Andrei Petrenko

Andrei Petrenko's picture
Research Areas: 
Experimental Condensed Matter Physics
Education: 
Ph.D. 2017, Yale University
Advisor: 
Robert Schoelkopf
Dissertation Title: 
Enhancing the Lifetime of Quantum Information with Cat States in Superconducting Cavities
Dissertation Abstract: 

The field of quantum computation faces a central challenge that has thus far impeded the full-scale realization of quantum computing machines: decoherence.  Remarkably, however, protocols in Quantum Error Correction (QEC) exist to correct qubit errors and thus extend the lifetime of quantum information.  Reaching the “break-even” point of QEC, at which a qubit’s lifetime exceeds the lifetime of the system’s constituents, has thus far remained an outstanding goal.  In this work, we implement a QEC code within a superconducting cavity Quantum Electrodynamics (cQED) architecture that exploits the advantages of encoding quantum information in superpositions of coherent states, or cat states, in highly coherent superconducting cavities.  This hardware-efficient approach, termed the cat code, simplifies the encoding scheme and requires the extraction of just one error syndrome via single-shot photon number parity measurements.  By implementing the cat code within a full QEC system, we demonstrate for the first time quantum computing that reaches the break-even point.  Beyond applications to error correction, logical qubit encodings based on the cat code paradigm can be used to probe more fundamental questions of quantum entanglement between physical qubits and coherent states.  We demonstrate the violation of a Bell inequality in such a setup, which not only exhibits our ability to efficiently extract information from continuous variables encodings, but moreover illustrates a striking example of a system straddling the quantum-to-classical interface.  These results highlight the power of novel, hardware-efficient qubit encodings over traditional QEC schemes. Furthermore, they advance the field of experimental error correction from confirming the basic concepts to exploring the metrics that drive system performance and the challenges in implementing a fault-tolerant system.