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
Chaos sets a fundamental limit to quantum-information processing schemes. We study the onset of chaos in spatially extended quantum many-body systems that are relevant to quantum optical devices. We consider an extended version of the Tavis-Cummings model ...
Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal calls to oracles (computational property predictors). This problem becomes more apparen ...
Quantum optics studies how photons interact with other forms of matter, the understanding of which was crucial for the development of quantum mechanics as a whole. Starting from the photoelectric effect, the quantum property of light has led to the develop ...
Quantum computing has made significant progress in recent years, with Google and IBM releasing quantum computers with 72 and 50 qubits, respectively. Google has also achieved quantum supremacy with its 54-qubit device, and IBM has announced the release of ...
The impressive pace of advance of quantum technology calls for robust and scalable techniques for the characterization and validation of quantum hardware. Quantum process tomography, the reconstruction of an unknown quantum channel from measurement data, r ...
Quantum many-body control is a central milestone en route to harnessing quantum technologies. However, the exponential growth of the Hilbert space dimension with the number of qubits makes it challenging to classically simulate quantum many-body systems an ...
Frequency-bin qubits get the best of time-bin and dual-rail encodings, but require external modulators and pulse shapers to build arbitrary states. Here, instead, the authors work directly on-chip by controlling the interference of biphoton amplitudes gene ...
A new paradigm for data science has emerged, with quantum data, quantum models, and quantum computational devices. This field, called quantum machine learning (QML), aims to achieve a speedup over traditional machine learning for data analysis. However, it ...
The grand challenge of scaling up quantum computers requires a full-stack architectural standpoint. In this position paper, we will present the vision of a new generation of scalable quantum computing architectures featuring distributed quantum cores (Qcor ...
