arXiv:2604.13812v2 Announce Type: replace
Abstract: Quantum circuit optimization is a central task in quantum computing, as current NISQ devices suffer from error propagation that often scales with t...
By Jacopo Cossio, Daniele Lizzio Bosco, Riccardo Romanello, Giuseppe Serra, Carla Piazza
AlphaClifford is a model‑based reinforcement learning framework that uses Monte Carlo Tree Search to synthesize Clifford circuits from the H, S, and CNOT gate set. By modeling the state space with the algebraic properties of the symplectic group, it consistently reduces total and two‑qubit gate counts compared to existing heuristics. The approach also extends to hardware‑constrained transpilation and serves as a post‑synthesis optimizer in a full Clifford+T pipeline.
By Daniele Lizzio Bosco, Jacopo Cossio, Carla Piazza, Giuseppe Serra
The paper introduces QuantumEvo, an evolutionary framework that employs a large language model (LLM) to generate heuristics for ordering variables in binary decision diagrams (BDDs) used in reversible quantum circuit synthesis. By searching over heuristic families and directly manipulating BDD variable orderings, QuantumEvo produces the HGA-QE heuristic, which modifies the sifting step of a genetic algorithm to better align with quantum circuit cost (QCC). Across benchmark sets, HGA-QE achieves a 70.9% tie-or-win rate against the best per-function baseline and strictly outperforms it on 13.5% of functions, demonstrating competitive QCC performance and a clear advantage on benchmarks from different data sources.
By Yoonju Sim, Federico Berto, Chuanbo Hua, Jinkyoo Park, Changhyun Kwon
arXiv:2511. 08315v2 Announce Type: replace-cross Abstract: Binary Decision Diagrams (BDDs) are instrumental in many electronic design automation (EDA) tasks thanks to their compact representation of Boolean functions.
By Mingkai Miao, Jianheng Tang, Guangyu Hu, Hongce Zhang
arXiv:2610.10351v1 Announce Type: cross
Abstract: Differentiable quantum architecture search (DQAS) is a promising framework for the automated design of quantum circuits, particularly for variational...
By Lukas Thei{\ss}inger, Thore Gerlach, Christian Bauckhage
arXiv:2608. 12936v1 Announce Type: cross Abstract: As quantum computing progresses from proof-of-principle demonstrations toward practical utility, a significant impediment is the need to augment algorithmic feasibility with system-level optimization across heterogeneous hardware and software stacks.
By Harshkumar Oza, Aritra Sarkar, Syed Naqi Abbas, Rahul Bhowmick, Aryan Prakash, Prateek P Kulkarni, Krishna Kumar Sabapathy