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Adaptive variational low-rank dynamics for open quantum systems

We introduce a model-independent method for the efficient simulation of low-entropy systems, whose dynamics can be accurately described with a limited number of states. Our method leverages the time-dependent variational principle to efficiently integrate ...

Amer Physical Soc2024

Dynamical simulation via quantum machine learning with provable generalization

Zoë Holmes

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 ...

Amer Physical Soc2024

Succinct ordering and aggregation constraints in algebraic array theories

Viktor Kuncak, Rodrigo Raya

We discuss two extensions to a recently introduced theory of arrays, which are based on considerations coming from the model theory of power structures. First, we discuss how the ordering relation on the index set can be expressed succinctly by referring t ...

Elsevier Science Inc2024

Empirical Sample Complexity of Neural Network Mixed State Reconstruction

Giuseppe Carleo, Filippo Vicentini, Haimeng Zhao

Quantum state reconstruction using Neural Quantum States has been proposed as a viable tool to reduce quantum shot complexity in practical applications, and its advantage over competing techniques has been shown in numerical experiments focusing mainly on ...

Verein Forderung Open Access Publizierens Quantenwissenschaf2024

A Comparative Analysis of Tools & Task Types for Measuring Computational Problem-Solving

Richard Lee Davis, Engin Walter Bumbacher, Jérôme Guillaume Brender

How to measure students' Computational Problem-Solving (CPS) competencies is an ongoing research topic. Prevalent approaches vary by measurement tools (e.g., interactive programming, multiple-choice tests, or programming-independent tests) and task types ( ...

Association for Computing Machinery2024

Adaptive projected variational quantum dynamics

Giuseppe Carleo, David Linteau, Stefano Barison

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 ...

Amer Physical Soc2024

Dual-frame optimization for informationally complete quantum measurements

Ivano Tavernelli

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 ...

2024

Investigation of room-temperature single-photon emitters in GaN-based materials and GaN/AlN quantum dots

Johann Nicolaï Stachurski

Over the past decade, quantum photonics platforms aiming at harnessing the fundamental properties of single particles, such as quantum superposition and quantum entanglement, have flourished. In this context, single-photon emitters capable of operating at ...

EPFL2024

Graph generative deep learning models with an application to circuit topologies

Igor Krawczuk

Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison. Managing their complexity and facilitating their design drove part of the co-evolution of mode ...

EPFL2024

Hybrid ground-state quantum algorithms based on neural Schrödinger forging

Giuseppe Carleo, Sofia Vallecorsa

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 ...

Amer Physical Soc2024

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