Quantum simulations of one dimensional quantum systems

TL;DR

This paper introduces quantum algorithms for simulating one-dimensional quantum systems, achieving significant speedups, and provides methods for state preparation and complexity analysis for various potentials.

Contribution

The paper develops new quantum algorithms for simulating 1D quantum systems, including the quantum harmonic oscillator and quartic potentials, with subpolynomial and polynomial complexities.

Findings

Quantum harmonic oscillator simulation with subpolynomial complexity in energy cutoff and precision.

Efficient quantum state preparation for Gaussian-like states.

Numerical evidence for polynomial complexity in simulating quartic potentials.

Abstract

We present quantum algorithms for the simulation of quantum systems in one spatial dimension, which result in quantum speedups that range from superpolynomial to polynomial. We first describe a method to simulate the evolution of the quantum harmonic oscillator (QHO) based on a refined analysis of the Trotter-Suzuki formula that exploits the Lie algebra structure. For total evolution time $t$ and precision $\epsilon>0$, the complexity of our method is $ O(\exp(\gamma \sqrt{\log(N/\epsilon)}))$, where $\gamma>0$ is a constant and $N$ is the quantum number associated with an "energy cutoff" of the initial state. Remarkably, this complexity is subpolynomial in $N/\epsilon$. We also provide a method to prepare discrete versions of the eigenstates of the QHO of complexity polynomial in $\log(N)/\epsilon$, where $N$ is the dimension or number of points in the discretization. This method may be of independent interest as it provides a way to prepare, e.g., quantum states with Gaussian-like amplitudes. Next, we consider a system with a quartic potential. Our numerical simulations suggest a method for simulating the evolution of sublinear complexity $\tilde O(N^{1/3+o(1)})$, for constant $t$ and $\epsilon$. We also analyze complex one-dimensional systems and prove a complexity bound $\tilde O(N)$, under fairly general assumptions. Our quantum algorithms may find applications in other problems. As an example, we discuss the fractional Fourier transform, a generalization of the Fourier transform that is useful for signal analysis and can be formulated in terms of the evolution of the QHO.