5 citations · 8 across the 11 of their papers we have counts for
6 papers · 1 filter
Improved Hamiltonian learning and sparsity testing through Bell sampling
Savar D. Sinha, Yu Tong
We consider the problem of learning an -sparse Hamiltonian and the related problem of Hamiltonian sparsity testing. Through a detailed analysis of Bell sampling, we reduce the t…
Qubit-Efficient Quantum Algorithm for Linear Differential Equations
Di Fang, David Lloyd George, Yu Tong
As quantum hardware rapidly advances toward the early fault-tolerant era, a key challenge is to develop quantum algorithms that are not only theoretically sound but also hardware-f…
Heisenberg-limited Hamiltonian learning continuous variable systems via engineered dissipation
Tim Möbus, Andreas Bluhm, Tuvia Gefen +3
Discrete and continuous variables oftentimes require different treatments in many learning tasks. Identifying the Hamiltonian governing the evolution of a quantum system is a funda…
State-space gradient descent and metastability in quantum systems
Shuchen Zhu, Yu Tong
We propose a quantum algorithm, inspired by ADAPT-VQE, to variationally prepare the ground state of a quantum Hamiltonian, with the desirable property that if it fails to find the…
High-Temperature Fermionic Gibbs States are Mixtures of Gaussian States
Akshar Ramkumar, Yiyi Cai, Yu Tong +1
Efficient simulation of a quantum system generally relies on structural properties of the quantum state. Motivated by the recent results by Bakshi et al. on the sudden death of ent…
Ansatz-free Hamiltonian learning with Heisenberg-limited scaling
Hong-Ye Hu, Muzhou Ma, Weiyuan Gong +4
Learning the unknown interactions that govern a quantum system is crucial for quantum information processing, device benchmarking, and quantum sensing. The problem, known as Hamilt…