路径边采样的量子算法

Quantum sampling for the Euclidean path integral of lattice gauge theory

Although the Hamiltonian formalism is so far favored for quantum computation of lattice gauge theory, the path integral formalism would never be useless. The advantages of the path integral formalism are the knowledge and experience accumulated by classical lattice simulation and manifest Lorentz invariance. We discuss quantum computation of lattice gauge theory in the path integral formalism. We utilize a quantum sampling algorithm to generate gauge configurations, and demonstrate a benchmark test of $Z_2$ lattice gauge theory on a four-dimensional hypercube.

Direct computation of the quantum partition function by path-integral nested sampling

In the present work we introduce a computational approach to the absolute rovibrational quantum partition function using the path-integral formalism of quantum mechanics in combination with the nested sampling technique. The numerical applicability of path-integral nested sampling is demonstrated for small molecules of spectroscopic interest. The computational cost of the method is determined by the evaluation time of a point on the potential-energy surface (PES). For efficient PES implementations, the path-integral nested-sampling method can be a viable alternative to the direct Boltzmann sum…

Noise-Induced Sampling of Alternative Hamiltonian Paths in Quantum Adiabatic Search

We numerically simulate the effects of noise-induced sampling of alternative Hamiltonian paths on the ability of quantum adiabatic search (QuAdS) to solve randomly generated instances of the NP-Complete problem N-bit Exact Cover 3. The noise-averaged median runtime is determined as the noise-power and number of bits N are varied, and power-law and exponential fits are made to the data. Noise is seen to slowdown QuAdS, though a downward shift in the scaling exponent is found for N > 12 over a range of noise-power values. We discuss whether this shift might be connected to arguments in the liter…

超越玻色采样：高自旋采样作为实现量子优越性的实用路径

Beyond Boson Sampling: Higher Spin Sampling as a Practical Path to Quantum Supremacy

本文提出任意自旋-S态的自旋采样框架，给出m∼n^{1+ε}的准线性资源标度，为以更低实验成本实现量子优越性提供实用路径。

通过统一路径变化和非退化批量采样加速量子张量网络模拟

Accelerating Quantum Tensor Network Simulations with Unified Path Variations and Non-Degenerate Batched Sampling

本文提出统一路径变化和非退化批量采样方法，显著提升张量网络模拟效率，实现传统方法的10^8倍速度提升，尤其在非比例采样中表现突出。

随机薛定谔动力学的过渡路径与界面采样

Transition Path and Interface Sampling of Stochastic Schrödinger Dynamics

本文将过渡路径与界面采样方法扩展到随机薛定谔方程描述的开放量子系统，并通过四次双阱中的量子布朗运动模型揭示低温下因反芝诺效应导致的Arrhenius定律显著偏离。