Yi‐Zhuang You
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View article: Thermal‐Robust and Glass‐Compatible K <sub>1.3</sub> Al <sub>9</sub> Ga <sub>2</sub> O <sub>17.15</sub> :Eu for Laser‐Driven Lighting and Underwater Optical Communication
Thermal‐Robust and Glass‐Compatible K <sub>1.3</sub> Al <sub>9</sub> Ga <sub>2</sub> O <sub>17.15</sub> :Eu for Laser‐Driven Lighting and Underwater Optical Communication Open
For specialized lighting applications in harsh environments, such as solid‐state lighting (SSL) and underwater wireless optical communication (UWOC), phosphors must harbor both exceptional thermal stability and high luminescence efficiency…
View article: Designing Shadow Tomography Protocols by Natural Language Processing
Designing Shadow Tomography Protocols by Natural Language Processing Open
Quantum circuits form a foundational framework in quantum science, enabling the description, analysis, and implementation of quantum computations. However, designing efficient circuits, typically constructed from single- and two-qubit gate…
View article: Dual-unitary shadow tomography
Dual-unitary shadow tomography Open
We introduce “dual-unitary shadow tomography'' (DUST), a classical shadow tomography protocol based on dual-unitary brick-wall circuits. To quantify the performance of DUST, we study operator spreading and Pauli weight dynamics in one-dime…
View article: Sequential learning on a tensor network Born machine with trainable token embedding
Sequential learning on a tensor network Born machine with trainable token embedding Open
Generative models aim to learn the probability distributions underlying data, enabling the generation of new, realistic samples. Quantum-inspired generative models, such as Born machines based on the matrix product state (MPS) framework, h…
View article: Demonstration of robust and efficient quantum property learning with shallow shadows
Demonstration of robust and efficient quantum property learning with shallow shadows Open
View article: Deconfined criticality as intrinsically gapless topological state in one dimension
Deconfined criticality as intrinsically gapless topological state in one dimension Open
Deconfined criticality and gapless topological states have recently attracted growing attention, as both phenomena go beyond the traditional Landau paradigm. However, the deep connection between these two critical states, particularly in l…
View article: Machine Learning for Ground State Preparation via Measurement and Feedback
Machine Learning for Ground State Preparation via Measurement and Feedback Open
We present a recurrent neural network-based approach for ground state preparation utilizing mid-circuit measurement and feedback. Unlike previous methods that use machine learning solely as an optimizer, our approach dynamically adjusts qu…
View article: Topological Responses of the Standard Model Gauge Group
Topological Responses of the Standard Model Gauge Group Open
The local Lie algebra of the Standard Model (SM) is $su(3)\times su(2) \times u(1)$, yet its global gauge group, $G_{{\rm SM}_{\rm q}}=$SU(3)$\times$SU(2)$\times$U(1)/$\mathbb{Z}_{\rm q}$, q$=1,2,3,6$ remains undetermined. Building on prev…
View article: C-R-T Fractionalization in the First Quantized Hamiltonian Theory
C-R-T Fractionalization in the First Quantized Hamiltonian Theory Open
Recent research has revealed that the CRT symmetry for fermions exhibits a fractionalization distinct from the $\mathbb{Z}_2^{\mathcal{C}}\times\mathbb{Z}_2^{\mathcal{R}}\times\mathbb{Z}_2^{\mathcal{T}}$ for scalar bosons. In fact, the CRT…
View article: Contractive Unitary and Classical Shadow Tomography
Contractive Unitary and Classical Shadow Tomography Open
The rapid development of quantum technology demands efficient characterization of complex quantum many-body states. However, full quantum state tomography requires an exponential number of measurements in system size, preventing its practi…
View article: Stability of Phosphors for White LED Excitable by Violet Light
Stability of Phosphors for White LED Excitable by Violet Light Open
View article: Realizing triality and $p$-ality by lattice twisted gauging in (1+1)d quantum spin systems
Realizing triality and $p$-ality by lattice twisted gauging in (1+1)d quantum spin systems Open
In this paper, we study the twisted gauging on the (1+1)d lattice and construct various non-local mappings on the lattice operators. To be specific, we define the twisted Gauss law operator and implement the twisted gauging of the finite g…
View article: Monte Carlo Simulation of Operator Dynamics and Entanglement in Dual-Unitary Circuits
Monte Carlo Simulation of Operator Dynamics and Entanglement in Dual-Unitary Circuits Open
We investigate operator dynamics and entanglement growth in dual-unitary circuits, a class of locally scrambled quantum systems that enables efficient simulation beyond the exponential complexity of the Hilbert space. By mapping the operat…
View article: Holographic Classical Shadow Tomography
Holographic Classical Shadow Tomography Open
We introduce "holographic shadows", a new class of randomized measurement schemes for classical shadow tomography that achieves the optimal scaling of sample complexity for learning geometrically local Pauli operators at any length scale, …
View article: Realizing triality and $p$-ality by lattice twisted gauging in (1+1)d quantum spin systems
Realizing triality and $p$-ality by lattice twisted gauging in (1+1)d quantum spin systems Open
In this paper, we study the twisted gauging on the (1+1)d lattice and construct various non-local mappings on the lattice operators. To be specific, we define the twisted Gauss law operator and implement the twisted gauging of the finite g…
View article: Optical Conductivity in Symmetric Mass Generation Insulators
Optical Conductivity in Symmetric Mass Generation Insulators Open
Symmetric mass generation (SMG) insulators are interaction-driven, featureless Mott insulating states in quantum many-body fermionic systems. Recent advancements suggest that zeros in the fermion Green's function could lead to non-vanishin…
View article: Dual-unitary shadow tomography
Dual-unitary shadow tomography Open
We introduce ``dual-unitary shadow tomography'' (DUST), a classical shadow tomography protocol based on dual-unitary brick-wall circuits. To quantify the performance of DUST, we study operator spreading and Pauli weight dynamics in one-dim…
View article: Observing Schrödinger’s cat with artificial intelligence: emergent classicality from information bottleneck
Observing Schrödinger’s cat with artificial intelligence: emergent classicality from information bottleneck Open
We train a generative language model on the randomized local measurement data collected from Schrödinger’s cat quantum state. We demonstrate that the classical reality emerges in the language model due to the information bottleneck: althou…
View article: Renormalization Group flow, Optimal Transport and Diffusion-based Generative Model
Renormalization Group flow, Optimal Transport and Diffusion-based Generative Model Open
Diffusion-based generative models represent a forefront direction in generative AI research today. Recent studies in physics have suggested that the renormalization group (RG) can be conceptualized as a diffusion process. This insight moti…
View article: Quantum Magnetism in Wannier-Obstructed Mott Insulators
Quantum Magnetism in Wannier-Obstructed Mott Insulators Open
We develop a strong coupling approach towards quantum magnetism in Mott insulators for Wannier-obstructed bands. Despite the lack of Wannier orbitals, electrons can still singly occupy a set of exponentially localized but nonorthogonal orb…
View article: Quantum Generative Modeling of Sequential Data with Trainable Token Embedding
Quantum Generative Modeling of Sequential Data with Trainable Token Embedding Open
Generative models are a class of machine learning models that aim to learn the underlying probability distribution of data. Unlike discriminative models, generative models focus on capturing the data's inherent structure, allowing them to …
View article: Probing the edge states of Chern insulators using microwave impedance microscopy
Probing the edge states of Chern insulators using microwave impedance microscopy Open
Microwave impedance microscopy (MIM) has been utilized to directly visualize topological edge states in many quantum materials. While the microwave response for conventional metals and insulators can be accurately quantified using simple l…
View article: C-R-T Fractionalization, Fermions, and Mod 8 Periodicity
C-R-T Fractionalization, Fermions, and Mod 8 Periodicity Open
Charge conjugation (C), mirror reflection (R), time reversal (T), and fermion parity $(-1)^{\rm F}$ are basic discrete spacetime and internal symmetries of the Dirac fermions. In this article, we determine the group, called the C-R-T fract…
View article: Review of: "Mass Creation via the Phase Transition of the Higgs Field"
Review of: "Mass Creation via the Phase Transition of the Higgs Field" Open
The paper aims to present a novel theoretical approach to understanding the Higgs mechanism, proposing a first-order phase transition due to explicit symmetry breaking in the Higgs potential.While the subject is of significant interest, I …
View article: Evaluation of human-model prediction difference on the Internet Scale of Data
Evaluation of human-model prediction difference on the Internet Scale of Data Open
Evaluating models on datasets often fails to capture their behavior when faced with unexpected and diverse types of inputs. It would be beneficial if we could evaluate the difference between human annotation and model prediction for an int…
View article: Fermi surface symmetric mass generation: a quantum Monte-Carlo study
Fermi surface symmetric mass generation: a quantum Monte-Carlo study Open
The symmetric mass generation (SMG) phase is an insulator in which a single-particle gap is intrinsically opened by the interaction, without involving symmetry spontaneously breaking or topological order. Here, we perform unbiased quantum …
View article: Quantum Generative Modeling of Sequential Data with Trainable Token Embedding
Quantum Generative Modeling of Sequential Data with Trainable Token Embedding Open
Generative models are a class of machine learning models that aim to learn the underlying probability distribution of data. Unlike discriminative models, generative models focus on capturing the data's inherent structure, allowing them to …
View article: Data driven modeling for self-similar dynamics
Data driven modeling for self-similar dynamics Open
Multiscale modeling of complex systems is crucial for understanding their intricacies. Data-driven multiscale modeling has emerged as a promising approach to tackle challenges associated with complex systems. On the other hand, self-simila…
View article: Machine learning renormalization group for statistical physics
Machine learning renormalization group for statistical physics Open
We develop a machine-learning renormalization group (MLRG) algorithm to explore and analyze many-body lattice models in statistical physics. Using the representation learning capability of generative modeling, MLRG automatically learns the…
View article: Strong Pairing Originated from an Emergent $\mathbb{Z}_2$ Berry Phase in La$_3$Ni$_2$O$_7$
Strong Pairing Originated from an Emergent $\mathbb{Z}_2$ Berry Phase in La$_3$Ni$_2$O$_7$ Open
The recent discovery of high-temperature superconductivity in La$_3$Ni$_2$O$_7$ offers a fresh platform for exploring unconventional pairing mechanisms. Starting with the basic argument that the electrons in $d_{z^2}$ orbitals nearly form …