18 citations · 22 across the 11 of their papers we have counts for
12 papers
Efficient Learning of Fermionic Magic States under Free-Fermion Evolution
Jiwon Heo, Myeongjin Shin, Changhun Oh
We establish efficient learning for a family of fermionic magic states under unknown number-conserving free-fermion evolution. Each input block has a definite particle number and i…
Emergent Prethermal Symmetries for Scalable Hamiltonian Learning
Myeongjin Shin, Junseo Lee, Iman Marvian +1
Learning an interacting many-body Hamiltonian at the Heisenberg limit generally requires control that preserves local information for evolution times of order to at…
Hamiltonian Learning and Certification via Eigenphase Engineering
Myeongjin Shin, Yu Tong
We study Hamiltonian learning for -local Hamiltonians at large step size, using only forward time evolution at integer multiples of a fixed time interval. For this task, our new…
Heisenberg-limited Hamiltonian learning without short-time control
Myeongjin Shin, Junseo Lee, Changhun Oh
Characterizing quantum systems by learning their underlying Hamiltonians is a central task in quantum information science. While recent algorithmic advances have achieved near-opti…
Certifying and learning local quantum Hamiltonians
Andreas Bluhm, Matthias C. Caro, Francisco Escudero Gutiérrez +4
In this work, we study the problems of certifying and learning quantum -local Hamiltonians, for a constant . Our main contributions are as follows: - Certification of Hamilto…
Optimal certification of constant-local Hamiltonians
Junseo Lee, Myeongjin Shin
We study the problem of certifying local Hamiltonians from real-time access to their dynamics. Given oracle access to for an unknown -local Hamiltonian and a full…
Near optimal quantum algorithm for estimating Shannon entropy
Myeongjin Shin, Kabgyun Jeong
We present a near-optimal quantum algorithm, up to logarithmic factors, for estimating the Shannon entropy in the quantum probability oracle model. Our approach combines the singul…
Bounding quantum uncommon information with quantum neural estimators
Donghwa Ji, Junseo Lee, Myeongjin Shin +2
In classical information theory, uncommon information refers to the amount of information that is not shared between two messages, and it admits an operational interpretation as th…
Resource-efficient algorithm for estimating the trace of quantum state powers
Myeongjin Shin, Junseo Lee, Seungwoo Lee +1
Estimating the trace of quantum state powers, , for identical quantum states is a fundamental task with numerous applications in quantum information processing,…
Disentangling quantum neural networks for unified estimation of quantum entropies and distance measures
Myeongjin Shin, Seungwoo Lee, Junseo Lee +4
The estimation of quantum entropies and distance measures, such as von Neumann entropy, Rényi entropy, Tsallis entropy, trace distance, and fidelity-induced distances such as the B…
Mutual information maximizing quantum generative adversarial networks
Mingyu Lee, Myeongjin Shin, Junseo Lee +1
One of the most promising applications in the era of Noisy Intermediate-Scale Quantum (NISQ) computing is quantum generative adversarial networks (QGANs), which offer significant q…
Estimating Quantum Mutual Information Through a Quantum Neural Network
Myeongjin Shin, Junseo Lee, Kabgyun Jeong
We propose a method of quantum machine learning called quantum mutual information neural estimation (QMINE) for estimating von Neumann entropy and quantum mutual information, which…