CQuIC Seminars
Scalable Techniques for Quantum Network Engineering
Presented by Nikolas Anton Tezak, Stanford University
In the quest for creating "quantum enhanced" systems for information processing most currently pursued design strategies are difficult to scale significantly beyond a few dozen qubits. The dominant design paradigm relies on a vast overhead of external classical control. In this talk we argue for a more integrated framework which treats quantum and hybrid quantum-classical systems on equal footing.
We have recently defined a Quantum Hardware Description Language (QHDL) capable of describing networks of interconnected open quantum systems. QHDL is compiled to symbolic and numerical system models by a custom software tool suite named QNET.
We further present a model reduction technique for describing networks of nonlinear oscillators in the semi-classical regime. This allows us to design ultra-low power systems capable of classical computation and investigate the effect of quantum noise on their error rates.
We discuss example applications of the above methods including (1) quantum feedback networks for autonomous quantum error corrections, (2) coupled optical parametric oscillators for generating tunable squeezed light, and (3) neuromorphic optical circuits for all-optical machine learning.
Finally, we present a model transformation capable of dividing the description of quantum states into a low-dimensional quasi-classical part coupled to a lower complexity quantum state. This approach is in principle exact and naturally tailored to simulating coupled quantum systems with varying degrees of dissipation.
3:00 pm, Wednesday, June 22, 2016
PAIS-3300, PAIS
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