arXiv subjects

Jiaqi Miao

Publications and source records attributed to Jiaqi Miao.

6 recordsLinked to original sources

arXiv2026

TensorCircuit-NG: A Universal, Composable, and Scalable Platform for Quantum Computing and Quantum Simulation

We present TensorCircuit-NG, a next-generation quantum software platform designed to bridge the gap between quantum physics, artificial intelligence, and high-performance computing. Moving beyond the scope of traditional circuit simulators, TensorCircuit-NG establishes a unified, tensor-native programming paradigm where quantum circuits, tensor networks, and neural networks fuse into a single, end-to-end differentiable computational graph. Built upon industry-standard machine learning backends (JAX, TensorFlow, PyTorch), the framework introduces comprehensive capabilities for approximate circuit simulation, analog dynamics, fermion Gaussian states, qudit systems, and scalable noise modeling. To tackle the exponential complexity of deep quantum circuits, TensorCircuit-NG implements advanced distributed computing strategies, including automated data parallelism and model-parallel tensor network slicing. We validate these capabilities on GPU clusters, demonstrating a near-linear speedup in distributed variational quantum algorithms. TensorCircuit-NG enables flagship applications, including end-to-end QML for CIFAR-100 computer vision, efficient pipelines from quantum states to neural networks via classical shadows, and differentiable optimization of tensor network states for many-body physics.

quant-ph↗

arXiv2024

Self-regulated photoresponsive heterogeneous PNIPAM hydrogel actuators

Self-regulated actuators harness material intelligence to enable complex deformations and dynamics, representing a significant advancement in automated soft robotics. However, investigations on self-regulated soft actuators, particularly those using simplified actuation modules such as a unidirectional light beam, remain limited. Here, we present a design paradigm for self-regulated actuators based on Poly(N-isopropylacrylamide) (PNIPAM) heterogeneous hydrogel, where self-regulated deformations are actuated by a fixed near-infrared laser. By utilizing the different responsiveness of PNIPAM hydrogels and those integrated with reduced graphene oxide (rGO), we develop three heterogeneous hydrogel configurations: up-down, side-by-side, and hybrid. These designs enable complex biomimetic deformations in soft hydrogel actuators, resembling a bending finger or a flexible industrial manipulator, all actuated by a single fixed laser source. These proposed heterogeneous designs and actuation strategies leverage material intelligence to create soft actuators with enhanced autonomy, paving the way for soft automation, adaptive systems, and biomedical applications.

cond-mat.soft↗

arXiv2024

Bioinspired multi-asymmetric magnetized surfaces for tailoring energy-free liquid manipulation and 3-DOF solid transportation

Through the utilization of smart materials and well-designed structures, functional surfaces have been developed to enable small-scale liquid/solid manipulation tasks, thereby facilitating crucial applications in the fields of microfluidics, soft robotics, and biomedical engineering. However, the design of functional systems with flexible, tunable, and multimodal liquid/solid manipulation capabilities remains a challenging endeavor. Here, inspired by asymmetric structural features in natural plants and metachrony in cross-scale biological systems, I report a magnetic-responsive functional surface that can achieve rich liquid operations under static magnetic fields, while also enabling the transportation of solids with multiple degrees of freedom (DOFs) under dynamic magnetic fields. The presence of curvature pillars on the surface, combined with their magnetic-driven tilt/gradient arrangement, imparts liquids with multi-directional spreading modes based on the asymmetry of Laplace pressure. I elucidate the mechanisms governing these liquid spreading modes and subsequently develop compelling liquid operations, such as adjustable anti-gravity climbing, spontaneous modal shifts in liquid transport, and liquid mixing. Furthermore, the dynamic metachronal motion of the magnetic pillars can be harnessed for solid object transportation. I illustrate the synchronous/asynchronous transport modes of the surface and propose a novel strategy for achieving 3-DOF solid transportation by coordinating the arrangement of objects and employing magnetic actuation strategies. This study presents a new design concept for application-oriented manipulation surfaces, which hold significant potential for extensive engineering applications.

cond-mat.soft↗

arXiv2024

Variational post-selection for ground states and thermal states simulation

Variational quantum algorithms (VQAs), as one of the most promising routes in the noisy intermediate-scale quantum (NISQ) era, offer various potential applications while also confront severe challenges due to near-term quantum hardware restrictions. In this work, we propose a framework to enhance the expressiveness of variational quantum ansatz by incorporating variational post-selection techniques. These techniques apply variational modules and neural network post-processing on ancilla qubits, which are compatible with the current generation of quantum devices. Equipped with variational post-selection, we demonstrate that the accuracy of the variational ground state and thermal state preparation for both quantum spin and molecule systems is substantially improved. Notably, in the case of estimating the local properties of a thermalized quantum system, we present a scalable approach that outperforms previous methods through the combination of neural post-selection and a new optimization objective.

quant-ph↗

arXiv2024

Design, Actuation, and Functionalization of Untethered Soft Magnetic Robots with Life-Like Motions: A Review

Soft robots have demonstrated superior flexibility and functionality than conventional rigid robots. These versatile devices can respond to a wide range of external stimuli (including light, magnetic field, heat, electric field, etc.), and can perform sophisticated tasks. Notably, soft magnetic robots exhibit unparalleled advantages over numerous soft robots (such as untethered control, rapid response, and high safety), and have made remarkable progress in small-scale manipulation tasks and biomedical applications. Despite the promising potential, soft magnetic robots are still in their infancy and require significant advancements in terms of fabrication, design principles, and functional development to be viable for real-world applications. Recent progress shows that bionics can serve as an effective tool for developing soft robots. In light of this, the review is presented with two main goals: (i) exploring how innovative bioinspired strategies can revolutionize the design and actuation of soft magnetic robots to realize various life-like motions; (ii) examining how these bionic systems could benefit practical applications in small-scale solid/liquid manipulation and therapeutic/diagnostic-related biomedical fields.

cs.RO↗

arXiv2023

Neural network encoded variational quantum algorithms

We introduce a general framework called neural network (NN) encoded variational quantum algorithms (VQAs), or NN-VQA for short, to address the challenges of implementing VQAs on noisy intermediate-scale quantum (NISQ) computers. Specifically, NN-VQA feeds input (such as parameters of a Hamiltonian) from a given problem to a neural network and uses its outputs to parameterize an ansatz circuit for the standard VQA. Combining the strengths of NN and parameterized quantum circuits, NN-VQA can dramatically accelerate the training process of VQAs and handle a broad family of related problems with varying input parameters with the pre-trained NN. To concretely illustrate the merits of NN-VQA, we present results on NN-variational quantum eigensolver (VQE) for solving the ground state of parameterized XXZ spin models. Our results demonstrate that NN-VQE is able to estimate the ground-state energies of parameterized Hamiltonians with high precision without fine-tuning, and significantly reduce the overall training cost to estimate ground-state properties across the phases of XXZ Hamiltonian. We also employ an active-learning strategy to further increase the training efficiency while maintaining prediction accuracy. These encouraging results demonstrate that NN-VQAs offer a new hybrid quantum-classical paradigm to utilize NISQ resources for solving more realistic and challenging computational problems.

quant-ph↗