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IEEE/ACM Symposium on Edge Computing (SEC)

Quantum Angle Generator for Image Generation

Florian Rehm, Sofia Vallecorsa, Michele Grossi, K. Borras, Dirk Krücker, S. Schnake, Alexi Verney-Provatas

Published Dec 1, 2022• 3 citations
Publisher

Abstract

The Quantum Angle Generator (QAG) is a new generative model for quantum computers. It consists of a parameterized quantum circuit trained with an objective function. The QAG model utilizes angle encoding for the conversion between the generated quantum data and classical data. Therefore, it requires one qubit per feature or pixel, while the output resolution is adjusted by the number of shots performing the image generation. This approach allows the generation of highly precise images on recent quantum computers. In this paper, the model is optimised for a High Energy Physics (HEP) use case generating simplified one-dimensional images measured by a specific particle detector, a calorimeter. With a reasonable number of shots, the QAG model achieves an elevated level of accuracy. The advantages of the QAG model are lined out - such as simple and stable training, a reasonable amount of qubits, circuit calls, circuit size and computation time compared to other quantum generative models, e.g. quantum GANs (qGANs) and Quantum Circuit Born Machines.

DOI
10.1109/sec54971.2022.00064

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The Quantum Angle Generator (QAG) is a new generative model for quantum computers. It consists of a parameterized quantum circuit trained with an objective function. The QAG model utilizes angle encoding for the conversion between the generated quantum data and classical data. Therefore, it requires one qubit per feature or pixel, while the output resolution is adjusted by the number of shots performing the image generation. This approach allows the generation of highly precise images on recent quantum computers. In this paper, the model is optimised for a High Energy Physics (HEP) use case generating simplified one-dimensional images measured by a specific particle detector, a calorimeter. With a reasonable number of shots, the QAG model achieves an elevated level of accuracy. The advantages of the QAG model are lined out - such as simple and stable training, a reasonable amount of qubits, circuit calls, circuit size and computation time compared to other quantum generative models, e.g. quantum GANs (qGANs) and Quantum Circuit Born Machines.
The Quantum Angle Generator (QAG) is a cutting-edge quantum machine learning model designed to generate precise images on current Noise Intermediate Scale Quantum devices. It utilizes variational quantum circuits and incorporates the MERA-upsampling architecture, achieving exceptional accuracy. The study demonstrates the QAG model’s ability to learn hardware noise behavior, with stable results in the presence of simulated quantum hardware noise up to 1.5% during inference and 3% during training. However, deploying the noiseless trained model on real quantum hardware reduces accuracy. Training the model directly on hardware allows it to learn the underlying noise behavior, maintaining precision comparable to the noisy simulator. The QAG model’s noise robustness and accuracy make it suitable for analyzing simulated calorimeter shower images used in high-energy physics simulations at CERN’s Large Hadron Collider.
The Quantum Angle Generator (QAG) is a cutting-edge quantum machine learning model designed to generate precise images on current Noise Intermediate Scale Quantum devices. It utilizes variational quantum circuits and incorporates the MERA-upsampling architecture, achieving exceptional accuracy. The study demonstrates the QAG model's ability to learn hardware noise behavior, with stable results in the presence of simulated quantum hardware noise up to 1.5%1.5\%1.5% during inference and 3%3\%3% during training. However, deploying the noiseless trained model on real quantum hardware reduces accuracy. Training the model directly on hardware allows it to learn the underlying noise behavior, maintaining precision comparable to the noisy simulator. The QAG model's noise robustness and accuracy make it suitable for analyzing simulated calorimeter shower images used in high-energy physics simulations at CERN's Large Hadron Collider.
Abstract The quantum angle generator (QAG) is a new full quantum machine learning model designed to generate accurate images on current noise intermediate scale quantum devices. Variational quantum circuits form the core of the QAG model, and various circuit architectures are evaluated. In combination with the so-called MERA-upsampling architecture, the QAG model achieves excellent results, which are analyzed and evaluated in detail. To our knowledge, this is the first time that a quantum model has achieved such accurate results. To explore the robustness of the model to noise, an extensive quantum noise study is performed. In this paper, it is demonstrated that the model trained on a physical quantum device learns the noise characteristics of the hardware and generates outstanding results. It is verified that even a quantum hardware machine calibration change during training of up to 8% can be well tolerated. For demonstration, the model is employed in indispensable simulations in high energy physics required to measure particle energies and, ultimately, to discover unknown particles at the large Hadron Collider at CERN.
The Quantum Angle Generator (QAG) is a new full Quantum Machine Learning model designed to generate accurate images on current Noise Intermediate Scale (NISQ) Quantum devices. Variational quantum circuits form the core of the QAG model, and various circuit architectures are evaluated. In combination with the so-called MERA-upsampling architecture, the QAG model achieves excellent results, which are analyzed and evaluated in detail. To our knowledge, this is the first time that a quantum model has achieved such accurate results. To explore the robustness of the model to noise, an extensive quantum noise study is performed. In this paper, it is demonstrated that the model trained on a physical quantum device learns the noise characteristics of the hardware and generates outstanding results. It is verified that even a quantum hardware machine calibration change during training of up to 8% can be well tolerated. For demonstration, the model is employed in indispensable simulations in high energy physics required to measure particle energies and, ultimately, to discover unknown particles at the Large Hadron Collider at CERN.
Quantum-based generative models offer a promising approach for simulating complex phenomena in high-energy physics, such as calorimeter showers used for particle identification and energy reconstruction at the LHC. We propose the Quantum Angle Generator (QAG), a variational quantum model trained with a Maximum Mean Discrepancy loss to generate images from the probabilistic outputs of quantum circuits. We study model and training hyperparameters, assess the impact of quantum noise during training and inference, and evaluate the QAG in both noiseless simulations and simulated Noisy Intermediate-Scale Quantum hardware. Results show that the QAG can adapt through learning to hardware-induced noise, yielding stable, high-quality outputs even under significant noise and calibration variability.
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.