenges”, in Proceedings of the IEEE Conference on Computer Communications Workshop New York, NY, pp. 16. https://doi.org/10.1109/INFOCOMWKSHPS54753.2022.9798138.
- Z. Cho, Y. Son, H. Jeong et al (2022). “A new approach to quantum computing multi-qubit generation and development of quantum computing platform with magnetic resonance imaging techniques”. arXiv:2206.05932 https://doi.org/10.48550/arXiv.2206.05932.
- E. Alarcon, S. Abadal, F. Sebastiano et al (2023). “Scalable multi-chip quantum architectures enabled by cryogenic hybrid wireless/quantum-coherent network-in-package”, in Proceedings of the IEEE International Symposium on Circuits and Systems Monterey, CA, USA, pp. 15. https://doi.org/10.1109/ISCAS46773.2023.10181857.
- Y. Zheng, C. Zhai, D. Liu et al (2023). “Multichip multidimensional quantum networks with entanglement retrievability”. Science 381, 221226. https://doi.org/10.1126/science.adg9210.
- M. Field, A. Chen, B. Scharmann et al (2024). “Modular superconducting-qubit architecture with a multichip tunable coupler”. Physical Review Applied 21, 054063. https://doi.org/10.1103/PhysRevApplied.21.054063.
- J. Stokes, J. Izaac, N. Killoran, G. Carleo (2020). “Quantum natural gradient”. Quantum 4, 269. https://doi.org/10.22331/q-2020-05-25-269.
- M. S. Alvarez-Alvarado, F. E. Alban-Chacon, E. A. Lamilla-Rubio et al (2021). “Three novel quantum inspired swarm optimization algorithms using different bounded potential fields”. Scientific Reports 11: 1, 11655. https://doi.org/10.1038/s41598-021-90847-7.
- D. Wierichs, J. Izaac, C. Wang, C. Y.-Y. Lin (2022). “General parameter-shift rules for quantum gradients”. Quantum, 6, 677. https://doi.org/10.22331/q-2022-03-30-677.
- E. R. Anschuetz, B. T. Kiani (2022). “Quantum variational algorithms are swamped with traps”. Nature Communications, 13: 1, 7760. https://doi.org/10.1038/s41467-022-35364-5.
- D. Failde, J. D. Viqueira, M. Mussa Juane, A. Gomez (2023). “Using differential evolution to avoid local minima in variational quantum algorithms. Scientific Reports, 13: 1 16230. https://doi.org/10.1038/s41598-023-43 404-3.
- J. D. Viqueira, D. Failde, M. M. Juane, A. Gomez, D. Mera (2025). “Density matrix emulation of quantum recurrent neural networks for multivariate time series prediction”. Machine Learning: Science and Technology, 6, 015023. https://doi.org/10.1088/2632-2153/ad9431
- D. Ferrari, S. Carretta, M. Amoretti (2023). “A modular quantum compilation framework for distributed quantum computing”. IEEE Transactions on Quantum Engineering, 4, 2500213. https://doi.org/10.1109/TQE.2023.3303935.
- O. Mukhanov, B. L. T. Plourde, A. Opremcak et al (2019). “Scalable quantum computing infrastructure based on superconducting electronics”, in Proceedings of the IEEE International Electron Devices Meeting San Francisco, CA, USA, pp. 31.2.131.2.4. https://doi.org/10.1109/IEDM19573.2019.8993634.
- B. Baheri, Q. Guan, S. Xu, V. Chaudhary (2022). “SQCC: Smart quantum circuit cutting”, in Proceedings of the IEEE International Parallel and Distributed Processing Symposium Workshops Lyon, France, pp. 614615. https://doi.org/10.1109/IPDPSW55747.2022.00104.
- K. Smith, M. Perlin, P. Gokhale et al (2023). “Clifford-based circuit cutting for quantum simulation”, in Proceedings of the 50th Annual International Symposium on Computer Architecture Orlando, FL, USA, pp. 113. https://doi.org/10.1145/3579371.3589352.
- E. El-Araby, N. Mahmud, M. J. Jeng et al (2023). “Towards complete and scalable emulation of quantum algorithms on high-performance reconfigurable computers”. IEEE Transactions on Computers, 72, 23502364. https://doi.org/10.1109/TC.2023.3248276.
- E. Andres, M. P. Cuellar, G. Navarro (2023). “Efficient dimensionality reduction strategies for quantum reinforcement learning”. IEEE Access 11, 104534104553. https://doi.org/10.1109/ACCESS.2023.3318173.
- M. Benedetti, E. Lloyd, S. Sack, and M. Fiorentini (2019). “Parameterized quantum circuits as machine learning models”. Quantum Science and Technology, 4. https://doi.org/10.1088/2058-9565/ab4eb5.
- M. Schuld, I. Sinayskiy, and F. Petruccione (2014). “The quest for a quantum neural network”. Quantum Information Processing, 13. https://doi.org/10.1007/s11128-014-0809-8.
- K. Beer, D. Bondarenko, T. Farrelly, T. Osborne, R. Salzmann, D. Scheiermann, and R. Wolf. (2020). “Training deep quantum neural networks”. Nature Communications, 11, 808. https://doi.org/10.1038/s41467-020-14 454-2.
- M. Cerezo, A. Arrasmith, R. Babbush, S. C. Benjamin, S. Endo, K. Fujii, J. R. McClean, K. Mitarai, X. Yuan, L. Cincio, et al (2021). “Variational quantum algorithms”. Nature Reviews Physics, 3, 625. https://doi.org/10.1038/s42254-021-00348-9.
- S. Mangini, F. Tacchino, D. Gerace, D. Bajoni, and C. Macchiavello (2021). “Quantum computing models for artificial neural networks”. Europhysics Letters, 134, 10002. https://doi.org/10.1209/0295-5075/134/10002.
- A. Kandala, A. Mezzacapo, K. Temme, M. Takita, M. Brink, J. M. Chow, and J. M. Gambetta (2017). “Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets”. Nature, 549, 242. https://doi.org/10.1038/nature23879.
- R. Sweke, F. Wilde, J. Meyer, M. Schuld, P. K. Faehrmann, B. Meynard-Piganeau, and J. Eisert (2020). “Stochastic gradient descent for hybrid quantum-classical optimization”. Quantum, 4, 314. https://doi.org/10. 22331/q-2020-08-31-314.
- J. Biamonte et al (2017). “Quantum machine learning”. Nature 549, 195–202. https://doi.org/10.1038/nature23474.
- Z.-C. Yang et al (2017). “Optimizing variational quantum algorithms using Pontryagin’s minimum principle”. Physical Review X, 7, 021027. https://doi.org/10.1103/PhysRevX.7.021027.
- Y.-H. Oh et al (2019). “Solving multi-coloring combinatorial optimization problems using hybrid quantum algorithms”. arXiv:1911.00595 https://doi.org/10.48550/arXiv.1911.00595.
- J. Biamonte (2021). “Universal variational quantum computation”. Physical Review A 103: 3, L030401. https://doi.org/10.1103/PhysRevA.103.L030401.
- V. Havlicek et al (2019). “Supervised learning with quantum-enhanced feature spaces”. Nature 567: 7747, 209–212. https://doi.org/10.1038/s41586-019-0980-2.
- A. Manzano et al (2023). “Parametrized quantum circuits and their approximation capacities in the context of quantum machine learning”. arXiv:2307.14792 https://doi.org/10.48550/arXiv.2307.14792.
- V. Dunjko, J. M. Taylor, and H. J. Briegel (2016). “Quantum-enhanced machine learning”. Physical Review Letters, 117. https://doi.org/10.1103/PhysRevLett.117.130501.
- L. Pira (2024). The Fundamentals of Quantum Neural Networks. PhD Dissertation. University of Technology Sydney.
- E. Tang (2019). “A quantum-inspired classical algorithm for recommendation systems”, in Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing https://doi.org/10.1145/3313276.3316310.
- S. Lloyd, M. Mohseni, and P. Rebentrost (2013). “Quantum algorithms for supervised and unsupervised machine learning”. arXiv:1307.0411 https://doi.org/10.48550/arXiv.1307.0411.
- V. Giovannetti, S. Lloyd, and L. Maccone (2008). “Architectures for a quantum random access memory”. Physical Review A, 78. https://doi.org/10.1103/PhysRevA.78.052310.
- V. Giovannetti, S. Lloyd, and L. Maccone (2008). “Quantum random access memory”. Physical Review Letters, 100. https://doi.org/10.1103/PhysRevLett.100.160501.
- S. Arunachalam, V. Gheorghiu, T. Jochym-O’Connor, M. Mosca, and P. V. Srinivasan (2015). “On the robustness of bucket brigade quantum RAM”. New Journal of Physics, 17, 123010. https://doi.org/10.1088/1367-2630/17/12/123010.
- L. Gyongyosi and S. Imre (2019). “Training optimization for gate-model quantum neural networks”. Scientific Reports DOI: 10.1038/s41598-019-48892-w. https://doi.org/10.1038/s41598-019-48892-w.
- G. Carleo, I. Cirac, K. Cranmer, L. Daudet, M. Schuld, N. Tishby, L. Vogt-Maranto, and L. Zdeborova (2019). “Machine learning and the physical sciences”. Reviews of Modern Physics, 91. https://doi.org/10.1103/RevModPhys.91.045002.
- A. Dawid, J. Arnold, B. Requena, A. Gresch, M. P lodzien, K. Donatella, K. A. Nicoli, P. Stornati, R. Koch, M. Buttner, et al (2022). “Modern applications of machine learning in quantum sciences”. arXiv:2204.04198 https://doi.org/10.48550/arXiv.2204.04198.
- M. Bukov, A. G. R. Day, D. Sels, P. Weinberg, A. Polkovnikov, and P. Mehta (2018). “Reinforcement learning in different phases of quantum control”. Physical Review X, 8. https://doi.org/10.1103/PhysRevX.8.031086.
- M. Y. Niu, S. Boixo, V. N. Smelyanskiy, and H. Neven (2019). “Universal quantum control through deep reinforcement learning”. npj Quantum Information, 5, 1. https://doi.org/10.1038/s41534-019-0141-3.
- H. P. Nautrup, N. Delfosse, V. Dunjko, H. J. Briegel, and N. Friis (2019). “Optimizing quantum error correction codes with reinforcement learning”. Quantum, 3, 215. https://doi.org/10.22331/q-2019-12-16-215.
- G. Torlai and R. G. Melko (2017). “A neural decoder for topological codes”. Physical Review Letters, 119, 030501. https://doi.org/10.1103/PhysRevLett.119.030501.
- G. Torlai, G. Mazzola, J. Carrasquilla, M. Troyer, R. Melko, and G. Carleo (2018). “Many-body quantum state tomography with neural networks”. Nature Physics, 14. https://doi.org/10.1038/s41567-018-0048-5.
- Q. Xu and S. Xu (2018). “Neural network state estimation for full quantum state tomography. arXiv:1811.06654 https://doi.org/10.1103/PhysRevA.106.012409.
- G. Sentis, A. Monras, R. Munoz Tapia, J. Calsamiglia, and E. Bagan (2019). “Unsupervised classification of quantum data”. Physical Review X, 9, 041029. https://doi.org/10.1103/PhysRevX.9.041029.
- N. Liu and P. Rebentrost (2018). “Quantum machine learning for quantum anomaly detection”. Physical Review A, 97, 042315. https://doi.org/10.1103/PhysRevA.97.042315.
- S. Wang et al (2021). “Noise-induced barren plateaus in variational quantum algorithms”. Nature Communications 12: 1, 6961. https://doi.org/10.1038/s41467-021-27045-6.
- J. R. McClean, S. Boixo, V. N. Smelyanskiy, R. Babbush, and H. Neven (2018). “Barren plateaus in quantum neural network training landscapes”. Nature Communications, 9. https://doi.org/10.1038/s41467-018-07090-4.
- M. Cerezo, A. Sone, T. Volko, L. Cincio, and P. J. Coles (2021). “Cost function dependent barren plateaus in shallow parametrized quantum circuits”. Nature Communications, 12: 1. https://doi.org/10.1038/s41467-021-21728-w.
- A. Arrasmith et al (2022). “Equivalence of quantum barren plateaus to cost concentration and narrow gorges”. Quantum Science and Technology 7: 4, 045015. https://doi.org/10.1088/2058-9565/ac7d06.
- M. Cerezo et al (2022). “Challenges and opportunities in quantum machine learning”. Nature Computational Science 2: 9, 567–576. https://doi.org/10.1038/s43588-022-00311-3.
- S. Herbst (2024). “Beyond 0s and 1s: Exploring the impact of noise, data encoding, and hyperparameter optimization in quantum machine learning”. Diploma Thesis Technische Universitat Wien.
- D. Suchan (2024). “Hyperparameter tuning for quantum machine learning”. Diploma Thesis Technische Universitat Wien.
- L. Alchieri et al (2021). “An introduction to quantum machine learning: from quantum logic to quantum deep learning”. Quantum Machine Intelligence 3: 2, 28. https://doi.org/10.1007/s42484-021-00056-8.
- N. Wiebe (2020). “Key questions for the quantum machine learner to ask themselves”. New Journal of Physics, 22, 091001. https://doi.org/10.1088/1367-2630/abac39
- D. Ventura and T. Martinez (2000). “Quantum associative memory”. Information Sciences, 126, 273. https://doi.org/10.1016/S0020-0255(99)00101-2.
- R. LaRose and B. Coyle (2020). “Robust data encodings for quantum classifiers”. Physical Review A, 102. https://doi.org/10.1103/PhysRevA.102.032420.
- M. Schuld and F. Petruccione (2018). Supervised Learning with Quantum Computers Springer, DOI: 10.1007/978-3-319-96424-9.
- H.-Y. Huang, M. Broughton, M. Mohseni, R. Babbush, S. Boixo, H. Neven, and J. R. McClean (2021). “Power of data in quantum machine learning”. Nature Communications, 12. https://doi.org/10.1038/s41467-021-22 539-9.
- M. Schuld and N. Killoran (2019). “Quantum machine learning in feature Hilbert spaces”. Physical Review Letters, 122, 040504. https://doi.org/10.1103/PhysRevLett.122.040504.
- A. Skolik, J. R. McClean, M. Mohseni, P. van der Smagt, and M. Leib (2021). “Layerwise learning for quantum neural networks”. Quantum Machine Intelligence, 3, 1. https://doi.org/10.1007/s42484-020-00036-4.
- T. Haug, C. N. Self, and M. S. Kim (2023). “Quantum machine learning of large datasets using randomized measurements”. Machine Learning: Science and Technology, 4 015005. https://doi.org/10.1088/2632-2153/acb0b4.
- Y. Du, Y. Qian, and D. Tao (2021). “Accelerating variational quantum algorithms with multiple quantum processors”. arXiv:2106.12819 https://doi.org/10.48550/arXiv.2106.12819.
- C. Cade, L. Mineh, A. Montanaro, and S. Stanisic (2020). “Strategies for solving the Fermi-Hubbard model on near-term quantum computers”. Physical Review B, 102. https://doi.org/10.1103/PhysRevB.102.235122.
- R. Wiersema, C. Zhou, Y. de Sereville, J. F. Carrasquilla, Y. B. Kim, and H. Yuen (2020). “Exploring entanglement and optimization within the Hamiltonian Variational Ansatz”. PRX Quantum, 1. https://doi.org/10.1103/PRXQuantum.1.020319.
- M. Weigold, J. Barzen, F. Leymann, and M. Salm (2020). “Data encoding patterns for quantum computing” in Proceedings of the 27th Conference on Pattern Languages of Programs https://doi.org/10.5555/3511065.3511068.
- M. Weigold, J. Barzen, F. Leymann, and M. Salm (2021). “Expanding data encoding patterns for quantum algorithms”, in 2021 IEEE 18th International Conference on Software Architecture Companion (ICSA-C) pp. 95–101. https://doi.org/10.1109/ICSA-C52384.2021.00025.
- M. Schuld, M. Fingerhuth and F. Petruccione (2017). “Implementing a distance-based classifier with a quantum interference circuit”. Europhysics Letters, 119, 60002. https://doi.org/10.1209/0295-5075/119/60002
- J. Romero, J.P. Olson, and A. Aspuru-Guzik (2017). “Quantum autoencoders for efficient compression of quantum data”. Quantum Science and Technology, 2, 045001. https://doi.org/10.1088/2058-9565/aa8072.
- M. H. Amin, E. Andriyash, J. Rolfe, B. Kulchytskyy, and R. Melko (2018). “Quantum Boltzmann Machine”. Physical Review X, 8, 021050. https://doi.org/10.1103/PhysRevX.8.021050.
- R. Moretti, A. Giachero, V. Radescu, M. Grossi (2024). “Enhanced feature encoding and classification on distributed quantum hardware”. arXiv:2412.01664 https://doi.org/10.48550/arXiv.2412.01664.
- T. Tomesh, P. Gokhale, E. R. Anschuetz, and F. T. Chong (2021). “Coreset clustering on small quantum computers”. Electronics, 10. https://doi.org/10.3390/electronics10141690.
- A. W. Harrow (2020). “Small quantum computers and large classical data sets”. arXiv:2004.00026 https://doi.org/10.48550/arXiv.2004.00026.
- C. N. Self, K. E. Khosla, A. W. R. Smith, F. Sauvage, P. D. Haynes, J. Knolle, F. Mintert, and M. S. Kim (2021). “Variational quantum algorithm with information sharing”. npj Quantum Information, 7. https://doi.org/10.1038/s41534-021-00452-9.
- Y. Du, Y. Qian, X. Wu, D. Tao (2022). “A distributed learning scheme for variational quantum algorithms”. IEEE Transactions on Quantum Engineering, 3, 116. https://doi.org/10.1109/TQE.2022.3175267.
- J. F. Fitzsimons (2017). “Private quantum computation: an introduction to blind quantum computing and related protocols”. npj Quantum Information 3, 23. https://doi.org/10.1038/s41534-017-0025-3
- R. Kaewpuang, M. Xu, D. T. Hoang et al (2023). “Elastic entangled pair and qubit resource management in quantum cloud computing”. arXiv:2307.13185 https://doi.org/10.48550/arXiv.2307.13185.
- M. A. Serrano, L. E. Sanchez, A. Santos-Olmo et al (2023). “Minimizing incident response time in realworld scenarios using quantum computing”. Software Quality Journal 32, 163192. https://doi.org/10.1007/s11219-023-09632-6.
- P. J. Karalekas, T. Ryan, R. S. Smith (2020). “A quantum-classical cloud platform optimized for variational hybrid algorithms”. Quantum Science and Technology 5, 2. https://doi.org/10.1088/2058-9565/ab7559.
- Y. Ding and A. Javadi-Abhari. (2022). “Quantum and post-Moore’s law computing”. IEEE Internet Computing 26: 1, 56. https://doi.org/10.1109/MIC.2021.3133675.
- D. Subhi and L. Bacsardi (2023). “Using quantum nodes connected via the quantum cloud to perform IOT quantum network”. Condensed Matter 8: 1, p. 24. https://doi.org/10.3390/condmat8010024.
- IBM Quantum Computing Services, IBM (2023). https://quantum-computing.ibm.com/
- IBM Cloud, IBM (2024). https://www.ibm.com/cloud
- Google Cloud, Google (2024). Google Quantum AI. https://quantumai.google/
- IonQ (2022). IonQ Cloud Service. https://ionq.com/
- Quantinuum Cloud Service, Quantinuum (2022). https://www.quantinuum.com/.
- Pasqal: Programmable Atomic Arrays (2024). https://www.pasqal.com/.
- Azure Quantum, Microsoft (2023). https://azure.microsoft.com/en-us/services/quantum
- QuEra, Quantum Computing with Neutral Atoms (2024). https://www.quera.com/
- C. Gonzalez (2021). “Cloud based QC with Amazon Braket”. Digitale Welt 5: 2, 14–17. https://doi.org/10.1007/s42354-021-0330-z.
- Amazon Braket, Amazon (2024). https://aws.amazon.com/braket/
- PlanQK (2020). https://www.planqk.de/
- C. Linnhoff-Popien (2020). “PlanQK: Quantum computing meets artificial intelligence: How to make an ambitious idea reality”. Digitale Welt 4: 2, 28–35. https://doi.org/10.1007/s42354-020-0257-9.
- Quantum Inspire, De Voorhoede (2024). https://www.quantum-inspire.com/
- QuantumPath. https://www.quantumpath.es/
- J. L. Hevia, G. Peterssen, M. Piattini (2021). “QuantumPath: A quantum software development platform”. Software: Practice and Experience 1–14. https://doi.org/10.1002/spe.3064.
- Strangeworks Quantum Computing Platform, Strangeworks (2022). https://strangeworks.com/
- QFaaS, Github (2022). https://github.com/Cloudslab/qfaas
- H. T. Nguyen, M. Usman, and R. Buyya (2024). “QFaaS: A serverless function-as-a-service framework for quantum computing”. Future Generation Computer Systems 154, 281–300. https://doi.org/10.1016/j.future.2024.01.018.
- 1Qloud Optimization Platform, 1QBit (2024). https://1qbit.com/1qloud/
- Qemist Cloud, Good Chemistry Co (2024). https://goodchemistry.com/qemist-cloud/
- N. Aslam, H. Zhou, E. K. Urbach, M. J. Turner, R. L. Walsworth, M. D. Lukin, and H. Park (2023). “Quantum sensors for biomedical applications. Nature Reviews Physics 5: 3, 157–169. https://doi.org/10.1038/s42254-023-00558-3.
- L. Thiel, D. Rohner, M. Ganzhorn, P. Appel, E. Neu, B. Muller, R. Kleiner, D. Koelle, and P. Maletinsky (2016). “Quantitative nanoscale vortex imaging using a cryogenic quantum magnetometer”. Nature Nanotechnology, 11: 8, 677–681. https://doi.org/10.1038/nnano.2016.63.
- H. M. J. Boffin, G. Hussain, J.-P. Berger, and L. Schmidtobreick (2016). “Astronomy at high angular resolution”. Astrophysics and Space Science Library 439. https://doi.org/10.1007/978-3-319-39739-9.
- Y. Zhang et al (2021). “QED driven QAOA for networkflow optimization”. Quantum 5: 510. https://doi.org/10.22331/q-2021-07-27-510.
- B. Chevalier, W. Roga, M. Takeoka (2024). “Compressed sensing enhanced by a quantum approximate optimization algorithm”. Physical Review A 110, 062410. https://doi.org/10.1103/PhysRevA.110.062410.
- J. Li, M. Alam, and S. Ghosh (2023). “Large-scale quantum approximate optimization via divide-and-conquer”. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 42: 6, 18521860. https://doi.org/10.1109/TCAD.2022.3212196.
- G. D. Kahanamoku-Meyer et al (2022). “Classically verifiable quantum advantage from a computational Bell test”. Nature Physics 18: 8, 918–924. https://doi.org/10.1038/s41567-022-01643-7.
- Z. Brakerski et al (2021). “A cryptographic test of quantumness and certifiable randomness from a single quantum device”. Journal of the ACM (JACM) 68: 5, 1–47. https://doi.org/10.1145/3441309.
- H. R. Grimsley et al (2019). “An adaptive variational algorithm for exact molecular simulations on a quantum computer”. Nature Communications 10: 1, 3007. https://doi.org/10.1038/s41467-019-10988-2.
- Y.-F. Niu, S. Zhang, C. Ding, W.-S. Bao, and H.-L. Huang (2023). “Parameter-parallel distributed variational quantum algorithm”. SciPost Physics 14, 5. https://doi.org/10.21468/SciPostPhys.14.5.132.
- D. Bluvstein et al (2024). “Logical quantum processor based on reconfigurable atom arrays”. Nature 626: 7997, 58–65. https://doi.org/10.1038/s41586-023-06927-3.
- J. Bartusek et al (2023). “Obfuscation of pseudo-deterministic quantum circuits”, in Proceedings of the 55th Annual ACM Symposium on Theory of Computing 1567–1578. https://doi.org/10.1145/3564246.3585179
- J. Rajakumar et al (2024). “Polynomial-time classical simulation of noisy IQP circuits with constant depth”. arXiv:2403.14607 https://doi.org/10.48550/arXiv.2403.14607.
- S. Chen, J. Cotler, H.-Y. Huang, and J. Li (2023). “The complexity of NISQ”. Nature Communications 14, 6001. https://doi.org/10.1038/s41467-023-41217-6.
- V. Dunjko and H. J. Briegel (2018). “Machine learning and artificial intelligence in the quantum domain”. Reports on Progress in Physics 81, 074001. https://doi.org/10.1088/1361-6633/aab406
- J. A. Cortese, Timothy M. Braje (2018). “Loading classical data into a quantum computer”. arXiv:1803.01958 https://doi.org/10.48550/arXiv.1803.01958.
- Y. Cao, G. G. Guerreschi, and A. Aspuru-Guzik (2017). “Quantum neuron: An elementary building block for machine learning on quantum computers”. arXiv:1711.11240 https://doi.org/10.48550/arXiv.1711.11240.
- K. Mitarai, M. Negoro, M. Kitagawa, and K. Fujii (2018). “Quantum circuit learning”. Physical Review A 98. https://doi.org/10.1103/PhysRevA.98.032309.
- J. Allcock, C.-Y. Hsieh, I. Kerenidis, and S. Zhang (2020). “Quantum algorithms for feedforward neural networks”. ACM Transactions on Quantum Computing, 1. https://doi.org/10.1145/3411466.
- M. Schuld, A. Bocharov, K. M. Svore, and N. Wiebe (2020). “Circuit-centric quantum classifiers”. Physical Review A, 101. https://doi.org/10.1103/PhysRevA.101.032308.
- Q.-X. Xiea, Y. Zhaobc (2024). “A novel quantum-classical hybrid algorithm for determining eigenstate energies in quantum chemistry”. arXiv:2406.00296 https://doi.org/10.48550/arXiv.2406.00296.
- C. C. Hian (2024). Resource-Efficient Quantum Algorithms for Quantum Chemistry. PhD Dissertation, National University of Singapore.
- J. Bausch (2018). “Classifying data using near-term quantum devices”. International Journal of Quantum Information, 16, 8. https://doi.org/10.1142/S0219749918400014.
- M. L. Wall et al (2021). “Generative machine learning with tensor networks: Benchmarks on near-term quantum computers”. Physical Review Research, 3: 2. https://doi.org/10.1103/PhysRevResearch.3.023010.
- B. Coecke et al (2020). “Foundations for near-term quantum natural language processing”. arXiv:2012.03755 https://doi.org/10.48550/arXiv.2012.03755.
- M. Alam et al (2022). “QNet: A scalable and noise-resilient quantum neural network architecture for noisy intermediate-scale quantum computers”. Frontiers in Physics 9. https://doi.org/10.3389/fphy.2021.755139.
- S. Lloyd and C. Weedbrook (2018). “Quantum generative adversarial learning”. Physical Review Letters, 121. https://doi.org/10.1103/PhysRevLett.121.040502.
- P.-L. Dallaire-Demers and N. Killoran (2018). “Quantum generative adversarial networks”. Physical Review A, 98, 012324. https://doi.org/10.1103/PhysRevA.98.012324.
- C. Zoufal, A. Lucchi, and S. Woerner (2019). “Quantum generative adversarial networks for learning and loading random distributions”. npj Quantum Information, 5. https://doi.org/10.1038/s41534-019-0223-2.
- M. Henderson, S. Shakya, S. Pradhan, and T. Cook (2019). “Quanvolutional neural networks: powering image recognition with quantum circuits”. arXiv:1904.04767 https://doi.org/10.48550/arXiv.1904.04767.
- J. Bausch (2020). “Recurrent quantum neural networks”, in H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin (eds), Advances in Neural Information Processing Systems Curran Associates, Inc., 33, pp. 1368–1379. https://doi.org/10.48550/arXiv.2006.14619.
- I. Kerenidis, J. Landman, and A. Prakash (2020). “Quantum algorithms for deep convolutional neural networks”. arXiv:1911.01117 https://doi.org/10.48550/arXiv.1911.01117.
- I. Cong, S. Choi, and M. D. Lukin (2019). “Quantum convolutional neural networks”. Nature Physics, 15, 1273. https://doi.org/10.1038/s41567-019-0648-8.
- E. Grant, L. Wossnig, M. Ostaszewski, and M. Benedetti (2019). “An initialization strategy for addressing barren plateaus in parametrized quantum circuits”. Quantum 3. https://doi.org/10.22331/q-2019-12-09-214.
- A. Pesah, M. Cerezo, S. Wang, T. Volko, A. T. Sornborger, and P. J. Coles (2021). “Absence of barren plateaus in quantum convolutional neural networks”. Physical Review X 11, 041011. https://doi.org/10.1103/PhysRevX.11.041011.
- Y. Ding, L. Lamata, M. Sanz, X. Chen, and E. Solano (2019). “Experimental implementation of a quantum autoencoder via quantum adders”. Advanced Quantum Technologies 2, 1800065. https://doi.org/10.1002/qute.201800065.
- A. Rocchetto, S. Aaronson, S. Severini, G. Carvacho, D. Poderini, I. Agresti, M. Bentivegna, and F. Sciarrino (2019). “Experimental learning of quantum states”. Science Advances 5, eaau1946. https://doi.org/10.1126/sciadv.aau1946.
- B. Coyle, D. Mills, V. Danos, and E. Kashe (2020). “The born supremacy: Quantum advantage and training of an Ising Born Machine”. npj Quantum Information 6, 1. https://doi.org/10.1038/s41534-020-00 288-9.
- M. Benedetti, D. Garcia-Pintos, O. Perdomo, V. Leyton-Ortega, Y. Nam, and A. Perdomo-Ortiz (2019). “A generative modeling approach for benchmarking and training shallow quantum circuits”. npj Quantum Information 5, 1. https://doi.org/10.1038/s41534-019-0157-8.
- F. Tacchino, C. Macchiavello, D. Gerace, and D. Bajoni (2019). “An artificial neuron implemented on an actual quantum processor”. npj Quantum Information 5, 1. https://doi.org/10.1038/s41534-019-0140-4.
- E. Grant, M. Benedetti, S. Cao, A. Hallam, J. Lockhart, V. Stojevic, A. G. Green, and S. Severini (2018). “Hierarchical quantum classifiers”. npj Quantum Information 4, 1. https://doi.org/10.1038/s41534-018-0116-9.
- D. Riste, M. P. Da Silva, C. A. Ryan, A. W. Cross, A. D. Corcoles, J. A. Smolin, J. M. Gambetta, J. M. Chow, and B. R. Johnson (2017). “Demonstration of quantum advantage in machine learning”. npj Quantum Information 3, 1. https://doi.org/10.1038/s41534-017-0017-3.
- J. S. Otterbach, R. Manenti, N. Alidoust, A. Bestwick, M. Block, B. Bloom, S. Caldwell, N. Didier, E. S. Fried, S. Hong, et al (2017). “Unsupervised machine learning on a hybrid quantum computer”. arXiv:1712.05771 https://doi.org/10.48550/arXiv.1712.05771.
- B. Heim, M. Soeken, S. Marshall, C. Granade, M. Roetteler, A. Geller, M. Troyer, and K. Svore (2020). “Quantum programming languages”. Nature Reviews Physics 2: 12, 709–722. https://doi.org/10.1038/s42254-020-00245-7.
- A. Tavakoli, A. Pozas-Kerstjens, P. Brown, M. Araujo (2024). “Semidefinite programming relaxations for quantum correlations”. Reviews of Modern Physics, 96, 045006. https://doi.org/10.1103/RevModPhys.96.045006.
- R. LaRose (2019). “Overview and comparison of gate level quantum software platforms”. Quantum 3, 130. https://doi.org/10.22331/q-2019-03-25-130.
- M. Fingerhuth, T. Babej, and P. Wittek (2018). “Open source software in quantum computing”. PloS One 13: 12, e0208561. https://doi.org/10.1371/journal.pone.0208561.
- J. Zhao (2020). “Quantum software engineering: landscapes and horizons”. arXiv:2007.07047 https://doi.org/10.48550/arXiv.2007.07047.
- J. M. Murillo et al. (2024). “Quantum software engineering: roadmap and challenges ahead”. ACM Transactions on Software Engineering and Methodology https://doi.org/10.1145/3712002.
- J. Ray. (2022). “China at the nexus of AI and quantum computing”. Chinese Power and Artificial Intelligence: Perspectives and Challenges 155–172.
- M. Ying, Y. Feng, R. Duan et al (2012). “Quantum programming: From theories to implementations”. Chinese Science Bulletin 57, 1903–1909. https://doi.org/10.1007/s11434-012-5147-6.
- I. Kumara, W. J. Van Den Heuvel, D. A. Tamburri (2021). “Qsoc: Quantum service-oriented computing”, in Symposium and Summer School on Service-Oriented Computing 5263. https://doi.org/10.1007/978-3-030-87568-8 3.
- J. Romero-Alvarez, J. Alvarado-Valiente, E. Moguel, J. Garcia-Alonso (2023). “Quantum web services: Development and deployment”, in Web Engineering Springer, Alicante, Spain, pp. 421–423. https://doi.org/10.1007/978-3-031-34444-239.
- V. Bergholm, J. Izaac, M. Schuld, C. Gogolin, S. Ahmed, V. Ajith, M. S. Alam, G. Alonso-Linaje, B. Akash Narayanan, A. Asadi, et al (2018). “PennyLane: Automatic differentiation of hybrid quantum-classical computations”. arXiv:1811.04968 https://doi.org/10.48550/arXiv.1811.04968.
- G. Aleksandrowicz, T. Alexander, P. Barkoutsos, L. Bello, Y. Ben-Haim, D. Bucher, F. J. Cabrera-Hernandez, J. Carballo-Franquis, A. Chen, C.-F. Chen, et al (2019). “Qiskit: An open-source framework for quantum computing”. https://doi.org/10.5281/zenodo.2562111.
- H. Nagarajan, O. Lockwood and C. Coffrin (2021). “QuantumCircuitOpt: An open-source framework for provably optimal quantum circuit design”. arXiv:2111.11674 https://doi.org/10.48550/arXiv.2111.11674.
- QuPanda: an open source quantum computing framework (2018). “Origin quantum”, https://github.com/OriginQ/QPanda-2
- S. Diadamo, J. Notzel, B. Zanger, and M. M. Bese (2021). “Distributed quantum computing and network control for accelerated VQE”. IEEE Transactions on Quantum Engineering, 2, 1. https://doi.org/10.1109/TQE.2021.3057908.
- T. Chatterjee, A. Das, S. I. Mohtashim, A. Saha, A. Chakrabarti (2022). “Qurzon: A prototype for a divide and conquer-based quantum compiler for distributed quantum systems”. SN Computer Science, 3, 4. https://doi.org/10.1007/s42979-022-01207-9.
- W. Tang, M. Martonosi, ScaleQC (2022). “A scalable framework for hybrid computation on quantum and classical processors”. arXiv.2207.00933. https://doi.org/10.48550/arXiv.2207.00933.
- K. N. Smith, M. A. Perlin, P. Gokhale, P. Frederick, D. Owusu-Antwi, R. Rines et al. (2023). “Clifford-based circuit cutting for quantum simulation”. Proc. of the 50th Annual Int. Symp. on Computer Architecture (ISCA’23) ACM, New York, pp. 113. https://doi.org/10.1145/3579371.3589352.
- “TorchQuantum: Fast library for quantum computing in PyTorch”. (2022). https://hanruiwanghw.wixsite. com/torchquantum
- M. Broughton, G. Verdon, T. McCourt, A. J. Martinez, J. H. Yoo, S. V. Isakov, P. Massey, R. Halavati, M. Y. Niu, A. Zlokapa, et al (2020). “TensorFlow quantum: A software framework for quantum machine learning”. arXiv:2003.02989 https://doi.org/10.48550/arXiv.2003.02989.
- C. Xing and M. Broughton (2021). “Training with multiple workers using tensor flow quantum”. https://blog.tensorflow.org.
- N. N. Hegade, N. L. Kortikar, B. Das, B. K. Behera, and P. K. Panigrahi (2017). “Experimental demonstration of quantum tunneling in IBM quantum computer”. arXiv:1712.07326 https://doi.org/10.48550/arXiv.1712.07326.
- H. Norlen (2020). Quantum Computing in Practice with Qiskit and IBM Quantum Experience: Practical Recipes for Quantum Computer Coding at the Gate and Algorithm Level with Python. Packt Publishing Ltd.
- D. Fortunato, J. Campos, and R. Abreu (2022). “Mutation testing of quantum programs: A case study with qiskit”. IEEE Transactions on Quantum Engineering 3, 1–17. https://doi.org/10.1109/TQE.2022.3195061.
- Pyquil, Rigetti Computing (2017). https://www.rigetti.com/applications/pyquil
- K. Singhal, K. Hietala, S. Marshall, and R. Rand (2022). “Q# as a quantum algorithmic language”. arXiv:2206.03532. https://doi.org/10.4204/EPTCS.394.10.
- The Quipper Language (2013). https://www.mathstat.dal.ca/~selinger/quipper/
- J. M. Smith, N. J. Ross, P. Selinger, and B. Valiron (2014). “Quipper: Concrete resource estimation in quantum algorithms”. arXiv:1412.0625. https://doi.org/10.48550/arXiv.1412.0625.
- Dwave Ocean (2018). https://docs.ocean.dwavesys.com/en/stable/
- D. Willsch, M. Willsch, C. D. Gonzalez Calaza, F. Jin, H. De Raedt, M. Svensson, and K. Michielsen (2022). “Benchmarking advantage and Dwave 2000q quantum annealers with exact cover problems”. Quantum Information Processing 21: 4, 141. https://doi.org/10.1007/s11128-022-03476-y.
- R. Forest (2020). https://qcs.rigetti.com/sdk-downloads
- J. Olivares-Sanchez, J. Casanova, E. Solano, and L. Lamata (2020). “Measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti quantum computer”. Quantum Reports 2: 2, 293–304. https://doi.org/10.3390/quantum2020019.
- Microsoft Quantum SDK, Microsoft (2022). https://learn.microsoft.com/en-us/azure/quantum/qsharp-overview
- J. Hooyberghs (2022). Introducing Microsoft Quantum Computing for Developers: Using the Quantum Development Kit and Q# Apress, ISBN-10: 1484272455.
- Strawberry Fields, Xanadu (2018). https://strawberryfields.ai/
- ProjectQ: An open source software framework for quantum computing (2022). https://github.com/ProjectQ-Framework/ProjectQ
- Cirq: An open source framework for programming quantum computers. https://quantumai.google/cirq
- J. R. McClean, N. C. Rubin, K. J. Sung, I. D. Kivlichan, X. Bonet-Monroig, Y. Cao, C. Dai, E. S. Fried, C. Gidney, B. Gimby et al (2020). “Openfermion: the electronic structure package for quantum computers”. Quantum Science and Technology 5: 3, 034014. https://doi.org/10.1088/2058-9565/ab8ebc.
- J. R. Johansson, P. D. Nation, and F. Nori (2012). “Qutip: An open-source Python framework for the dynamics of open quantum systems”. Computer Physics Communications 183: 8, 1760–1772. https://doi.org/10.1016/j.cpc.2012.02.021.
- J. Garcia-Alonso, J. Rojo, D. Valencia, E. Moguel, J. Berrocal, and J. M. Murillo (2021). “Quantum software as a service through a quantum API gateway”. IEEE Internet Computing 26: 1, 34–41. https://doi.org/10.1109/MIC.2021.3132688.
- T. L. Patti, J. Kossaifi, S. F. Yelin, and A. Anandkumar (2021). “Tensorly-quantum: Quantum machine learning with tensor methods”. arXiv:2112.10239. https://doi.org/10.48550/arXiv.2112.10239.
- C. F. Negre, H. Ushijima-Mwesigwa, and S. M. Mniszewski (2020). “Detecting multiple communities using quantum annealing on the D-wave system”. Plos One 15: 2, e0227538. https://doi.org/10.1371/journal.pone.0227538.
- J. Romero-Alvarez, J. Alvarado-Valiente, E. Moguel et al (2023). “Leveraging API specifications for scaffolding quantum applications”, in International Workshop on Quantum Software Engineering and Technology(QCE23) IEEE Quantum Week 2023. https://doi.org/10.1109/QCE57702.2023.10208.
- I. Kumara, W.-J. Van Den Heuvel, D. A. Tamburri (2021). “Qsoc: Quantum service-oriented computing”, in Symposium and Summer School on Service-Oriented Computing Springer, pp. 52–63. https://doi.org/10.1007/978-3-030-87568-8 3.
- J. Alvarado-Valiente, J. Romero-Alvarez, E. Moguel, J. Garcia-Alonso, J. M. Murillo (2023). “Technological diversity of quantum computing providers: a comparative study and a proposal for API Gateway integration”. Software Quality Journal 32, 5373. https://doi.org/10.1007/s11219-023-09633-5.
- S. Schwichtenberg, C. Gerth, G. Engels (2017). “From Open API to semantic specifications and code adapters” in 2017 IEEE International Conference on Web Services (ICWS) pp. 484-491. https://doi.org/10.1109/ICWS.2017.56.
- J. Romero-Alvarez, J. Alvarado-Valiente, J. Casco-Seco et al. (2023). ”Developing high-level abstractions for creating quantum services: Open API and Async API”, in Symposium and Summer School On Service-Oriented Computing (SummerSOC 2023) Crete, Greece. https://doi.org/10.1007/978-3-031-64136-7 8.
- B. Heim (2021). “Universal quantum intermediate representation”. APS March Meeting Abstracts 2021, 34-009.
- Y. Ohkura, T. Satoh, R. Van Meter (2022). “Simultaneous execution of quantum circuits on current and near-future NISQ systems”. IEEE Transactions on Quantum Engineering 3. https://doi.org/10.1109/TQE.2022.3164716.
- M. Kaliyanandi, J. Murugan, S. K. Subburaj et al (2023). “Design and development of novel security approach designed for cloud computing with load balancing”, in Advances in Intelligent Applications and Innovative Approach 2760, 050005. https://doi.org/10.1063/5.0126814.
- E. Karacan, A. Karakaya, S. Akleylek (2022). “ Quantum secure communication between service provider and SIM”. IEEE Access 10, 69135-69146. https://doi.org/10.1109/ACCESS.2022.3186306.
- M. Salm, J. Barzen, F. Leymann, B. Weder (2022). “Prioritization of compiled quantum circuits for different quantum computers”, in 2022 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) pp. 1258-1265. https://doi.org/10.1109/SANER53432.2022.00150.
- J. Alvarado-Valiente, J. Romero-Alvarez, D. Arias et al (2023). ”Improving the quality of quantum services generation process: Controlling errors and noise”, in Hybrid Artificial Intelligent Systems Springer, Salamanca, Spain, pp. 180–191. https://doi.org/10.1007/978-3-031-40725-3 16.
- J. Alvarado-Valiente, J. Romero-Alvarez, A. Diaz et al (2023). ”Quantum services generation and deployment process: A quality-oriented approach”, in Quality of Information and Communications Technology Springer, Aveiro, Portugal, pp. 200–214. https://doi.org/10.1007/978-3-031-43703-8 15.
- V. Silva (2018). Practical Quantum Computing for Developers Programming Quantum Rigs in the Cloud Using Python, Quantum Assembly Language and IBM QExperience. Apress, USA.
- B. Weder, J. Barzen, F. Leymann et al (2021). “Qprov: A provenance system for quantum computing”. IET Quantum Communication, 2: 4, 171–181. https://doi.org/10.1049/qtc2.12012.
- B. Weder, J. Barzen, J. F. Leymann, M. Zimmermann (2021). “Hybrid quantum applications need two orchestrations in superposition: A software architecture perspective”, in 2021 IEEE International Conference on Web Services (ICWS) pp. 1–13. https://doi.org/10.1109/ICWS53863.2021.00015.
- S. Imre, L. Gyongyosi (2013). Advanced Quantum Communications: An Engineering Approach. Wiley-IEEE Press, 488 pp. ISBN: 978-1-118-00236-0.
- W. Cai, X. Mu, W. Wang et al. (2024). “Protecting entanglement between logical qubits via quantum error correction”. Nature Physics, 20, 1022–1026. https://doi.org/10.1038/s41567-024-02446-8.
- N. Benchasattabuse, M. Hajdusek and R. Van Meter (2024). “Engineering challenges in all-photonic quantum repeaters”. IEEE Network https://doi.org/10.1109/MNET.2024.3411802.
- A. K. Nayak, E. Chitambar, L. R. Varshney (2024). “Reliable quantum memories with unreliable components”. Physical Review A, 110, 032423. https://doi.org/10.1103/PhysRevA.110.032423.
- W. Dur, H.J. Briegel (2007). “Entanglement purification and quantum error correction”. Reports on Progress in Physics 70: 8, 1381–1424. DOI: 10.1088/0034-4885/70/8/R03.
- C. Wang, A. Rahman, R. Li, M. Aelmans, K. Chakraborty (2024). “Application Scenarios for the Quantum Internet”. RFC 9583 Internet-Draft https://datatracker.ietf.org/doc/rfc9583/.
- D. J. Reilly (2019). “Challenges in scaling-up the control interface of a quantum computer”. Proc. IEEE Int. Electron Devices Meeting (IEDM) pp. 31.7.1–31.7.6. https://doi.org/10.1109/IEDM19573.2019.8993497.
- A. G. Fowler, M. Mariantoni, J. M. Martinis, and A. N. Cleland (2012). “Surface codes: Towards practical large-scale quantum computation”. Physical Review A 86, 32324. https://doi.org/10.1103/PhysRevA.86.032324.
- T. S. Metodi, D. D. Thaker, A. W. Cross et al. “A quantum logic array microarchitecture: scalable quantum data movement and computation” in Proc. 38th Annu. IEEE/ACM Int. Symp. Microarchit. (MICRO) p. 12. https://doi.org/10.1109/MICRO.2005.9
- D. Aharonov, M. Ben-Or (1999). “Fault-tolerant quantum computation with constant error rate”. arXiv:quant-ph/9906129 https://doi.org/10.48550/arXiv.quant-ph/9906129.
- Z. Cai, R. Babbush, S. C. Benjamin, S. Endo, W. J. Huggins, Y. Li, J. R. McClean, and T. E. O’Brien (2023). “Quantum error mitigation”. Reviews of Modern Physics 95, 045005. https://link.aps.org/doi/10.1103/RevModPhys.95.045005.
- S. M. Attya, S. Q. G. Haddad, H. K. R. Al-Zaidi et al (2024). “Quantum computing impact on traditional computer architecture models”. Radioelectronics. Nanosystems. Information Technologies 16: 5, 691704. http://dx.doi.org/10.17725/j.rensit.2024.16.691.
- S. Muralidharan, L. Li, J. Kim et al (2016). “Optimal architectures for long distance quantum communication”. Scientific Reports, 6, 20463. https://doi.org/10.1038/srep20463.
- P. Wang, C. Y. Luan, M. Qiao et al. et al (2021). “Single ion qubit with estimated coherence time exceeding one hour”. Nature Communications 12, 233. https://doi.org/10.1038/s41467-020-20330-w.
- Y. Wang, M. Um, J. Zhang et al (2017). “Single-qubit quantum memory exceeding ten-minute coherence time”. Nature Photon 11, 646–650. https://doi.org/10.1038/s41566-017-0007-1.
- C.E. Bradley, J. Randall, M.H. Abobeih et al (2019). “A ten-qubit solid-state spin register with quantum memory up to one minute”. Physical Review X 9, 031045. https://doi.org/10.1103/PhysRevX.9.031045.
- M. H. Abobeih, J. Cramer, M. A. Bakker et al (2018). “One-second coherence for a single electron spin coupled to a multi-qubit nuclear-spin environment”. Nature Communications 9, 2552. https://doi.org/10.1038/s41467-018-04916-z.
- O. Milul, B. Guttel, U. Goldblatt, S. Hazanov et al (2023). “Superconducting cavity qubit with tens of milliseconds single-photon coherence time”. PRX Quantum 4, 030336. https://doi.org/10.1103/PRXQuantum.4.030336.