arXiv AI By Irfansha Shaik

A 12-CNOT Double Qubit Excitation Gate

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arXiv:2608. 11733v1 Announce Type: cross Abstract: Effective implementation of high-level quantum gates is essential for practical quantum computing.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning
Jun 10

Parity Cross-Resonance: A Multiqubit Gate

arXiv:2508. 10807v2 Announce Type: replace-cross Abstract: We present a native three-qubit entangling gate that exploits engineered interactions to realize control-control-target and control-target-target operations in a single coherent step.

By Xuexin Xu, Siyu Wang, Radhika Joshi, Rihan Hai, Mohammad H. Ansari
arXiv Machine Learning
Sep 11

Quantum State Preparation with the QNN-based SRBB Algorithm

arXiv:2503. 13647v2 Announce Type: replace-cross Abstract: In this work, a novel algorithm structured on Lie algebras for the approximate quantum state preparation problem is proposed, addressing a challenge of fundamental importance in many areas of quantum computing.

By Marco Mordacci, Giacomo Belli, Michele Amoretti
arXiv Machine Learning
Jul 24

Neural Guided Sampling for Quantum Circuit Optimization

arXiv:2510. 12430v2 Announce Type: replace-cross Abstract: Translating a general quantum circuit on a specific hardware topology with a reduced set of available gates, also known as transpilation, comes with a substantial increase in the length of the equivalent circuit.

By Bodo Rosenhahn, Tobias J. Osborne, Christoph Hirche
arXiv Machine Learning
Sep 23

Bridge of $\Psi$'s: Quantum Circuit Optimization with Schr\"odinger Bridges

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.

By Lino S. Hofstetter, Lia Yeh, Prakash Murali
arXiv AI
Sep 10

How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits

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