arXiv Machine Learning By Janani Gomathi, Alex Meiburg

Gradient descent reliably finds depth- and gate-optimal circuits for generic unitaries

Read the original on arXiv Machine Learning →

arXiv:2601. 03123v2 Announce Type: replace-cross Abstract: When the gate set has continuous parameters, synthesizing a unitary operator as a quantum circuit is, in principle, always possible using exact methods.

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 Machine Learning.

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
Jun 9

Zero-shot Quantum Neural Architecture Search

arXiv:2605. 27410v2 Announce Type: replace-cross Abstract: Variational Quantum Algorithms (VQAs) are a leading approach to exploiting near-term quantum hardware, leveraging parameterized quantum circuits and classical optimization to achieve advantage.

By Tung Dao, Son N. Tran, Huynh Thi Thanh Binh
arXiv AI
Aug 20

AlphaClifford: Efficient Clifford Synthesis and Transpilation with Model-based RL

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