7 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…
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…