RQS-QuICS Joint Seminar

I provide a brief introduction to the tenets of quantum error correction using the four-qubit code, making contact with concatenated, CSS, stabilizer, and rotated surface codes. I then go over bosonic quantum memories, organizing them into bosonic stabilizer codes and bosonic Fock-state codes. I conclude by overviewing six use cases of bosonic encodings, three of which circumvent no-go theorems due to the infinite-dimensionality of bosonic Hilbert space.

Times:

Mon, Aug 1, 11 am to 12:30 pm est: Lecture 1 - Introduction to quantum error correction

The demands of fault tolerance mean that a wide variety of simple and exotic noise types must be tamed for quantum devices to progress. Crucially, this means keeping up with complex correlated — or non-Markovian — effects, both with respect to the background process and to control operations. Recently, we have developed a generalised version of quantum process tomography to characterise arbitrary non-Markovian processes in practice.

Randomized benchmarking protocols have become the prominent tool for assessing the quality of gates on digital quantum computing platforms.  In `classical’ variants of randomized benchmarking multi-qubit gates are drawn uniformly from a finite group.  The functioning of such schemes be rigorous guaranteed under realistic assumptions.  In contrast, experimentally attractive and practically more scalable randomized benchmarking schemes often directly perform random circuits or use other non-uniform probability measures.

An interesting problem in the field of quantum error correction involves finding a physical system that hosts a “self-correcting quantum memory,” defined as an encoded qubit coupled to an environment that naturally wants to correct errors. To date, a quantum memory stable against finite-temperature effects is only known in four spatial dimensions or higher. Here, we take a different approach to realize a stable quantum memory by relying on a driven-dissipative environment.

Generating randomness efficiently is a key capability in both classical and quantum information processing applications. For example, Haar-random quantum states serve as primitives for applications including quantum cryptography, quantum process tomography, and randomized benchmarking. How quickly can these random states be generated? And how much randomness is really necessary for any given application? In this talk, I will address these questions in Brownian quantum circuit models, which admit a large-$N$ limit that can be solved exactly.