5 citations · 8 across the 11 of their papers we have counts for
11 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…
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…
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…
Exponential Quantum Advantage for Pathfinding in Regular Sunflower Graphs
Jianqiang Li, Yu Tong
Finding problems that allow for superpolynomial quantum speedup is one of the most important tasks in quantum computation. A key challenge is identifying problem structures that ca…
Bayesian Inference for Stochastic Predictions of Non-Gaussian Systems with Applications in Climate Change
Yunjin Tong
Climate change poses significant challenges for accurate climate modeling due to the complexity and variability of non-Gaussian climate systems. To address the complexities of non-…
Data-Driven Computing Methods for Nonlinear Physics Systems with Geometric Constraints
Yunjin Tong
In a landscape where scientific discovery is increasingly driven by data, the integration of machine learning (ML) with traditional scientific methodologies has emerged as a transf…