80 citations · 636 across the 44 of their papers we have counts for
46 papers
Metrological Benchmarking of Random Quantum Circuits
Simone Cavazzoni, Changhun Oh
Random circuit sampling is a leading approach to demonstrating quantum computational advantage, but benchmarking noisy implementations through linear cross-entropy requires costly…
Efficient classical algorithm for estimating linear statistics of Boson Sampling
Benoit Seron, Hugo Thomas, Eduardo Araujo +3
Boson Sampling is a prominent candidate for the demonstration of quantum computational advantage but it remains unclear whether a large-scale boson sampler can find useful computat…
Ultimate Information Rate for Quantum Sensing under Multilevel Relaxation
Changhun Oh, Seok Hyung Lie, Youngrong Lim
We determine the ultimate information rate of a relaxing multilevel quantum sensor under unrestricted adaptive control and construct an explicit strategy that attains it. We consid…
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…
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…
Virtual purification complements quantum error correction in quantum metrology
Hyukgun Kwon, Changhun Oh, Youngrong Lim +3
Quantum resources enable one to achieve quantum-enhanced estimation sensitivity beyond its classical counterpart. Many studies mainly focus on reducing statistical error, under the…
Classical algorithms for estimating expectation values in linear-optical circuits
Youngrong Lim, Changhun Oh
We present a classical algorithm for approximating the expectation values of observables in linear-optical circuits with arbitrary product input states, achieving additive-error ac…
Advancing quantum imaging through learning theory
Yunkai Wang, Changhun Oh, Junyu Liu +2
We study quantum imaging by applying the resolvable expressive capacity (REC) formalism developed for physical neural networks (PNNs). In this paradigm of quantum learning, the ima…
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…
All-optical Loss-tolerant Distributed Quantum Sensing
Rajveer Nehra, Changhun Oh, Liang Jiang +1
Distributed quantum sensing (DQS) leverages quantum resources to estimate an unknown global property of a networked quantum sensor beyond the classical limit. We propose and analyz…
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…
Entanglement-enabled advantage for learning a bosonic random displacement channel
Changhun Oh, Senrui Chen, Yat Wong +8
We show that quantum entanglement can provide an exponential advantage in learning properties of a bosonic continuous-variable (CV) system. The task we consider is estimating a pro…
Universal Spreading of Conditional Mutual Information in Noisy Random Circuits
Su-un Lee, Changhun Oh, Yat Wong +2
We study the evolution of conditional mutual information in generic open quantum systems, focusing on one-dimensional random circuits with interspersed local noise. Unlike in noise…
Tight bounds on Pauli channel learning without entanglement
Senrui Chen, Changhun Oh, Sisi Zhou +2
Quantum entanglement is a crucial resource for learning properties from nature, but a precise characterization of its advantage can be challenging. In this work, we consider learni…
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…
Classical algorithm for simulating experimental Gaussian boson sampling
Changhun Oh, Minzhao Liu, Yuri Alexeev +2
Gaussian boson sampling is a promising candidate for showing experimental quantum advantage. While there is evidence that noiseless Gaussian boson sampling is hard to efficiently s…
Efficacy of virtual purification-based error mitigation on quantum metrology
Hyukgun Kwon, Changhun Oh, Youngrong Lim +2
Noise is the main source that hinders us from fully exploiting quantum advantages in various quantum informational tasks. However, characterizing and calibrating the effect of nois…
Towards Symmetry-Aware Efficient Simulation of Quantum Systems and Beyond
Min Chen, Minzhao Liu, Changhun Oh +3
The efficient simulation of complex quantum systems remains a central challenge due to the exponential growth of Hilbert space with system size. Tensor network methods have long be…
Quantum-inspired classical algorithm for graph problems by Gaussian boson sampling
Changhun Oh, Bill Fefferman, Liang Jiang +1
We present a quantum-inspired classical algorithm that can be used for graph-theoretical problems, such as finding the densest -subgraph and finding the maximum weight clique, w…
Simulating lossy Gaussian boson sampling with matrix product operators
Minzhao Liu, Changhun Oh, Junyu Liu +2
Gaussian boson sampling, a computational model that is widely believed to admit quantum supremacy, has already been experimentally demonstrated and is claimed to surpass the classi…
Approximating outcome probabilities of linear optical circuits
Youngrong Lim, Changhun Oh
Quasiprobability representation is an important tool for analyzing a quantum system, such as a quantum state or a quantum circuit. In this work, we propose classical algorithms spe…
Spoofing cross entropy measure in boson sampling
Changhun Oh, Liang Jiang, Bill Fefferman
Cross entropy (XE) measure is a widely used benchmarking to demonstrate quantum computational advantage from sampling problems, such as random circuit sampling using superconductin…
Information transmission with continuous variable quantum erasure channels
Changchun Zhong, Changhun Oh, Liang Jiang
Quantum capacity, as the key figure of merit for a given quantum channel, upper bounds the channel's ability in transmitting quantum information. Identifying different type of chan…