Ultra-low latency quantum-inspired machine learning predictors implemented on FPGA

• URL: http://arxiv.org/abs/2409.16075v2
• Date: Wed, 25 Sep 2024 08:59:26 GMT
• Title: Ultra-low latency quantum-inspired machine learning predictors implemented on FPGA
• Authors: Lorenzo Borella, Alberto Coppi, Jacopo Pazzini, Andrea Stanco, Marco Trenti, Andrea Triossi, Marco Zanetti,
• Abstract summary: Tree Networks (TNs) are a computational paradigm used for representing quantum many-body systems. Recent works have shown how TNs can also be applied to perform Machine Learning (ML) tasks. We study the use of TTNs in high-frequency real-time applications by exploiting the low- hardware of the Field-Programmable Gate Array (FPGA) technology.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tensor Networks (TNs) are a computational paradigm used for representing quantum many-body systems. Recent works have shown how TNs can also be applied to perform Machine Learning (ML) tasks, yielding comparable results to standard supervised learning techniques. In this work, we study the use of Tree Tensor Networks (TTNs) in high-frequency real-time applications by exploiting the low-latency hardware of the Field-Programmable Gate Array (FPGA) technology. We present different implementations of TTN classifiers, capable of performing inference on classical ML datasets as well as on complex physics data. A preparatory analysis of bond dimensions and weight quantization is realized in the training phase, together with entanglement entropy and correlation measurements, that help setting the choice of the TTN architecture. The generated TTNs are then deployed on a hardware accelerator; using an FPGA integrated into a server, the inference of the TTN is completely offloaded. Eventually, a classifier for High Energy Physics (HEP) applications is implemented and executed fully pipelined with sub-microsecond latency.

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