Related papers: Quantum Kernel Evaluation via Hong-Ou-Mandel Inter…
Interference of two photons at a beamsplitter is at the core of many quantum photonic technologies, such as quantum key distribution or linear-optics quantum computing. Observing high-visibility interference is challenging because of the…
Topological insulators are materials that have a gapped bulk energy spectrum, but contain protected in-gap states appearing at their surface. These states exhibit remarkable properties such as unidirectional propagation and robustness to…
We report a technique for experimental characterization of an $M$-mode quantum optical process, generalizing the single-mode coherent-state quantum-process tomography method [M. Lobino et al., Science 322, 563 (2008); A. Anis and A.I.…
Quantum kernel methods are a promising method in quantum machine learning thanks to the guarantees connected to them. Their accessibility for analytic considerations also opens up the possibility of prescreening datasets based on their…
Quantum entanglement generation is generally known to be impossible by any classical means. According to Poisson statistics, coherent photons are not considered quantum particles due to the bunching phenomenon. Recently, a coherence…
We study the influence of the relativistic redshift on Hong-Ou-Mandel (HOM) interference, and present a genuine quantum test of general relativity. We use Glauber's theory of quantum coherence to predict the coincidence probability of…
Quantum computing can empower machine learning models by enabling kernel machines to leverage quantum kernels for representing similarity measures between data. Quantum kernels are able to capture relationships in the data that are not…
Two-photon interference is a fundamental quantum optics effect with numerous applications in quantum information science. Here, we study two-photon interference in multiple transverse-spatial modes along a single beam-path. Besides…
Quantum kernels quantify similarity between data points by measuring the inner product between quantum states, computed through quantum circuit measurements. By embedding data into quantum systems, quantum kernel feature maps, that may be…
Quantum algorithms based on quantum kernel methods have been investigated previously [1]. A quantum advantage is derived from the fact that it is possible to construct a family of datasets for which, only quantum processing can recognise…
The celebrated Hong--Ou--Mandel effect illustrates the richness of two-photon interferometry. In this work, we demonstrate that this extends to the realm of time-frequency interferometry. Taking advantage of the mathematical analogy which…
Measurement-device-independent quantum key distribution (MDI-QKD) has garnered significant attention for its potential to enable security-loophole-free quantum communication. Successful MDI-QKD protocols rely on performing a two-photon…
We discuss characterization of single-photon wave packets by measuring Hong-Ou-Mandel interference with a weak coherent pulse. A complete multimode calculation is presented and effects of multiphoton terms in the coherent field as well as…
Extending Hong--Ou--Mandel (HOM) interferometry to the fractional quantum Hall effect (FQHE) promises direct access to anyonic statistics, yet remains challenging: on-demand anyon injection is hindered by integer-charged minimal…
Multiphoton interference effects can be measured with a single detector when two input photons are temporally well separated when compared with the dead time of the single-photon avalanche detector. Here we experimentally demonstrate that…
We investigate the relation between indistinguishability and quantum entanglement in Hong- Ou-Mandel-interference experiments theoretically and relate these quantum mechanical principles to the theorem of entanglement monogamy. Employing…
Single photons exhibit inherently quantum and unintuitive properties such as the Hong-ou-Mandel effect, demonstrating their bosonic and quantized nature, yet at the same time may correspond to single excitations of spatial or temporal modes…
We develop a fully spatio-temporal numerical model, based on stochastic simulations, simulating the generation of spatio-temporal multimode spontaneous parametric down conversion, the propagation of the signal and idler beams through a…
We employ so-called quantum kernel estimation to exploit complex quantum dynamics of solid-state nuclear magnetic resonance for machine learning. We propose to map an input to a feature space by input-dependent Hamiltonian evolution, and…
We propose digital-analog quantum kernels for enhancing the detection of complex features in the classification of images. We consider multipartite-entangled analog blocks, stemming from native Ising interactions in neutral-atom quantum…