Quantum Bootstrapping via Compressed Quantum Hamiltonian Learning
arXiv:1409.1524 · doi:10.1088/1367-2630/17/2/022005
Abstract
Recent work has shown that quantum simulation is a valuable tool for learning empirical models for quantum systems. We build upon these results by showing that a small quantum simulators can be used to characterize and learn control models for larger devices for wide classes of physically realistic Hamiltonians. This leads to a new application for small quantum computers: characterizing and controlling larger quantum computers. Our protocol achieves this by using Bayesian inference in concert with Lieb-Robinson bounds and interactive quantum learning methods to achieve compressed simulations for characterization. Whereas Fisher information analysis shows that current methods which employ short-time evolution are suboptimal, interactive quantum learning allows us to overcome this limitation. We illustrate the efficiency of our bootstrapping protocol by showing numerically that an 8-qubit Ising model simulator can be used to calibrate and control a 50 qubit Ising simulator while using only about 750 kilobits of experimental data.
Minor changes to references
References in corpus (8)
- Quantum Data Fitting
- Lieb-Robinson Bounds and the Exponential Clustering Theorem
- Simulating chemistry using quantum computers
- Robust Online Hamiltonian Learning
- Suppressing qubit dephasing using real-time Hamiltonian estimation
- Two-Qubit Hamiltonian Tomography by Bayesian Analysis of Noisy Data
- Accelerated Randomized Benchmarking
- Verified Delegated Quantum Computing with One Pure Qubit