arXiv:2610. 37045v1 Announce Type: new Abstract: Efficient quantum circuit compilation is challenging for quantum computers, where limited qubit connectivity can introduce substantial overhead in the implementation of logical operations.
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...
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.
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.
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.
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...
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.
arXiv:2607. 20225v1 Announce Type: cross Abstract: While combinatorial optimization problems are central to many scientific and engineering applications, their solution remains challenging due to exponentially large search spaces.
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:2606. 29636v1 Announce Type: cross Abstract: An important task in quantum computing is unitary circuit synthesis compatible with physical hardware constraints.
arXiv:2607. 15313v1 Announce Type: new Abstract: The scaling hypothesis assumes that increasing model parameters yields emergent reasoning capabilities.
arXiv:2602. 05541v3 Announce Type: replace-cross Abstract: Matrix multiplication is a fundamental operation in compute-intensive tasks and a key component of modern quantum acceleration frameworks.
Bridge of Ψ's (BOPS) is a generative model that learns to transform quantum circuits into equivalent, optimized versions using Schr"odinger bridges and a custom denoiser architecture. Trained on data engineered to challenge existing optimizers, BOPS achieves a 2.46× reduction in gate count and a 2.45× reduction in depth on 8‑qubit, 64‑depth Clifford+$T$ circuits, outperforming nine baseline optimizers. This work demonstrates the first successful application of generative machine learning to quantum circuit optimization, expanding the quantum compilation stack with learned techniques.