arXiv subjects

Sunghwa Kang

Publications and source records attributed to Sunghwa Kang.

3 recordsLinked to original sources

arXiv2026

Quantum Radar Cross Section with two-photon entangled states

We study two-photon entangled states for quantum radar cross section (QRCS), which is an extension of a single-photon QRCS formula. Since signal-idler entanglement does not provide any enhancement of the QRCS [Brandsema's PhD Thesis (2017)], we focus on signal-signal entanglement and derive the corresponding biphoton QRCS. We show that it can provide an enhancement over the single-photon QRCS and two-photon separable QRCS, where the performance is evaluated for various two-dimensional target geometries in monostatic/bistatic configurations. Furthermore, using the double-Gaussian approximation, we derive QRCS formula for biphoton states with arbitrary degree of entanglement and compute the resulting scattering patterns.

quant-ph↗

arXiv2025

Single-shot detection limits of quantum illumination with multi-qudit states

Quantum illumination is a protocol for detecting a low-reflectivity target by using two-mode entangled states composed of signal and idler modes, which can outperform unentangled states. We study multi-qudit states for single-shot detection limits of quantum illumination under white noise environment. Using three-qubit states, we obtain that the performance is enhanced by the entanglement between signal and idler qubits, whereas it is degraded by the entanglement between signal qubits. The similar behaviors are also observed for three-qutrit, four-qubit, and four-ququart states. In particular, the optimal state is not a maximally entangled multipartite state but a combination of a maximally entangled bipartite state. Moreover, we show that quantum correlation can explain the quantum advantage of three-qubit, three-qutrit, and four-qubit states, with exception of a four-ququart state.

quant-ph↗

arXiv2024

Noise-robust chemical reaction networks training artificial neural networks

Artificial neural networks (NNs) can be implemented using chemical reaction networks (CRNs), where the concentrations of species act as inputs and outputs. In such biochemical computing, noise-robust computing is crucial due to the intrinsic and extrinsic noise present in chemical reactions. Previously suggested CRNs for feed-forward networks often utilized the rectified linear unit (ReLU) or discrete activation functions. However, one concern in this case is the discontinuities of the derivatives of those non-smooth functions, which can cause significant noise disruption during backpropagation. In this study, we propose a CRN that performs both feed-forward and training processes using smooth activation functions to avoid discontinuities in the backpropagation. All reactions occur in a single pot, and the reactions for training are bimolecular. Our case studies on XOR, Iris, MNIST datasets, and a non-linear regression model demonstrate that computation via the CRN (i) maintains accuracy despite noise in the reaction rates and the concentration of species and (ii) is insensitive to the choice of the running time and the magnitude of the noise in comparison to NNs with a non-smooth activation function. This work presents a noise-robust CRN for full NN computation, including backpropagation, paving the way for more stable and efficient biochemical computing systems.

q-bio.MN↗