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Found 13 papers, 5 papers with code
Demonstration of Robust and Efficient Quantum Property Learning with Shallow Shadows
This combined theoretical and experimental analysis positions the robust shallow shadow protocol as a scalable, robust, and sample-efficient protocol for characterizing quantum states on current quantum computing platforms.
OMNIINPUT: A Model-centric Evaluation Framework through Output Distribution
We propose a novel model-centric evaluation framework, OmniInput, to evaluate the quality of an AI/ML model's predictions on all possible inputs (including human-unrecognizable ones), which is crucial for AI safety and reliability.
Quantum Generative Modeling of Sequential Data with Trainable Token Embedding
Generative models are a class of machine learning models that aim to learn the underlying probability distribution of data.
Data driven modeling for self-similar dynamics
For deterministic dynamics, our framework can discern whether the dynamics are self-similar.
Gradient-based Wang-Landau Algorithm: A Novel Sampler for Output Distribution of Neural Networks over the Input Space
We first draw the connection between the output distribution of a NN and the density of states (DOS) of a physical system.
Hamiltonian-Driven Shadow Tomography of Quantum States
It relies on a unitary channel that efficiently scrambles the quantum information of the state to the measurement basis.
High-order Time-Reversal Symmetry Breaking Normal State
The phase locking, i. e., ordering of $\theta_-$, can take place in the phase fluctuation regime before the onset of superconductivity, i. e. when $\theta_+$ is disordered.
Superconductivity Strongly Correlated Electrons
RG-Flow: A hierarchical and explainable flow model based on renormalization group and sparse prior
In this work, we incorporate the key ideas of renormalization group (RG) and sparse prior distribution to design a hierarchical flow-based generative model, RG-Flow, which can separate information at different scales of images and extract disentangled representations at each scale.
Information Scrambling in Quantum Neural Networks
In this Letter, we show that this process can also be viewed from the opposite direction: the quantum information in the output qubits is scrambled into the input.
Emergent Quantum Mechanics in an Introspective Machine Learning Architecture
Can physical concepts and laws emerge in a neural network as it learns to predict the observation data of physical systems?
Disordered Systems and Neural Networks Quantum Physics
Out-of-Time-Order Correlation in Marginal Many-Body Localized Systems
We show that the out-of-time-order correlation (OTOC) $ \langle W(t)^\dagger V(0)^\dagger W(t)V(0)\rangle$ in many-body localized (MBL) and marginal MBL systems can be efficiently calculated by the spectrum bifurcation renormalization group (SBRG).
Strongly Correlated Electrons Disordered Systems and Neural Networks
Disordered XYZ Spin Chain Simulations using the Spectrum Bifurcation Renormalization Group
We argue that, with sufficiently strong disorder, these spin glass phases are separated by marginally many-body localized (MBL) critical lines.
Strongly Correlated Electrons Disordered Systems and Neural Networks
Entanglement Holographic Mapping of Many-Body Localized System by Spectrum Bifurcation Renormalization Group
We introduce the spectrum bifurcation renormalization group (SBRG) as a generalization of the real-space renormalization group for the many-body localized (MBL) system without truncating the Hilbert space.
Strongly Correlated Electrons Disordered Systems and Neural Networks Quantum Physics
