Variational Quantum Eigensolver Enables Novel Regularization in Generative Adversarial Networks
Summarize this article with:
Generative Adversarial Networks (GANs) continually seek improved methods for generating realistic and well-classified data, and recent research explores the potential of incorporating concepts from quantum computing to achieve this. David Strnadel from Tomas Bata University in Zlin, along with colleagues, investigates whether energy terms derived from parameterized quantum circuits can function as effective regularization signals within GANs. This work demonstrates a novel approach, termed QACGAN, which integrates a Variational Quantum Eigensolver-inspired energy computation into the GAN training process via differentiable pathways, achieving high classification accuracy on the MNIST dataset within a reduced number of training epochs. While acknowledging limitations in scalability and the absence of direct quantum advantage in this initial proof of concept, the team establishes a methodological contribution by successfully bridging quantum-inspired energy calculations with GAN training loops, opening avenues for future research into the benefits of such auxiliary signals. Experiments, currently conducted on a noiseless statevector simulator with a limited number of qubits, utilise a deliberately simple Hamiltonian parameterization. The observed computational overhead, approximately 200times slower than a classical GAN, primarily reflects simulator limitations rather than inherent quantum costs. On the MNIST dataset, the energy-regularized model, termed QACGAN, achieves high classification accuracy, ranging from 99 to 100 percent within 5 epochs, compared to 87. The core idea is to use the output of a VQE calculation, specifically an Ising Hamiltonian, as an auxiliary signal to guide the GAN’s learning process. Key findings include demonstrating the technical possibility of incorporating VQE computations into a GAN training loop and achieving initial improvements in classification accuracy. The authors emphasize that this is an exploratory study and do not claim quantum advantage, acknowledging limitations such as the simplicity of the Hamiltonian used and the reliance on a noise-free simulator. The methodology involves calculating the energy of a simple Ising Hamiltonian via VQE and using this energy as a scalar signal to influence the GAN’s discriminator. The authors acknowledge limitations, including the use of a small-scale simulator and the absence of comparative studies against classical regularization techniques, and therefore refrain from claiming any quantum advantage. Future research should focus on systematic ablation studies to determine whether VQE-based regularization offers benefits beyond equivalent classical methods, and on exploring scalability beyond the current toy setting. Overall, this paper is a proof-of-concept study that demonstrates the technical feasibility of integrating VQE computations into GAN training. Researchers augmented the Auxiliary Classifier GAN (ACGAN) generator objective with an energy term inspired by the Variational Quantum Eigensolver (VQE), computed using class-specific Ising Hamiltonians. Experiments on the MNIST dataset demonstrate that this energy-regularized model, termed QACGAN, achieves high classification accuracy, reaching 99 to 100 percent within just 5 epochs, significantly outperforming the baseline ACGAN which achieved 87. 8 percent accuracy at the same stage. QACGAN consistently matched or surpassed the ACGAN baseline in Inception Score, achieving peak values above 2. 23, and maintained high classifier accuracy, reaching 99. 0% and 100. 0% at epoch 5. Importantly, these results suggest that the auxiliary energy term influences class conditioning during GAN training, guiding it to produce more accurately classified samples.
The team measured total training time, finding that one QACGAN epoch requires approximately 1. 45 hours on a noiseless statevector simulator, compared to just 22 seconds per epoch for the ACGAN baseline. This computational overhead is currently attributed to simulator artifacts and Python-level overhead, rather than inherent quantum costs.
The team achieved high classification accuracy, reaching 99 to 100 percent within five epochs, when employing this energy-regularized model on the MNIST dataset, a notable improvement compared to the 87. 8 percent achieved by a standard GAN. This suggests that incorporating these VQE-inspired energy terms influences the conditioning of the generative model, guiding it to produce more accurately classified samples. Importantly, this work establishes a methodological contribution, proving that VQE-style computations can be incorporated into GAN training via differentiable pathways. The authors acknowledge limitations, including the use of a small-scale simulator and the absence of comparative studies against classical regularization techniques, and therefore refrain from claiming any quantum advantage. Future research should focus on systematic ablation studies to determine whether VQE-based regularization offers benefits beyond equivalent classical methods, and on exploring scalability beyond the current toy setting. 👉 More information 🗞 Differentiable Energy-Based Regularization in GANs: A Simulator-Based Exploration of VQE-Inspired Auxiliary Losses 🧠 ArXiv: https://arxiv.org/abs/2512.12581 Tags: