Unlocking the power of global quantum gates with machine learning

• DOI: 10.48550/arxiv.2502.02405
• Publication Date: 2025-02-04
• Authors: Vinit Singh; Bin Yan
• Keywords: Quantum Physics; FOS: Physical sciences; Quantum Physics (quant-ph)

In conventional circuit-based quantum computing architectures, the standard gate set includes arbitrary single-qubit rotations and two-qubit entangling gates. However, this choice is not always aligned with native operations available in certain hardware, where natural gates are restricted to two qubits but can act on multiple, or even all, simultaneously. leveraging capabilities of global for algorithm implementations highly challenging, as directly compiling local sequences into usually gives rise a circuit that more complex than original one. Here, we circumvent difficulty using variational approach. Specifically, propose parameterized ansatze composed finite number layers unitaries, which be implemented constant time. Furthermore, by construction, these equivalent linear depth local-gate circuits expressible. We demonstrate approach applying it problem ground state preparation Heisenberg model toric code Hamiltonian, highlighting its potential offer practical advantage.