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
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
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
arXiv:2607. 25865v1 Announce Type: cross Abstract: Quantum error correction (QEC) is indispensable for scalable fault-tolerant quantum computing.
By Ge Yan, Shanchuan Li, Pengyue Ma, Qixin Zhang, Pingchuan Ma, Jianping Wang, Min-Hsiu Hsieh, Yuxuan Du
arXiv:2607. 24714v1 Announce Type: cross Abstract: Trapped-ion quantum computers rely on shuttling compilers, which cast an input algorithm into a sequence of ion-qubit movements within a given architecture.
By Fabian Kreppel, Reza Salkhordeh, Ferdinand Schmidt-Kaler, Andr\'e Brinkmann
arXiv:2606. 13380v1 Announce Type: cross Abstract: The design of high performing quantum circuits remains largely dependent on human expertise.
By Kenya Sakka, Wataru Mizukami, Kosuke Mitarai
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. Quantum resource estimation (QRE) plays a central role in this transition, yet existing approaches remain largely compilation-heavy or domain-knowledge-guided symbolic annotations, and tightly coupled to long-term fault-tolerant assumptions, limiting their topical applicability.
arXiv:2607. 15313v1 Announce Type: new Abstract: The scaling hypothesis assumes that increasing model parameters yields emergent reasoning capabilities.
By Junhao Song, Yu Zhou, William Knottenbelt, Yudong Cao
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
arXiv:2606. 13811v1 Announce Type: cross Abstract: Can Large Language Models (LLMs) understand and reason about quantum operators?
By Rogerio Feris, Yunchao Liu, Pengyuan Li, Hang Hua, David Kremer
arXiv:2411.10406v4 Announce Type: replace-cross
Abstract: In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, sm...
By Masoud Mohseni, Artur Scherer, K. Grace Johnson, Oded Wertheim, Matthew Otten, Namit Anand, Navid Anjum Aadit, Yuri Alexeev, Gilad Ben-Shach, Kirk M. Bresniker, Kerem Y. Camsari, Barbara Chapman, Soumitra Chatterjee, Shuvro Chowdhury, Gebremedhin A. Dagnew, Tom Dvir, Aniello Esposito, Farah Fahim, Michael Ferguson, Marco Fiorentino, Archit Gajjar, Katerina Gratsea, Gaurav Gyawali, Christian Heiter, Ali H. Z. Kavaki, Abdullah Khalid, Xiangzhou Kong, Bohdan Kulchytskyy, Elica Kyoseva, Ruoyu Li, P. Aaron Lott, Igor L. Markov, Robert F. McDermott, Lucas Morais, Giacomo Pedretti, Pooja Rao, Eleanor Rieffel, Allyson Silva, John Sorebo, Panagiotis Spentzouris, Ziv Steiner, Boyan Torosov, Davide Venturelli, Robert J. Visser, Zak Webb, Xin Zhan, Yonatan Cohen, Pooya Ronagh, Alan Ho, Raymond G. Beausoleil, John M. Martinis
arXiv:2607. 11936v1 Announce Type: new Abstract: Hyperdimensional Computing (HDC) represents symbols using high-dimensional hypervectors of dimension $D$.
By Sanggeon Yun, Hyunwoo Oh, Ryozo Masukawa, Raheeb Hassan, Mohsen Imani