Generative Invertible Quantum Neural Networks

Abstract

Invertible Neural Networks (INN) have become established tools for the simulation and generation of highly complex data. We propose a quantum-gate algorithm for a Quantum Invertible Neural Network (QINN) and apply it to the LHC data of jet-associated production of a Z-boson that decays into leptons, a standard candle process for particle collider precision measurements. We compare the QINN's performance for different loss functions and training scenarios. For this task, we find that a hybrid QINN matches the performance of a significantly larger purely classical INN in learning and generating complex data.

Boosted Ensembles of Qubit and Continuous Variable Quantum Support Vector Machines for B Meson Flavor Tagging

“…[30] Following the successful application of classical ML algorithms, the potential for QML to yield new benefits in high energy physics has already begun to be explored, with promising, if preliminary, results reported to date. [31][32][33][34][35][36][37][38][39][40][41] In this work, we introduce ensembles of boosted quantum support vector machines (QSVMs) as a technique for performing B meson flavor tagging near the level of state-of-the-art classical algorithms. QSVMs are powerful classifiers which both have the potential to exponentially outperform their classical counterparts [42] and are capable of being implemented on near-term NISQ devices, [34] making them attractive candidates to study in the search for practically useful applications of QML.…”