A5086977182- Quantum Computing Algorithms and Architecture
- Quantum Information and Cryptography
- Quantum and electron transport phenomena
- Quantum many-body systems
- Advancements in Semiconductor Devices and Circuit Design
- Advanced Fiber Laser Technologies
- Laser-Matter Interactions and Applications
- Quantum Mechanics and Applications
- Nonlinear Photonic Systems
- Advanced Memory and Neural Computing
- Spectral Theory in Mathematical Physics
- Theoretical and Computational Physics
- Quantum Mechanics and Non-Hermitian Physics
- Quantum chaos and dynamical systems
- Medicinal Plant Extracts Effects
- Neural Networks and Applications
- Mental Health and Psychiatry
- Computability, Logic, AI Algorithms
- Advanced Algebra and Logic
- Adversarial Robustness in Machine Learning
- Complementary and Alternative Medicine Studies
- Thermal and Kinetic Analysis
- Neural dynamics and brain function
Tsinghua University
2021-2025
Deep learning and quantum computing have achieved dramatic progresses in recent years. The interplay between these two fast-growing fields gives rise to a new research frontier of machine learning. In this work, we report an experimental demonstration training deep neural networks via the backpropagation algorithm with six-qubit programmable superconducting processor. We experimentally perform forward process classically simulate backward process. particular, show that three-layer can be...
Machine learning has achieved dramatic success over the past decade, with applications ranging from face recognition to natural language processing. Meanwhile, rapid progress been made in field of quantum computation including developing both powerful algorithms and advanced devices. The interplay between machine physics holds intriguing potential for bringing practical modern society. Here, we focus on neural networks form parameterized circuits. We will mainly discuss different structures...
Topologically ordered phases of matter elude Landau's symmetry-breaking theory, featuring a variety intriguing properties such as long-range entanglement and intrinsic robustness against local perturbations. Their extension to periodically driven systems gives rise exotic new phenomena that are forbidden in thermal equilibrium. Here, we report the observation signatures phenomenon-a prethermal topologically time crystal-with programmable superconducting qubits arranged on square lattice. By...
Neuroevolution, a field that draws inspiration from the evolution of brains in nature, harnesses evolutionary algorithms to construct artificial neural networks. It bears number intriguing capabilities are typically inaccessible gradient-based approaches, including optimizing neural-network architectures, hyperparameters, and even learning training rules. In this paper, we introduce quantum neuroevolution algorithm autonomously finds near-optimal networks for different machine-learning...
Persistent currents circulate continuously without requiring external power sources. Here, we extend their theory to include dissipation within the framework of non-Hermitian quantum Hamiltonians. Using Green's function formalism, introduce a Fermi-Dirac distribution and derive an analytical expression for persistent current that relies solely on complex spectrum. We apply our formula two dissipative models supporting currents: (i) phase-biased superconducting-normal-superconducting...
Symmetry-protected topological phases cannot be described by any local order parameter and are beyond the conventional symmetry-breaking paradigm for understanding quantum matter. They characterized boundary states robust against perturbations that respect protecting symmetry. In a clean system without disorder, these edge modes typically only occur ground of systems with bulk energy gap would not survive at finite temperatures due to mobile thermal excitations. Here, we report observation...
<title>Abstract</title> Symmetry-protected topological phases cannot be described by any local order parameter and are beyond the conventional symmetry-breaking paradigm for understanding quantum matter. They characterized boundary modes that remain stable under symmetry respecting perturbations. In clean, gapped systems without disorder, stability of these edge is restricted to ground state manifold; at finite temperatures, interactions with mobile thermal excitations lead their decay....
Abstract Quantum tangent kernel methods provide an efficient approach to analyzing the performance of quantum machine learning models in infinite-width limit, which is crucial importance designing appropriate circuit architectures for certain tasks. Recently, they have been adapted describe convergence rate training errors neural networks analytical manner. Here, we study connections between expressibility and value concentration models. In particular, global loss functions, rigorously prove...
Quantum computers may outperform classical on machine learning tasks. In recent years, a variety of quantum algorithms promising unparalleled potential to enhance, speed up, or innovate have been proposed. Yet, systems, similar their counterparts, likewise suffer from the catastrophic forgetting problem, where training model with new tasks would result in dramatic performance drop for previously learned ones. This problem is widely believed be crucial obstacle achieving continual multiple...
As a newly developed treatment method for schizophrenia, horticultural therapy is gaining more attention. However, there as of now little research investigating this topic well general lack studies adopting into standard plans.
Catastrophic forgetting describes the fact that machine learning models will likely forget knowledge of previously learned tasks after process a new one. It is vital problem in continual scenario and recently has attracted tremendous concern across different communities. We explore catastrophic phenomena context quantum learning. found that, similar to those classical based on neural networks, systems likewise suffer from such classification emerging various application scenes. show local...
Quantum artificial intelligence exploits the interplay between and quantum physics: on one hand, a plethora of tools ideas from can be adopted to tackle intricate problems; other computing could also bring unprecedented opportunities enhance, speed up, or innovate intelligence. Yet, learning systems, similar classical ones, may suffer adversarial attacks: adding tiny carefully-crafted perturbation legitimate input data would cause systems make incorrect predictions at notably high confidence...
Quantum state tomography, a process that reconstructs quantum from measurements on an ensemble of identically prepared copies, plays crucial role in benchmarking devices. However, brute-force approaches to tomography would become impractical for large systems, as the required resources scale exponentially with system size. Here, we explore machine learning approach and report experimental demonstration reconstructing states based neural network generative models array programmable...
Topologically ordered phases of matter elude Landau's symmetry-breaking theory, featuring a variety intriguing properties such as long-range entanglement and intrinsic robustness against local perturbations. Their extension to periodically driven systems gives rise exotic new phenomena that are forbidden in thermal equilibrium. Here, we report the observation signatures phenomenon -- prethermal topologically time crystal with programmable superconducting qubits arranged on square lattice. By...
Quantum nonlocality describes a stronger form of quantum correlation than that entanglement. It refutes Einstein's belief local realism and is among the most distinctive enigmatic features mechanics. crucial resource for achieving advantages in variety practical applications, ranging from cryptography certified random number generation via self-testing to machine learning. Nevertheless, detection nonlocality, especially many-body systems, notoriously challenging. Here, we report an...
Bell nonlocality is an intrinsic feature of quantum mechanics, which can be certified via the violation inequalities. It therefore a fundamental question to certify from experimental data. Here, we present optimization scheme improve certification by exploring flexible mappings between inequalities and Hamiltonians corresponding operators. We show that several Hamiltonian models mapped new with improved classical bounds than original one, enabling more robust detection nonlocality. From...
Quantum tangent kernel methods provide an efficient approach to analyzing the performance of quantum machine learning models in infinite-width limit, which is crucial importance designing appropriate circuit architectures for certain tasks. Recently, they have been adapted describe convergence rate training errors neural networks analytical manner. Here, we study connections between trainability and expressibility models. In particular, global loss functions, rigorously prove that high both...
Machine learning has achieved dramatic success over the past decade, with applications ranging from face recognition to natural language processing. Meanwhile, rapid progress been made in field of quantum computation including developing both powerful algorithms and advanced devices. The interplay between machine physics holds intriguing potential for bringing practical modern society. Here, we focus on neural networks form parameterized circuits. We will mainly discuss different structures...
Deep learning and quantum computing have achieved dramatic progresses in recent years. The interplay between these two fast-growing fields gives rise to a new research frontier of machine learning. In this work, we report the first experimental demonstration training deep neural networks via backpropagation algorithm with six-qubit programmable superconducting processor. particular, show that three-layer can be trained efficiently learn two-qubit channels mean fidelity up 96.0% ground state...