9 papers
Gaussian Process Bandit Optimization with Machine Learning Predictions and Application to Hypothesis Generation
Xin Jennifer Chen, Yunjin Tong
Many real-world optimization problems involve an expensive ground-truth oracle (e.g., human evaluation, physical experiments) and a cheap, low-fidelity prediction oracle (e.g., mac…
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…
Learning Hamiltonians in the Heisenberg limit with static single-qubit fields
Shrigyan Brahmachari, Shuchen Zhu, Iman Marvian +1
Learning the Hamiltonian governing a quantum system is a central task in quantum metrology, sensing, and device characterization. Existing Heisenberg-limited Hamiltonian learning p…
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…
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…