8 citations · 18 across the 11 of their papers we have counts for
12 papers
Exploration via Activation-Space Steering for Reinforcement Learning with Verifiable Rewards
Yunan Zeng, Jinhao Jing, Xi Cheng +1
Reinforcement learning with verifiable rewards (RLVR) trains a language model by sampling responses, scoring them with an automatic verifier, and updating the policy from the resul…
Threshold and Parity BosonSampling in the Linear-Mode Regime
Byeongseon Go, Hyunseok Jeong, Changhun Oh
BosonSampling is among the most prominent candidates for demonstrating quantum advantage. However, while the hardness of BosonSampling relies on photon-number-resolving detection,…
Hardness and Complexity Transition of Noisy Random Circuit Sampling
Byeongseon Go, Changhun Oh, Hyunseok Jeong
Random circuit sampling (RCS) is a leading candidate for demonstrating quantum advantage, supported by strong complexity-theoretic evidence of hardness in the ideal setting and by…
Improved sample complexity bound for sample-based Lindbladian simulation
Siheon Park, Youngjin Seo, Byeongseon Go +3
We establish improved sample-complexity bounds for sample-based Lindbladian simulation based on the Wave Matrix Lindbladization (WML) algorithm. For a jump operator with dimens…
Complexity phase transition for continuous-variable cluster state
Byeongseon Go, Hyunseok Jeong, Changhun Oh
Continuous-variable (CV) cluster states offer a promising platform for large-scale measurement-based quantum computations (MBQC). However, finite squeezing inevitably introduces Ga…
Quantum Metrology under Coarse-Grained Measurement
Byeong-Yoon Go, Geunhee Gwak, Young-Do Yoon +4
While quantum metrology enables measurement precision beyond classical limits, its performance is often susceptible to experimental imperfections. Most prior studies have focused o…
Sufficient conditions for hardness of lossy Gaussian boson sampling
Byeongseon Go, Changhun Oh, Hyunseok Jeong
Gaussian boson sampling (GBS) is a prominent candidate for the experimental demonstration of quantum advantage. However, while the current implementations of GBS are unavoidably su…
Quantum computational advantage of noisy boson sampling with partially distinguishable photons
Byeongseon Go, Changhun Oh, Hyunseok Jeong
Boson sampling stands out as a promising approach toward experimental demonstration of quantum computational advantage. However, the presence of physical noise in near-term experim…
Sample-based Hamiltonian and Lindbladian simulation: Non-asymptotic analysis of sample complexity
Byeongseon Go, Hyukjoon Kwon, Siheon Park +2
Density matrix exponentiation (DME) is a quantum algorithm that processes multiple copies of a program state to realize the Hamiltonian evolution . Wave matrix Lindb…
On computational complexity and average-case hardness of shallow-depth boson sampling
Byeongseon Go, Changhun Oh, Hyunseok Jeong
Boson sampling, a computational task believed to be classically hard to simulate, is expected to hold promise for demonstrating quantum computational advantage using near-term quan…
Exploring Shallow-Depth Boson Sampling: Towards Scalable Quantum Supremacy
Byeongseon Go, Changhun Oh, Liang Jiang +1
Boson sampling is a sampling task proven to be hard to simulate efficiently using classical computers under plausible assumptions, which makes it an appealing candidate for quantum…
Theoretical studies on quantum imaging with time-integrated single-photon detection under realistic experimental conditions
Byeong-Yoon Go, Changhyoup Lee, Kwang-Geol Lee
We study a quantum-enhanced differential measurement scheme that uses quantum probes and single-photon detectors to measure a minute defect in the absorption parameter of an analyt…