21 citations · 49 across the 14 of their papers we have counts for
13 papers · 1 filter
Machine Learning Approaches to Decoding Topological Quantum Codes
Changwon Lee, Tak Hur, Jeongwoo Jae +1
Decoding is an essential component of quantum error correction (QEC), translating stabilizer measurement outcomes into corrective actions that suppress logical errors and preserve…
Quantum Occam Learning: Sample-Supported Expressibility for Circuit-Based Quantum Learning
Jeongho Bang, Kyoungho Cho, Jeongwoo Jae
A central principle in quantum machine learning is that an ansatz should be expressive enough to represent the quantum data of interest. Yet, the expressibility is statistically me…
Learning at the Edge of Causality: Optimal Learning-Sample Complexity from No-Signaling Constraints
Jeongho Bang, Kyoungho Cho, Jeongwoo Jae
What ultimately fixes the sample cost of quantum learning -- algorithmic ingenuity or physical law? We study this question in an arena where computation, learning, and causality co…
Measurement-based Dynamical Decoupling for Fidelity Preservation on Large-scale Quantum Processors
Jeongwoo Jae, Changwon Lee, Juzar Thingna +2
Dynamical decoupling (DD) is a key technique for suppressing decoherence and preserving the performance of quantum algorithms. We introduce a measurement-based DD (MDD) protocol th…
Operational Quasiprobability in Quantum Thermodynamics: Work Extraction by Coherence and Non-joint Measurability
Jeongwoo Jae, Junghee Ryu, Hoon Ryu
We employ the operational quasiprobability (OQ) as a work distribution, which reproduces the Jarzynski equality and yields the average work consistent with the classical definition…
Support-Projected Petz Monotone Geometry of Pure Two-Qubit Families: Universal Three-Channel Decomposition and Non-Reduction of Curvature Invariants
Gunhee Cho, Jeongwoo Jae
We develop a support-projected Petz monotone geometry for pure two-qubit families, obtained by pulling back arbitrary Petz monotone quantum metrics to circuit-defined submanifolds…
Reinforcement learning to learn quantum states for Heisenberg scaling accuracy
Jeongwoo Jae, Jeonghoon Hong, Jinho Choo +1
Learning quantum states is a crucial task for realizing quantum information technology. Recently, neural approaches have emerged as promising methods for learning quantum states. W…
Contextual quantum metrology
Jeongwoo Jae, Jiwon Lee, M. S. Kim +2
Quantum metrology promises higher precision measurements than classical methods. Entanglement has been identified as one of quantum resources to enhance metrological precision. How…
Metrological power of incompatible measurements
Jeongwoo Jae, Jiwon Lee, Kwang-Geol Lee +2
We show that measurement incompatibility is a necessary resource to enhance the precision of quantum metrology. To utilize incompatible measurements, we propose a probabilistic met…
Necessary and sufficient condition for joint measurability
Jeongwoo Jae, Kyunghyun Baek, Junghee Ryu +1
In order to analyze joint measurability of given measurements, we introduce a Hermitian operator-valued measure, called -measure, such that it has marginals of positive operator…
Optical experiment to test negative probability in context of quantum-measurement selection
Junghee Ryu, Sunghyuk Hong, Joong-Sung Lee +6
Negative probability values have been widely employed as an indicator of the nonclassicality of quantum systems. Known as a quasiprobability distribution, they are regarded as a us…
Operational quasiprobabilities for continuous variables
Jeongwoo Jae, Junghee Ryu, Jinhyoung Lee
We generalize the operational quasiprobability involving sequential measurements proposed by Ryu {\em et al.} [Phys. Rev. A {\bf 88}, 052123] to a continuous-variable system. The q…
Irreducibility of coherent states
Jeongwoo Jae, Kang Hee Seol, Kwang-Geol Lee +1
We suggest an optical method which tests a nonclassical feature with a coherent state input. The test is designed with a multiplexer of on/off detectors and post-selection, adoptin…