Related papers: Pulse Shape Discrimination for Germanium Detectors…
Quantum Phase Estimation (QPE) stands as a pivotal quantum computing subroutine that necessitates an inverse Quantum Fourier Transform (QFT). However, it is imperative to recognize that enhancing the precision of the estimation inevitably…
Advancements in the implementation of quantum hardware have enabled the acquisition of data that are intractable for emulation with classical computers. The integration of classical machine learning (ML) algorithms with these data holds…
Image classification, a pivotal task in multiple industries, faces computational challenges due to the burgeoning volume of visual data. This research addresses these challenges by introducing two quantum machine learning models that…
The emerging field of quantum simulation of many-body systems is widely recognized as a very important application of quantum computing. A crucial step towards its realization in the context of many-electron systems requires a rigorous…
Partial differential equations (PDEs) are fundamental across numerous scientific fields. As these problems scale to high dimensions, classical numerical schemes introduce severe computational bottlenecks, known as the curse of…
SuperCDMS is the next phase of the Cryogenic Dark Matter Search experiment, which measures both phonon and charge signals generated by particle recoils within a germanium target mass. Charge signals are employed both in the definition of a…
An optical transmitter that uses entangled light generated by spontaneous parametric downconversion (SPDC), in conjunction with an optimal quantum-optical receiver (whose implementation is not yet known) is in principle capable of obtaining…
Quantum machine learning (QML) is an emerging field that promises advantages such as faster training, improved reliability and superior feature extraction over classical counterparts. However, its implementation on quantum hardware is…
We introduce a distributed quantum-classical framework that synergizes photonic quantum neural networks (QNNs) with matrix-product-state (MPS) mapping to achieve parameter-efficient training of classical neural networks. By leveraging…
Quantum computing promises to speed up some of the most challenging problems in science and engineering. Quantum algorithms have been proposed showing theoretical advantages in applications ranging from chemistry to logistics optimization.…
Barcoding photons can provide a host of functionalities that could benefit future quantum communication systems and networks beyond today's imagination. As a significant application of barcoding photons, we introduce code division…
Quantum ensemble classification has significant applications in discrimination of atoms (or molecules), separation of isotopic molecules and quantum information extraction. However, quantum mechanics forbids deterministic discrimination…
Reservoir computing leverages rich, non-linear dynamics to process temporal data. Quantum variants promise enhanced expressivity from high-dimensional Hilbert spaces, yet their practical applicability is hindered by hardware noise and…
P-type point contact (PPC) germanium detectors are used in rare event and low-background searches, including neutrinoless double beta (0vbb) decay, low-energy nuclear recoils, and coherent elastic neutrino-nucleus scattering. The detectors…
A brief outline of Dark Matter detection experiments using liquid argon technology is presented. The Pulse Shape background discrimination method (PSD) is described and the example of its use in 2.3 l R&D detector is given. Methods of…
In this study, we present an innovative methodology to classify quantum phases within the ANNNI (Axial Next-Nearest Neighbor Ising) model by combining Quantum Machine Learning (QML) techniques with the Shapley Additive Explanations (SHAP)…
The basic idea of quantum computing is surprisingly similar to that of kernel methods in machine learning, namely to efficiently perform computations in an intractably large Hilbert space. In this paper we explore some theoretical…
The continued advancements of Silicon Photomultipliers (SiPMs) have made them viable photosensors for low recoil energy Pulse Shape Discrimination (PSD) between fast neutron and gamma interactions when coupled to an appropriate…
A software package for modeling segmented High-Purity Segmented Germanium detectors, AGATAGeFEM, is presented. The choices made for geometry implementation and the calculations of the electric and weighting fields are discussed. Models used…
In recent years, notable progress has been made in the study of hole qubits in planar germanium, and circuit quantum electrodynamics (circuit QED) has emerged as a promising approach for achieving long-range coupling and scaling up of…