arXiv:2607. 12780v1 Announce Type: cross Abstract: Quantum circuit optimization for fault-tolerant computing requires exact functional equivalence while minimizing expensive non-Clifford resources such as T gates.
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Quantum circuit optimization for fault-tolerant computing requires exact functional equivalence while minimizing expensive non-Clifford resources such as T gates. We study this problem using a compact 44.
arXiv:2604. 23931v2 Announce Type: replace-cross Abstract: Variational quantum circuits (VQCs) are a leading approach to quantum machine learning on near-term devices, yet it remains unclear which circuit architecture yields the best accuracy-parameter trade-off on classical tabular data.
arXiv:2504. 05336v4 Announce Type: replace-cross Abstract: A recurring weakness in quantum machine learning (QML) is that reported ``quantum advantages'' are seldom tested against a \emph{capacity-matched} classical control, leaving it unclear whether a gain comes from the quantum substrate or from the architectural change that accompanies it.
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.
arXiv:2607. 29145v1 Announce Type: cross Abstract: Quantum software engineering is an emerging research field focusing on efficiently embedding the quantum programming paradigm into existing software ecosystems.
arXiv:2606. 27119v1 Announce Type: cross Abstract: Foundation decoders, a class of high-capacity neural decoders, are leading candidates for fault-tolerant quantum computing, with accurate and efficient decoding at large code distances.