Quantum algorithm for structure learning of Markov Random Fields

Quantum algorithm for Markov Random Fields structure learning by information theoretic properties

Probabilistic graphical models play a crucial role in machine learning and have wide applications in various fields. One pivotal subset is undirected graphical models, also known as Markov random fields. In this work, we investigate the structure learning methods of Markov random fields on quantum computers. We propose a quantum algorithm for structure learning of an $r$-wise Markov Random Field with a bounded degree underlying graph, based on a nearly optimal classical greedy algorithm. The quantum algorithm provides a polynomial speed-up over the classical counterpart in terms of the number …

Constructive Feedback of Non-Markovianity on Resources in Random Quantum States

We explore the impact of non-Markovian channels on the quantum correlations (QCs) of Haar uniformly generated random two-qubit input states with different ranks -- either one of the qubits (single-sided) or both the qubits independently (double-sided) are passed through noisy channels. Under dephasing and depolarizing channels with varying non-Markovian strength, entanglement and quantum discord of the output states collapse and revive with the increase of noise. We find that in case of the depolarizing double-sided channel, both the QCs of random states show a higher number of revivals on ave…

Qubit systems subject to unbalanced random telegraph noise: quantum correlations, non-Markovianity and teleportation

We address the dynamics of quantum correlations in a two-qubit system subject to unbalanced random telegraph noise (RTN) and discuss in details the similarities and the differences with the balanced case. We also evaluate quantum non-Markovianity of the dynamical map. Finally, we discuss the effects of unbalanced RTN on teleportation, showing that noise imbalance mitigates decoherence and preserves teleportation fidelity.

非马尔可夫随机幺正量子比特动力学

Non-Markovian random unitary qubit dynamics

我们比较了两种非马尔可夫量子演化的方法：一种是基于可分映射的概念，另一种是基于量子态的可分性。前一个概念是充分的特点，在当地的发电机，而它通常是不正确的后者。一个简单的量子比特随机幺正动力学的例子显示了这些方法之间的复杂差异。此外，在这种情况下，这两种方法的充分特点是在本地退相干率。作为一个副产品，它表明，熵可能单调增加，即使非马尔可夫量子比特动力学。

Quantum dephasing induced by non-Markovian random telegraph noise

We theoretically study the dynamical dephasing of a quantum two level system interacting with an environment exhibiting non-Markovian random telegraph fluctuations. The time evolution of the conditional probability of the environmental noise is governed by a generalized master equation depending on the environmental memory effect. The expression of the dephasing factor is derived exactly which is closely associated with the memory kernel in the generalized master equation for the conditional probability of the environmental noise. In terms of three important types memory kernels, we discuss th…

双稳态随机电报噪声驱动的量子比特动力学中的量子速度极限：从马尔可夫到非马尔可夫区域

Quantum Speed Limits in Qubit Dynamics Driven by Bistable Random Telegraph Noise: From Markovian to Non-Markovian Regimes

研究双稳态随机电报噪声下量子比特的量子速度极限，发现非马尔可夫记忆效应通过信息回流加速演化，而非平衡初始条件则导致纯马尔可夫行为，为设计快速抗噪量子协议提供指导。