基于定量因果原理的物理基本相互作用和变分原理的统一表示

Explaining Retrocausality Phenomena in Quantum Mechanics using a Modified Variational Principle

A modified lagrangian with causal and retrocausal momenta was used to derive a first causal wave equation and a second retrocausal wave equation using the principle of least action. The retrocausal wave function obtained through this method was found to be equivalent to the complex conjugate of the causal wave function, thus leading to the conclusion that a retrocausal effect is already implicit in quantum mechanics through the means of complex conjugation of the wave function when computing the probability density for a particle. Lastly, the same variational principle was employed with a frac…

Noise reduction using past causal cones in variational quantum algorithms

We introduce an approach to improve the accuracy and reduce the sample complexity of near term quantum-classical algorithms. We construct a simpler initial parameterized quantum state, or ansatz, based on the past causal cone of each observable, generally yielding fewer qubits and gates. We implement this protocol on a trapped ion quantum computer and demonstrate improvement in accuracy and time-to-solution at an arbitrary point in the variational search space. We report a $\sim 27\%$ improvement in the accuracy of the calculation of the deuteron binding energy and $\sim 40\%$ improvement in t…

用于因果圈费曼图和有向无环图的变分量子本征求解器

Variational quantum eigensolver for causal loop Feynman diagrams and directed acyclic graphs

本文提出一种基于变分量子本征求解器（VQE）的算法，通过最小化圈哈密顿量来高效识别多圈费曼图的因果表示或有向图中的无环构型，并适配选择多个简并最小值以提高检测率，相比Grover算法使用更少量子资源。

Explaining quantum correlations through evolution of causal models

We propose a framework for the systematic and quantitative generalization of Bell's theorem using causal networks. We first consider the multi-objective optimization problem of matching observed data while minimizing the causal effect of nonlocal variables and prove an inequality for the optimal region that both strengthens and generalizes Bell's theorem. To solve the optimization problem (rather than simply bound it), we develop a novel genetic algorithm treating as individuals causal networks. By applying our algorithm to a photonic Bell experiment, we demonstrate the trade-off between the q…

神经量子态的机械可解释性与因果特征引导：基于稀疏自编码器

Mechanistic Interpretability and Causal Feature Steering of Neural Quantum States via Sparse Autoencoders

利用稀疏自编码器从神经量子态内部激活中无监督提取物理可观测量相关特征，并通过后训练干预单一特征因果地平滑引导对应可观测量，揭示其可解释内部表示。