Self-correcting quantum many-body control using reinforcement learning with tensor networks

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Simulating the dynamics of large quantum systems is a formidable yet vital pursuit for obtaining a deeper understanding of quantum mechanical phenomena. While quantum computers hold great promise for speeding up such simulations, their practical applicatio ...

Sign Language conveys information through multiple channels composed of manual (handshape, hand movement) and non-manual (facial expression, mouthing, body posture) components. Sign language assessment involves giving granular feedback to a learner, in ter ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

Contemporary AI faces important limitations that remain unresolved despite significant successes. Chief among these are the inability to acquire new knowledge without destroying old, the incomprehensible and nonengineerable nature of internal representatio ...

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

Much attention has been paid to dynamical simulation and quantum machine learning (QML) independently as applications for quantum advantage, while the possibility of using QML to enhance dynamical simulations has not been thoroughly investigated. Here we d ...

Facial paralysis is a highly burdening condition, resulting in a patient's inability to move his musculature on one or both sides of the mouth. The restoration of facial movement is crucial for enhancing the overall patients' quality of life. The use of DE ...

This study investigates Quantum Extreme Learning Machines (QELMs), which enhance classical Extreme Learning Machines (ELMs) by incorporating quantum features. ...

This thesis presents the development, construction, and benchmark of an experimental platform that combines cold fermionic 6Li atoms with locally controllable light-matter interactions. To enable local control, a new device, the cavity-microscope, was crea ...

The ability to train ever-larger neural networks brings artificial intelligence to the forefront of scientific and technical discoveries. However, their exponentially increasing size creates a proportionally greater demand for energy and computational hard ...

Entanglement forging based variational algorithms leverage the bipartition of quantum systems for addressing ground-state problems. The primary limitation of these approaches lies in the exponential summation required over the numerous potential basis stat ...

We propose an adaptive quantum algorithm to prepare accurate variational time evolved wave functions. The method is based on the projected variational quantum dynamics (pVQD) algorithm, that performs a global optimization with linear scaling in the number ...

Neurons in the human amygdala and hippocampus are classically thought to encode a person’s identity invariant to visual features. However, it remains largely unknown how visual information from higher visual cortical areas is translated into such a semanti ...

Randomized measurement protocols such as classical shadows represent powerful resources for quantum technologies, with applications ranging from quantum state characterization and process tomography to machine learning and error mitigation. Recently, the n ...

High-dimensional data must be highly structured to be learnable. Although the compositional and hierarchical nature of data is often put forward to explain learnability, quantitative measurements establishing these properties are scarce. Likewise, accessin ...

Differences in acquisition protocols or hardware result in measurable changes in image characteristics. These differences affect distributional properties and can also affect the spatial and temporal characteristics of the images. Likewise, the distributio ...

At room temperature, mechanical motion driven by the quantum backaction of light has been observed only in pioneering experiments in which an optical restoring force controls the oscillator stiffness1,2. For solid-state mechanical resonators in which oscil ...

Many empirical studies have provided evidence for the emergence of algorithmic mechanisms (abilities) in the learning of language models, that lead to qualitative improvements in the model capabilities. Yet, a theoretical characterization of how such mecha ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

Rigid body interactions are fundamental to numerous scientific disciplines, but remain challenging to simulate due to their abrupt nonlinear nature and sensitivity to complex, often unknown environmental factors. These challenges call for adaptable learnin ...

Self-correcting quantum many-body control using reinforcement learning with tensor networks

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Overhead-constrained circuit knitting for variational quantum dynamics

Posterior-Based Analysis of Spatio-Temporal Features for Sign Language Assessment

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Foundations of a New Learning Paradigm in AI Grounded in the Principles of Evolutionary Developmental Biology

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

Dynamical simulation via quantum machine learning with provable generalization

DEMO: Dielectric elastomer actuators (DEAs) for the restoration of facial movements

Harnessing Photon Indistinguishability in Quantum Extreme Learning Machines

A Cavity-Microscope for Quantum Simulations with Locally-Controllable All-to-All Interactions

Training hybrid neural networks with multimode optical nonlinearities using digital twins

Hybrid ground-state quantum algorithms based on neural Schrödinger forging

Adaptive projected variational quantum dynamics

Feature-based encoding of face identity by single neurons in the human amygdala and hippocampus

Dual-frame optimization for informationally complete quantum measurements

Probing the latent hierarchical structure of data via diffusion models

Domain shift, domain adaptation, and generalization: A focus on MRI

Room-temperature quantum optomechanics using an ultralow noise cavity

A phase transition between positional and semantic learning in a solvable model of dot-product attention

Random matrix methods for high-dimensional machine learning models

Integrating Physics and Topology in Neural Networks for Learning Rigid Body Dynamics