- author = "Arinze Obidiegwu and Douglas {Mota Dias} and Emmanuel Obidiegwu and Conor Ryan",
- title = "Evolving Hardware-Efficient {Grover} Circuits with Grammatical Evolution",
- booktitle = "Proceedings of the 2026 Genetic and Evolutionary Computation Conference",
- year = "2026",
- editor = "Nelishia Pillay and Gabriel Kronberger and Leonardo Trujillo and Ting Hu and Sebastian {Rojas Gonzalez} and Saul {Calderon Ramirez} and Stephan Winkler and Yazmin Maldonado and James McDermott and Jose Manuel {Munoz Contreras} and Giorgia Nadizar and Marcella {Scoczynski Ribeiro Martins} and Hirad Assimi and Mario Andres Munoz and Oliver Cuate and Daniel Hernandez and Yanan Sun and Edgar Galvan and Aniko Ekart and Nadarajen Veerapen",
- address = "San Jose, Costa Rica",
- series = "GECCO '26",
- month = "13-17 " # jul,
- organisation = "SIGEVO",
- publisher = "Association for Computing Machinery",
- publisher_address = "New York, NY, USA",
- keywords = "genetic algorithms, genetic programming, Grammatical Evolution, Quantum computing, Evolvable hardware, EHW, Hardware realization, Indirect encoding, Noisy Intermediate-Scale Quantum, NISQ",
- isbn13 = "979-8-4007-2487-9",
-
URL = "
https://www.egr.msu.edu/~goodman/HumiesEntries2026/Obidiegwu/Paper.pdf",
-
DOI = "
10.1145/3795095.3805176",
- size = "9 pages",
- abstract = "Canonical quantum algorithms often achieve low execution fidelities on current Noisy Intermediate-Scale Quantum (NISQ) hardware. The standard implementation of Grover search algorithm, designed for theoretical generality, produces deep, gate-heavy circuits that are susceptible to noise. This paper challenges the one-size-fits-all design paradigm by using Grammatical Evolution (GE) to automatically discover hardware-efficient, state-specific quantum circuits. We demonstrate this approach by evolving bespoke circuits for all eight 3-qubit computational basis states and executing them on a 133-qubit IBM Heron quantum processor. To our knowledge, this is the first hardware-validated application of GE for this task. The results indicate significant performance gains: evolved circuits achieve hardware-executed fidelities up to 96.9 percent (vs. 66.3 percent baseline) while reducing circuit depth by 82.5-96.6 percent and gate count by 77.4-94.6 percent compared to canonical implementations. These findings suggest that automated symbolic search is a viable approach to designing algorithms that can execute on todays NISQ devices.",
-
notes = "2026 HUMIES finalist
GECCO-2026 A Recombination of the 35th International Conference on Genetic Algorithms (ICGA) and the 31st Annual Genetic Programming Conference (GP)",
