Speaker
Description
Tensor Networks (TNs) are a powerful computational framework originally developed for the efficient representation and simulation of quantum many-body systems. In recent years, they have gained increasing attention in machine learning (ML), demonstrating competitive performance in supervised learning tasks compared to conventional models.
In this work, we investigate the suitability of Tree Tensor Networks (TTNs) for high-frequency, real-time inference by exploiting the low-latency and high-throughput capabilities of Field-Programmable Gate Arrays (FPGAs). We present and evaluate multiple hardware implementations of TTN-based classifiers, targeting both standard ML benchmarks and complex datasets arising from physics applications.
During training, a systematic analysis is performed to determine optimal bond dimensions and weight quantization schemes. This analysis is informed by entanglement entropy and correlation function measurements, which provide insight into the representational capacity required by the model and guide the selection of the TTN architecture.
Following training, the TTN models are mapped onto a dedicated FPGA accelerator integrated within a server environment, with inference fully offloaded to hardware. This enables highly efficient, fully pipelined execution, achieving substantial reductions in inference latency. As a demonstrative application, we deploy a TTN-based classifier for a High Energy Physics (HEP) use case, achieving sub-microsecond inference latency while maintaining competitive classification performance.
These results demonstrate the feasibility of deploying quantum-inspired TN models within Level-1 trigger systems of HEP experiments, satisfying the stringent latency and throughput requirements while preserving robust classification performance. This work establishes TNs as a promising paradigm for real-time decision-making on specialized low-latency hardware platforms.
| Sessions | Quantum Machine Learning: |
|---|---|
| Invited | No |