Kaiwu-PyTorch-Plugin: Bridging Deep Learning and Photonic Quantum Computing for Energy-Based Models and Active Sample Selection

Hongdong Zhu; Qi Gao; Yin Ma; Shaobo Chen; Haixu Liu; Fengao Wang; Tinglan Wang; Chang Wu

quant-phFeb 22, 2026

Kaiwu-PyTorch-Plugin: Bridging Deep Learning and Photonic Quantum Computing for Energy-Based Models and Active Sample Selection

Authors: Hongdong Zhu, Qi Gao, Yin Ma, Shaobo Chen, Haixu Liu, Fengao Wang, Tinglan Wang, Chang Wu,

Organizations: Beijing QBoson Quantum Technology Co., Ltd. · Chinese Academy of Sciences

Abstract

This paper introduces the Kaiwu-PyTorch-Plugin (KPP) to bridge Deep Learning and Photonic Quantum Computing across multiple dimensions. KPP integrates the Coherent Ising Machine into the PyTorch ecosystem, addressing classical inefficiencies in Energy-Based Models. The framework facilitates quantum integration in three key aspects: accelerating Boltzmann sampling, optimizing training data via Active Sampling, and constructing hybrid architectures like QBM-VAE and Q-Diffusion. Empirical results on single-cell and OpenWebText datasets demonstrate KPPs ability to achieve SOTA performance, validating a comprehensive quantum-classical paradigm.

Figures & tables

Figure 1: Compact view of the CIM structure.

	Immune		HLCA		Lung
Model	Bio.	Batch	Bio.	Batch	Bio.	Batch
VAE Kingma and Welling (2013)	0.55	0.36	0.54	0.34	0.56	0.39
scVI Ergen et al. (2025)	0.65	0.59	0.72	0.44	0.52	0.47
LDVAE Ergen et al. (2025)	0.55	0.58	0.65	0.43	0.53	0.47
scPoli De Donno et al. (2023)	0.65	0.59	0.71	0.43	0.55	0.47
QBM-VAE	0.69	0.61	0.76	0.48	0.67	0.49

Table 1: Performance comparison on selected datasets

Model	Ours	SEDD	MLDM
PPL	22.54	24.56	23.83

Table 2: OpenWebText results for the first three models

Hybrid Quantum-Classical ML

Hybrid quantum-classical machine learning combines quantum circuits or annealers with classical models for learning, prediction, generation, and optimization.

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Q-PhotoNAS: Hybrid Quantum Neural Architecture Search Framework on Photonic Devices

Farah Elnakhal, Alberto Marchisio, Nouhaila Innan, Gabriel Falcao+1

Science Division, New York University Abu Dhabi, UAE · eBrain Lab, Division of Engineering, New York University Abu Dhabi, PO Box 129188, Abu Dhabi, UAE · Center for Quantum and Topological Systems, NYUAD Research Institute, New York University Abu Dhabi, UAE+1

Hybrid Quantum-Classical ML Hardware-Aware NAS Photonic Computing Quantum Architecture Search

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Photonic quantum computing is a promising platform for scalable quantum machine learning, but designing effective hybrid architectures remains challenging under hardware and optimization constraints. Existing approaches rely on manually tuned architectures that fail to account for the collaboration between classical preprocessing, phase encoding, and photonic circuit structure, limiting both accuracy and hardware compatibility. In this paper, we propose a neural architecture search framework for hybrid photonic quantum-classical models that combines genetic algorithm-based search with learnable quantum phase encoding to systematically explore the joint design space of classical and quantum components. Our framework encodes 19 hyperparameters across six gene groups and evolves a population of hybrid architectures using group-based crossover, per-gene mutation, and elitism, evaluating each candidate on a short training budget before full retraining of the best found design. We evaluate our framework on two image classification benchmarks, Digits and MNIST, achieving final validation accuracies of 99.44% and 98.78%, respectively, with first-principles execution time estimates on the Quandela Ascella photonic QPU projecting single-image inference at 67 ms (Digits) and 149 ms (MNIST). Our quantum contribution analysis further shows that the photonic layer extracts non-redundant features orthogonal to the classical pathway, providing a measurable accuracy advantage over classical-only baselines. Our results demonstrate that automated architecture search is both practical and impactful for hybrid photonic systems, opening the way for systematic design space exploration of quantum AI on photonic devices.

Abstract

Hybrid Quantum-Classical ML

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