Design Automation for Quantum Circuits

3D illustration of a quantum circuit layout, showing how wires connect qubits and how their positions affect circuit design.

Design Automation for Quantum Circuits (DAQC) refers to the use of specialized software tools to help turn high-level quantum algorithms into working instructions that can be used on real quantum computers.[1] This automation process is essential because quantum computers work in a very different way than classical ones: they use qubits which can be in multiple states at once, and are easily affected by noise.[2] Additionally, DAQC means using software to make quantum computing hardware and applications easier to develop. It turns high-level quantum algorithms into optimized circuits for specific quantum systems. DAQC tools bridge the gap between abstract quantum algorithms and physical hardware implementations, enabling efficient use of noisy intermediate-scale quantum (NISQ) devices and fault-tolerant architectures.[3] Unlike classical circuit design, which has well-developed tools, quantum design automation is still new and challenging. One of the reasons is because quantum bits (qubits) behave differently. They are sensitive to noise, have limited connections, and use reversible logic. These issues require special methods for breaking down gates, reducing errors, mapping circuits, and simulating them. As quantum processors grow and change, automated design is crucial to ensure they work well and correctly on different hardware.[4]

The automation process in quantum circuit design includes various stages such as algorithm specification, circuit synthesis, gate decomposition, qubit mapping, and noise-aware optimization.[5] These stages help transform abstract quantum algorithms into physical instructions that can run on real quantum devices, often constrained by specific topologies and hardware characteristics.[6]

As the quantum computing ecosystem matures, numerous software frameworks and toolchains have emerged to support this design process. Platforms like IBM's Qiskit, Google's Cirq, and the MQT Suite provide environments for simulating, optimizing, and compiling quantum circuits tailored to current quantum hardware. These tools play a critical role in making quantum computing more scalable, reproducible, and accessible to researchers and engineers.[7]

  1. ^ Quetschlich, Nils; Burgholzer, Lukas; Wille, Robert (2023-07-20). "MQT Bench: Benchmarking Software and Design Automation Tools for Quantum Computing". Quantum. 7: 1062. arXiv:2204.13719. Bibcode:2023Quant...7.1062Q. doi:10.22331/q-2023-07-20-1062. ISSN 2521-327X.
  2. ^ Hong, Xin; Zhou, Xiangzhen; Li, Sanjiang; Feng, Yuan; Ying, Mingsheng (2022-11-30). "A Tensor Network based Decision Diagram for Representation of Quantum Circuits". ACM Transactions on Design Automation of Electronic Systems. 27 (6): 1–30. doi:10.1145/3514355. ISSN 1084-4309.
  3. ^ Kundu, Joydeep; Paria, Prantik; Dey, Rajjdeep; Sengupta, Saptarshi; Dey, Pritam; Pradhan, Debkanta; Mukherjee, Chiradeep; Pramanik, Sayak (2023), Bhattacharyya, Siddhartha; Banerjee, Jyoti Sekhar; Köppen, Mario (eds.), "Performance Analysis of Reversible Full Adders in Noisy Intermediate Scale Quantum (NISQ) Devices", Human-Centric Smart Computing, vol. 316, Singapore: Springer Nature Singapore, pp. 67–79, doi:10.1007/978-981-19-5403-0_6, ISBN 978-981-19-5402-3, retrieved 2025-06-08
  4. ^ Desurvire, Emmanuel (2009-02-19). Classical and Quantum Information Theory: An Introduction for the Telecom Scientist (1 ed.). Cambridge University Press. doi:10.1017/cbo9780511803758.018. ISBN 978-0-521-88171-5.
  5. ^ Matteo, Olivia Di; Mosca, Michele (2016-03-01). "Parallelizing quantum circuit synthesis". Quantum Science and Technology. 1 (1): 015003. arXiv:1606.07413. Bibcode:2016QS&T....1a5003D. doi:10.1088/2058-9565/1/1/015003. ISSN 2058-9565.
  6. ^ Kim, Dongmin; Heng, Sengthai; Han, Youngsun (2022-02-28). "A Parallelized Qubit Mapping Algorithm for Large-scale Quantum Circuits". IEIE Transactions on Smart Processing & Computing. 11 (1): 40–48. doi:10.5573/IEIESPC.2021.11.1.40. ISSN 2287-5255.
  7. ^ Mulherkar, Jaideep; Rajdeepak, Rishikant; Sunitha, V. (2022-10-01). "Implementation of quantum hitting times of cubelike graphs on IBM's Qiskit platform". International Journal of Quantum Information. 20 (7): 2250020–2250928. Bibcode:2022IJQI...2050020M. doi:10.1142/S0219749922500204. ISSN 0219-7499.

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