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Entanglement, which quantifies non-local correlations in quantum mechanics, is the fascinating concept behind much of aspiration towards quantum technologies. Nevertheless, directly measuring the entanglement of a many-particle system is…
Quantum Machine Learning(QML) is developed by combining quantum mechanics principles with classical machine learning techniques in a hybrid framework that can give faster, exponential, more efficient power of quantum computing with the data…
Quasiparticle - a key concept to describe interacting particles - characterizes electron-electron interaction in metals (Fermi liquid) and electron pairing in superconductors. While this concept essentially relies on the simplification of…
Identifying, quantifying, and suppressing decoherence mechanisms in qubits are important steps towards the goal of engineering a quantum computer or simulator. Superconducting circuits offer flexibility in qubit design; however, their…
This paper reports a novel method for supervised machine learning based on the mathematical formalism that supports quantum mechanics. The method uses projective quantum measurement as a way of building a prediction function. Specifically,…
Quantum machine learning (QML) has great potential for the analysis of chemical datasets. However, conventional quantum data-encoding schemes, such as fingerprint encoding, are generally unfeasible for the accurate representation of…
The immunity of Ising superconductors to external magnetic fields originates from a spin locking of the paired electrons to an intrinsic Zeeman-like field. The spin-momentum locking in non-centrosymmetric crystalline materials leads to…
Recently, machine learning has been widely applied in the field of quantum information, notably in tasks such as entanglement detection, steering characterization, and nonlocality verification. However, few studies have focused on utilizing…
Quantum properties, such as entanglement and coherence, are indispensable resources in various quantum information processing tasks. However, there still lacks an efficient and scalable way to detecting these useful features, especially for…
Millimeter-wave massive multiple-input multiple-output systems employ highly directional beamforming to overcome severe path loss, and their performance critically depends on accurate beam alignment. Conventional codebook-based methods…
Using a realistic ten-orbital tight-binding model Hamiltonian fitted to the angle-resolved photoemission (ARPES) data on LiFeAs, we analyze the temperature, frequency, and momentum dependencies of quasiparticle interference (QPI) to…
Quantitative phase imaging (QPI) has been widely applied in characterizing cells and tissues. Spatial light interference microscopy (SLIM) is a highly sensitive QPI method, due to its partially coherent illumination and common path…
Quantum entanglement is a key resource in quantum computing and quantum information processing tasks. However, its quantification remains a major challenge since it cannot be directly extracted from physical observables. To address this…
Unlike charge and spin, the orbital degree of freedom of electrons in transition metal oxides is difficult to detect. We present the theoretical study of a new detection method in metallic orbitally active systems by analyzing the…
Quasiparticle interference (QPI) of the electronic states has been widely applied in scanning tunneling microscopy (STM) to analyze the electronic band structure of materials. Single-defect induced QPI reveals defect-dependent interaction…
Accurate prediction of compound-protein interactions (CPI) remains a cornerstone challenge in computational drug discovery. While existing sequence-based approaches leverage molecular fingerprints or graph representations, they critically…
We propose a way to determine the pairing state of the iron pnictide superconductors exploiting the momentum (Q) scan of the neutron scattering data. We investigate the spin susceptibility in the s+- and s++ superconducting states for…
Here we present the results of benchmarking a quantum processor based on trapped $^{171}$Yb$^{+}$ ions by performing basic quantum machine learning algorithms. Using a quantum-enhanced support vector machine algorithm with up to five qubits…
In this paper we explore the behavior of the quasi-particle interference pattern (QPI) of scanning tunneling microscopy as a function of temperature, $T$. After insuring a minimal consistency with photoemission, we find that the QPI pattern…
The capabilities of image probe experiments are rapidly expanding, providing new information about quantum materials on unprecedented length and time scales. Many such materials feature inhomogeneous electronic properties with intricate…