Summary
An important challenge in building a quantum computer is quantifying the level of control obtained in the preparation of a quantum state. The state of a quantum device is characterized from experimental measurements, using a procedure known as tomography. Exact tomography requires a vast amount of computer resources, making it prohibitive for quantum devices larger than a few qubits. In this project we develop a practical, approximate tomography method using modern machine learning techniques. Our work is based on training artificial neural networks using measurement data obtained from a system of qubits. After training, the neural network is sampled to determine properties of the underlying quantum state. As part of a collaborative effort, we will demonstrate our machine learning algorithms on both synthetic and experimental measurement data. Our ultimate goal is to deliver practical machine learning technology to design and characterize near-term quantum devices.
Related Content
Reliably operating noisy quantum computers
Summary The overall goal of the project is to develop practical methods to be able to reliably run useful applications on near-term quantum computers. This requires identifying and overcoming the ubiquitous errors that currently limit quantum computing capabilities. Traditional methods of quantifying errors in quantum computers fail to predict how errors affect the output of […]
January 22, 2020
Engineering and Characterizing Programmable Interaction Graphs in a Trapped Ion Quantum Simulator
Summary Quantum simulators have the potential to bring unprecedented capabilities in areas such as the discovery of new materials and drugs. Engineering precise and programmable interaction graphs between qubits or spins forms the backbone of simulator applications. The trapped ion system is unique in that the interaction graph between qubits can be programmed, in […]
July 24, 2018
Developing Tools for Quantum Characterization and Validation
Summary Coherence is essential for quantum computation; yet it introduces a unique sensitivity to any imperfections in hardware design, control systems, and the operating environment. Overcoming these sensitivities requires a hierarchy of strategies, ranging from optimization of the hardware architecture to software solutions including quantum error correction. Randomized Benchmarking Protocols are an important family of […]
October 3, 2017