Quantum state classification using the inherent nonlinearity of a SNAIL quantum-limited amplifier
ORAL
Abstract
We discuss the implementation of a quantum non-linear processing scheme (arXiv:2409.03748) through a two-SNAIL (Superconducting Nonlinear Asymmetric Inductive eLements) oscillator system. In this scheme, SNAIL1 acts as a state generator that generates on demand one of two squeezed vacuum states with orthogonal squeezing directions, while SNAIL2 is used to classify the state generated. Numerical evidence is presented that a non-zero non-linearity in SNAIL2 is required to carry out the task with high fidelity. In this regime, SNAIL2 acts as a quantum non-linear processor that can infer higher-order features (here, second order) of an incident quantum signal, concentrating them into linearly-measurable observables, a transduction not possible using linear amplifiers. We discuss the optimization of the system parameters within a realistic model of the measurement chain for sample-efficient classification.
*This work is supported by AFOSR under Grant No. FA9550-20-1-0177 and the Army Research Office under Grant No. W911NF18-1-0144. Simulations in this paper were performed using the Princeton Research Computing resources at Princeton University, which is a consortium of groups led by the Princeton Institute for Computational Science and Engineering (PICSciE) and Office of Information Technology's Research Computing.
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Publication: paper in progress
Presenters
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Elif Cuce
- Princeton University