arXiv subjects

Sung Won Yun

Publications and source records attributed to Sung Won Yun.

3 recordsLinked to original sources

arXiv2026

Carrier-Assisted Entanglement Purification

Entanglement distillation, a fundamental building block of quantum networks, enables the purification of noisy entangled states shared among distant nodes by local operations and classical communication. Its practical realization presents several technical challenges, including the storage of quantum states in quantum memory and the execution of coherent quantum operations on multiple copies of states within the quantum memory. In this work, we present an entanglement purification protocol via quantum communication, namely a carrier-assisted entanglement purification protocol, which utilizes two elements only: i) quantum memory for a single-copy entangled state shared by parties and ii) single qubits travelling between parties. We show that the protocol, when single-qubit transmission is noiseless, can purify a noisy entangled state shared by parties. When single-qubit transmission is noisy, the purification relies on types of noisy qubit channels; we characterize Pauli channels such that the protocol works for the purification. We address this limitation by using multiple carrier qubits, and show that for any non-entanglement-breaking Pauli channel, the protocol's fixed-point fidelity approaches unity as the number of carriers increases. Our results significantly reduce the experimental overhead required for distilling entanglement: the practical advantage is demonstrated through parameters directly related to the capability of entanglement purification, such as noise in quantum memory, local measurements, channel use, and entanglement fidelity. We envisage that the protocol would make long-distance pure entanglement closer to a practical realization.

quant-ph↗

arXiv2026

Measurement circuit ansatz: Naimark versus quantum neural-network measurements

In this work, we present constructions of quantum circuits to implement general measurements on quantum hardware. Firstly, we investigate a quantum circuit ansatz by following the Naimark extension with a universal set of gates, such as controlled-NOT and single-qubit gates; we call it a {\it Naimark measurement}. We present a circuit ansatz framed by the Naimark extension, leaving single-qubit gates with parameters, and apply a classical optimizer to determine their parameters to approximate a desired quantum measurement. Secondly, we relax the Naimark measurement with quantum neural-network (QNN) circuits, employing parameterized quantum circuits. We present hybrid Naimark-QNN measurements by incorporating QNN circuits into Naimark measurements. Thirdly, we also consider fully QNN measurements with shallow parameterized circuits. Then, we compare the constructed measurement circuits, Naimark, hybrid Naimark-QNN, and fully QNN measurements, for strategies of state discrimination, such as minimum-error and maximum-confidence measurements. We demonstrate that QNN circuits can efficiently and effectively achieve near-optimal quantum measurements with fewer training iterations.

quant-ph↗

arXiv2026

Peptide Structure Prediction Using Counter-Diabatic Quantum Approximate Optimization Algorithm (CD-QAOA)

In this study, we predicted the structure of the heptapeptide APRLRFY, a neuropeptide sequence, on a tetrahedral lattice using a Quantum Approximate Optimization Algorithm (QAOA). QAOA is based on the adiabatic approximation and has been successfully applied to a wide range of optimization problems. However, relatively slow convergence during ground-state searches has frequently been reported. To overcome this limitation, we employed the Counter-Diabatic Quantum Approximate Optimization Algorithm (CD-QAOA), which introduces an additional counter-diabatic driving term into the adiabatic framework to accelerate convergence toward the ground state during peptide structure prediction. In the heptapeptide structure prediction, intermolecular interactions were modeled using two different approaches. In the first approach, only the interaction between the second residue, proline (P), and the seventh residue, tyrosine (Y), was included in the optimization. In the second approach, all residue-residue interactions within the heptapeptide were modeled using the Miyazawa-Jernigan (MJ) interaction matrix. To validate the peptide structures predicted using CD-QAOA, we additionally employed several classical computational methods, including quantum chemistry-based Hartree-Fock (HF) calculation and Density Functional Theory (DFT) calculation, conventional molecular dynamics (MD) simulation, and Hamiltonian replica exchange molecular dynamics (H-REMD) simulation. The structural similarities among the conformations obtained from these different approaches were systematically analyzed. CD-QAOA is highly effective for predicting the structures of short peptides. In particular, we demonstrate that a quantum-classical hybrid framework can significantly improve both the efficiency and accuracy of peptide structure prediction.

q-bio.BM↗