Quantum Techniques in Machine Learning

Quantum Circuit Synthesis Using Projective Quantum Drifting Model

Not scheduled

20m

Jan Mouton Learning Centre (Stellenbosch University)

Poster

Speaker

Hoang Anh Nguyen (Quantum AI & Cyber Security Institute (QACI))

Description

Generating compact quantum circuits for a target unitary is a key problem in quantum compilation. In this work, we propose Projective Quantum Drifting model (PQD), a geometry-guided framework for target-unitary circuit synthesis. PQD progressively refines an initial unitary through projective drift directions, producing residual circuit blocks that improve the approximation. We compare PQD with baseline compilation methods using unitary fidelity, gate length, and runtime. The results show that PQD achieves competitive fidelity while generating circuits and maintaining efficient training time, making it a promising approach for practical quantum circuit synthesis.

Presentation materials

abstract_qtml.pdf