Optimal Bounds, Barriers, and Extensions for Non-Hermitian Bivariate Quantum Signal Processing
Joshua M. Courtney

TL;DR
This paper advances the understanding of multivariate quantum signal processing for non-Hermitian Hamiltonians, providing tight bounds, analyzing optimization landscapes, and proposing efficient algorithms with near-optimal complexity.
Contribution
It establishes tight bounds on anti-Hermitian query complexity, analyzes the optimization landscape, and introduces improved algorithms and methods for non-Hermitian quantum simulation.
Findings
Anti-Hermitian query complexity is tight at $d_I = \Theta(\beta_I T + \log(1/\varepsilon)/\log\log(1/\varepsilon))$.
Fast-forwarding to $d_I = \mathcal{O}(\sqrt{\beta_I T})$ is impossible, but linear improvements are achievable.
Block peeling reduces angle-finding complexity from $\mathcal{O}(d^3)$ to $\mathcal{O}(d^2)$.
Abstract
Multivariate quantum signal processing (M-QSP) has recently been shown to be applicable for non-Hermitian Hamiltonian simulation, opening several problems regarding the optimization landscape, angle-finding, and constant-factor analysis. We resolve several of these problems here. We find the anti-Hermitian query complexity to be tight, established via Chebyshev coefficient bounds, modified Bessel function asymptotics, and Lambert~ inversion. Fast-forwarding to is impossible in the bivariate polynomial model, though a linear state-dependent improvement to is achievable. The optimization landscape of M-QSP admits spurious local minima, but a warm-start basin guarantee ensures the two-stage…
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