6 papers
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,…