Multi-level quantum signal processing with applications to ground state preparation using fast-forwarded Hamiltonian evolution
<|reference_start|>Multi-level quantum signal processing with applications to ground state preparation using fast-forwarded Hamiltonian evolution: The preparation of the ground state of a Hamiltonian $H$ with a large spectral radius has applications in many areas such as electronic structure theory and quantum field theory. Given an initial state with a constant overlap with the ground state, and assuming that the Hamiltonian $H$ can be efficiently simulated with an ideal fast-forwarding protocol, we first demonstrate that employing a linear combination of unitaries (LCU) approach can prepare the ground state at a cost of $\mathcal{O}(\log^2(\|H\| \Delta^{-1}))$ queries to controlled Hamiltonian evolution. Here $\|H\|$ is the spectral radius of $H$ and $\Delta$ the spectral gap. However, traditional Quantum Signal Processing (QSP)-based methods fail to capitalize on this efficient protocol, and its cost scales as $\mathcal{O}(\|H\| \Delta^{-1})$. To bridge this gap, we develop a multi-level QSP-based algorithm that exploits the fast-forwarding feature. This novel algorithm not only matches the efficiency of the LCU approach when an ideal fast-forwarding protocol is available, but also exceeds it with a reduced cost that scales as $\mathcal{O}(\log(\|H\| \Delta^{-1}))$. Additionally, our multi-level QSP method requires only $\mathcal{O}(\log(\|H\| \Delta^{-1}))$ coefficients for implementing single qubit rotations. This eliminates the need for constructing the PREPARE oracle in LCU, which prepares a state encoding $\mathcal{O}(\|H\| \Delta^{-1})$ coefficients regardless of whether the Hamiltonian can be fast-forwarded.<|reference_end|>
@article{dong2024multi-level,
    title={Multi-level quantum signal processing with applications to ground state
  preparation using fast-forwarded Hamiltonian evolution},
    author={Yulong Dong, Lin Lin},
    journal={arXiv preprint arXiv:2406.02086},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.02086},
    primaryClass={quant-ph cs.NA math.NA}
}