Search Talks

Using superconducting parametric cavities, we have demonstrated much of the toolbox of linear quantum optics, but also extended it by taking advantage of the strong nonlinearities of superconducting circuits. In a set of experiments, we have used these parametric cavities as a platform for analog quantum simulation of lattice field theories. Preliminary results already show the promise of the platform for this application. For instance, a single device can simulate a number of different models, including topological and chiral models, in a flexible and programmable way.

Trapped ions are among the most advanced technology platforms for quantum information processing, in particular quantum simulation. However, ions are most readily trapped as a linear chain in radio-frequency traps, limiting their use to simulate higher dimensional quantum systems. In this talk, I'll describe an analog and an analog-digital hybrid [1] quantum simulation protocols to simulate programmable 2D and 3D spin models in a linear ion chain, by manipulating phonon-mediated long-ranged interactions between ion spins. The ability to dynamically engineer lattice geometries enables the investigation of a rich variety of physical phenomena such as spin frustration, topological states, and quantum quenches. [1] Rajabi et al., npj Quantum Information 5:32 (2019)

Coherent errors in a quantum system can, in principle, build up much more rapidly than incoherent errors, accumulating as the square of the number of qubits in the system rather than linearly. I will characterize the types of channels that can exhibit such behavior and present a simple protocol that can detect and characterize coherent errors whenever they are present, no matter what their nature. This allows us to identify coherent errors in gates and measurements to within a constant fraction of the maximum possible sensitivity to such errors.

Tensor networks are powerful computational tools, widely used in condensed matter physics, and increasingly in high-energy physics, with promising applications to machine learning problems. Developed in collaboration with Google and X, we present TensorNetwork: a new software package that makes it easier to code tensor network algorithms and, by using a framework like TensorFlow as a backend, to accelerate computations using specialized hardware (GPUs, TPUs) and integrate tensor networks into machine-learning projects.