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Researcher profile

Z. Jiang

Z. Jiang contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
cond-mat.str-elquant-phArtificial Intelligencecond-mat.dis-nncond-mat.mes-hallcond-mat.mtrl-scicond-mat.stat-mechRobotics

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Published work

7 published item(s)

preprint2026arXiv

Shared Backbone PPO for Multi-UAV Communication Coverage with Connection Preservation

This paper proposes a Shared Backbone Proximal Policy Optimization (Shared Backbone PPO) algorithm. By sharing the base module between the Actor and Critic networks, the algorithm achieves efficient training and improved performance. The algorithm is implemented in a connectivity-preserving multi-UAV swarm communication coverage task and compared with the standard PPO algorithm. Experimental results demonstrate that the proposed method achieves superior performance. Furthermore, a graph information aggregation module is incorporated into the model architecture to accommodate the communication conditions among agents. With the integration of this module, the algorithm remains effective, and the trained agent swarm exhibits a higher level of cooperation.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Platform

With the development of industry, drones are appearing in various field. In recent years, deep reinforcement learning has made impressive gains in games, and we are committed to applying deep reinforcement learning algorithms to the field of robotics, moving reinforcement learning algorithms from game scenarios to real-world application scenarios. We are inspired by the LunarLander of OpenAI Gym, we decided to make a bold attempt in the field of reinforcement learning to control drones. At present, there is still a lack of work applying reinforcement learning algorithms to robot control, the physical simulation platform related to robot control is only suitable for the verification of classical algorithms, and is not suitable for accessing reinforcement learning algorithms for the training. In this paper, we will face this problem, bridging the gap between physical simulation platforms and intelligent agent, connecting intelligent agents to a physical simulation platform, allowing agents to learn and complete drone flight tasks in a simulator that approximates the real world. We proposed a reinforcement learning framework based on Gazebo that is a kind of physical simulation platform (ROS-RL), and used three continuous action space reinforcement learning algorithms in the framework to dealing with the problem of autonomous landing of drones. Experiments show the effectiveness of the algorithm, the task of autonomous landing of drones based on reinforcement learning achieved full success.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Extremely low-energy collective modes in a quasi-one-dimensional system

We have investigated the quasiparticle dynamics and collective excitations in the quasi-one-dimensional material ZrTe$_5$ using ultrafast optical pump-probe spectroscopy. Our time-domain results reveal two coherent oscillations having extremely low energies of $\hbarω_1\sim$0.33 meV (0.08 THz) and $\hbarω_2\sim$1.9 meV (0.45 THz), which are softened as the temperature approaches two different critical temperatures ($\sim$54 K and $\sim$135 K). We attribute these two collective excitations to the amplitude mode of charge density wave instabilities in ZrTe$_5$ with tremendously small nesting wave vectors. Furthermore, scattering with the $\hbarω_2$ mode may result in a peculiar quasiparticle decay process with a timescale of $\sim$1-2 ps below the transition temperature $T^*$ ($\sim$135 K). Our findings provide pivotal information for studying the fluctuating order parameters and their associated quasiparticle dynamics in various low-dimensional topological systems and other materials.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Realizing topologically ordered states on a quantum processor

The discovery of topological order has revolutionized the understanding of quantum matter in modern physics and provided the theoretical foundation for many quantum error correcting codes. Realizing topologically ordered states has proven to be extremely challenging in both condensed matter and synthetic quantum systems. Here, we prepare the ground state of the toric code Hamiltonian using an efficient quantum circuit on a superconducting quantum processor. We measure a topological entanglement entropy near the expected value of $\ln2$, and simulate anyon interferometry to extract the braiding statistics of the emergent excitations. Furthermore, we investigate key aspects of the surface code, including logical state injection and the decay of the non-local order parameter. Our results demonstrate the potential for quantum processors to provide key insights into topological quantum matter and quantum error correction.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Demonstrating a Continuous Set of Two-qubit Gates for Near-term Quantum Algorithms

Quantum algorithms offer a dramatic speedup for computational problems in machine learning, material science, and chemistry. However, any near-term realizations of these algorithms will need to be heavily optimized to fit within the finite resources offered by existing noisy quantum hardware. Here, taking advantage of the strong adjustable coupling of gmon qubits, we demonstrate a continuous two-qubit gate set that can provide a 3x reduction in circuit depth as compared to a standard decomposition. We implement two gate families: an iSWAP-like gate to attain an arbitrary swap angle, $θ$, and a CPHASE gate that generates an arbitrary conditional phase, $ϕ$. Using one of each of these gates, we can perform an arbitrary two-qubit gate within the excitation-preserving subspace allowing for a complete implementation of the so-called Fermionic Simulation, or fSim, gate set. We benchmark the fidelity of the iSWAP-like and CPHASE gate families as well as 525 other fSim gates spread evenly across the entire fSim($θ$, $ϕ$) parameter space achieving purity-limited average two-qubit Pauli error of $3.8 \times 10^{-3}$ per fSim gate.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Direct measurement of non-local interactions in the many-body localized phase

The interplay of interactions and strong disorder can lead to an exotic quantum many-body localized (MBL) phase. Beyond the absence of transport, the MBL phase has distinctive signatures, such as slow dephasing and logarithmic entanglement growth; they commonly result in slow and subtle modification of the dynamics, making their measurement challenging. Here, we experimentally characterize these properties of the MBL phase in a system of coupled superconducting qubits. By implementing phase sensitive techniques, we map out the structure of local integrals of motion in the MBL phase. Tomographic reconstruction of single and two qubit density matrices allowed us to determine the spatial and temporal entanglement growth between the localized sites. In addition, we study the preservation of entanglement in the MBL phase. The interferometric protocols implemented here measure affirmative correlations and allow us to exclude artifacts due to the imperfect isolation of the system. By measuring elusive MBL quantities, our work highlights the advantages of phase sensitive measurements in studying novel phases of matter.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Unraveling the Topological Phase of ZrTe$_5$ via Magneto-infrared Spectroscopy

For materials near the phase boundary between weak and strong topological insulators (TIs), their band topology depends on the band alignment, with the inverted (normal) band corresponding to the strong (weak) TI phase. Here, taking the anisotropic transition-metal pentatelluride ZrTe$_5$ as an example, we show that the band inversion manifests itself as a second extremum (band gap) in the layer stacking direction, which can be probed experimentally via magneto-infrared spectroscopy. Specifically, we find that the band anisotropy of ZrTe$_5$ features a slow dispersion in the layer stacking direction, along with an additional set of optical transitions from a band gap away from the Brillouin zone center. Our work identifies ZrTe5 as a strong TI at liquid helium temperature and provides a new perspective in determining band inversion in layered topological materials.

0 citations0 reviewsNo code linkedOpen access