6 papers
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…
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…
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…
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…