<!--HTML-->Generative Models, and Generative Adversarial Networks (GAN) in particular, are being studied as possible alternatives to Monte Carlo. Meanwhile, it has also been proposed that, in certain circumstances, simulation using GANs can itself be sped-up by using quantum GANs (qGANs). Our work presents an advanced prototype of qGAN, that we call the dual-Parameterized Quantum Circuit (PQC) GAN, with a classical discriminator and two quantum generators which take the form of PQCs. The first PQC learns the probability distribution over the images of $N$ pixels, while the second generates normalized pixel intensities of an individual image for each PQC input. The performance of the dual-PQC architecture has been evaluated through the application in HEP to imitate calorimeter outputs, translated into pixelated images. The results demonstrate that the model can reproduce a fixed number of images with a reduced size as well as their probability distribution and we anticipate it should allow us to scale up to real calorimeter outputs.
<!--HTML-->Generative Models, and Generative Adversarial Networks (GAN) in particular, are being studied as possible alternatives to Monte Carlo. Meanwhile, it has also been proposed that, in certain circumstances, simulation using GANs can itself be sped-up by using quantum GANs (qGANs). Our work presents an advanced prototype of qGAN, that we call the dual-Parameterized Quantum Circuit (PQC) GAN, with a classical discriminator and two quantum generators which take the form of PQCs. The first PQC learns the probability distribution over the images of $N$ pixels, while the second generates normalized pixel intensities of an individual image for each PQC input. The performance of the dual-PQC architecture has been evaluated through the application in HEP to imitate calorimeter outputs, translated into pixelated images. The results demonstrate that the model can reproduce a fixed number of images with a reduced size as well as their probability distribution and we anticipate it should allow us to scale up to real calorimeter outputs.
Generative models, and Generative Adversarial Networks (GAN) in particular, are being studied as possible alternatives to Monte Carlo simulations. It has been proposed that, in certain circumstances, simulation using GANs can be sped-up by using quantum GANs (qGANs). We present a new design of qGAN, the dual-Parameterized Quantum Circuit (PQC) GAN, which consists of a classical discriminator and two quantum generators which take the form of PQCs. The first PQC learns a probability distribution over N-pixel images, while the second generates normalized pixel intensities of an individual image for each PQC input. With a view to HEP applications, we evaluated the dual-PQC architecture on the task of imitating calorimeter outputs, translated into pixelated images. The results demonstrate that the model can reproduce a fixed number of images with a reduced size as well as their probability distribution and we anticipate it should allow us to scale up to real calorimeter outputs.
Generative models, and Generative Adversarial Networks (GAN) in particular, are being studied as possible alternatives to Monte Carlo simulations. It has been proposed that, in certain circumstances, simulation using GANs can be sped-up by using quantum GANs (qGANs). We present a new design of qGAN, the dual-Parameterized Quantum Circuit (PQC) GAN, which consists of a classical discriminator and two quantum generators which take the form of PQCs. The first PQC learns a probability distribution over N-pixel images, while the second generates normalized pixel intensities of an individual image for each PQC input. With a view to HEP applications, we evaluated the dual-PQC architecture on the task of imitating calorimeter outputs, translated into pixelated images. The results demonstrate that the model can reproduce a fixed number of images with a reduced size as well as their probability distribution and we anticipate it should allow us to scale up to real calorimeter outputs.
Abstract In an earlier work [1], we introduced dual-Parameterized Quantum Circuit (PQC) Generative Adversarial Networks (GAN), an advanced prototype of quantum GAN. We applied the model on a realistic High-Energy Physics (HEP) use case: the exact theoretical simulation of a calorimeter response with a reduced problem size. This paper explores the dual-PQC GAN for a more practical usage by testing its performance in the presence of different types of quantum noise, which are the major obstacles to overcome for successful deployment using near-term quantum devices. The results propose the possibility of running the model on current real hardware, but improvements are still required in some areas.
Abstract The prospect of quantum computing with a potential exponential speed-up compared to classical computing identifies it as a promising method in the search for alternative future High Energy Physics (HEP) simulation approaches. HEP simulations, such as employed at the Large Hadron Collider at CERN, are extraordinarily complex and require an immense amount of computing resources in hardware and time. For some HEP simulations, classical machine learning models have already been successfully developed and tested, resulting in several orders of magnitude speed-up. In this research, we proceed to the next step and explore whether quantum computing can provide sufficient accuracy, and further improvements, suggesting it as an exciting direction of future investigations. With a small prototype model, we demonstrate a full quantum Generative Adversarial Network (GAN) model for generating downsized eight-pixel calorimeter shower images. The advantage over previous quantum models is that the model generates real individual images containing pixel energy values instead of simple probability distributions averaged over a test sample. To complete the picture, the results of the full quantum GAN model are compared to hybrid quantum-classical models using a classical discriminator neural network.
Quantum Machine Learning (qML) is one of the most promising and very intuitive applications onnear-term quantum devices which possess the potential to combat computing resource challengesfaster than traditional computers. Classical Machine Learning (ML) is taking up a significant role inparticle physics to speed up detector simulations. Generative Adversarial Networks (GANs) haveproven to achieve a similar level of accuracy compared to Monte Carlo-based simulations whiledecreasing the computation time by orders of magnitude. In this research we are moving on and applyquantum computing to GAN-based detector simulations.Given the limitations of current quantum hardware in terms of number of qubits, connectivity, andnoise, we perform initial tests with a simplified GAN model running on quantum simulators. Themodel is a classical-quantum hybrid ansatz. It consists of a quantum generator, defined as aparameterised circuit based on single and two qubit gates, combined with a classical discriminator.Our initial qGAN prototype focuses on a one-dimensional toy-distribution, representing the energydeposited in a detector by a single particle. It employs three qubits and achieves high physics accuracythanks to hyper-parameter optimisation. Furthermore, we study the influence of real hardware noisefor the qML GAN training. A second qGAN is developed to simulate 2D images with a 64-pixelresolution, representing the energy deposition patterns in the detector. Different quantum ansatzes arestudied. We obtained the best results using a tree-tensor-network architecture with six qubits.Additionally, we discuss challenges and potential benefits of quantum computing as well as our plansfor future developments.
Quantum Generative Adversarial Networks (QGANs), as a rising paradigm in Quantum Machine Learning, have shown promising potential in image generation and processing. However, their output quality remains suboptimal, and existing research is largely limited to small-scale, proof-of-concept studies. In this work, we propose a hybrid quantum-classical GAN architecture, where the generator integrates parameterized quantum circuits (PQCs) and classical neural networks. This integration significantly enhances the visual quality of generated images. Our model leverages the complementary strengths of quantum and classical components and outperforms existing methods (Tsang et al. in IEEE Trans. Quantum Eng. 4:1–19, 2023 ; Gulrajani et al. in Proceedings of the 31st international conference on neural information processing systems. NIPS’17, Red Hook, pp. 5769–5779, 2017 ), particularly in terms of image fidelity. Experiments conducted on the MNIST family of datasets show that our hybrid approach achieves a 20.26% average reduction in Fréchet Inception Distance. Furthermore, it improves the Structural Similarity Index Measure, Cosine Similarity, and Peak Signal-to-Noise Ratio by 26.04%, 2.22%, and 7.62%, respectively. These results highlight the effectiveness of combining quantum computing with machine learning, and underscore the potential of hybrid quantum-classical models in advancing generative tasks.