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Journal of Artificial Intelligence General science (JAIGS)

The Development of an Automated Approach for Designing Quantum Algorithms Using Circuits Generated By Generative Adversarial Networks (Gans)

Ankit Sharma

Published Sep 23, 2024• 1 citation
Publisher

Abstract

The advent of quantum computing has inaugurated a novel epoch of computational prowess, offering the potential to tackle intricate problems with unparalleled speed (Zoufal et al., 2019). Quantum circuits, which are essential components of quantum computation, serve as representations of sequences of quantum gates designed for specific quantum processes (Zoufal et al., 2019). Nevertheless, the task of creating efficient quantum circuits continues to be a formidable and labor-intensive undertaking. This research proposal presents a unique methodology that utilizes Generative Adversarial Networks (GANs) to automate the process of generating quantum circuits that are specifically designed for particular quantum gates and operations (Zoufal et al., 2019). The main goal of this study is to create a model based on Generative Adversarial Networks (GANs) that can generate quantum circuits by leveraging the collaborative efforts of the generator and discriminator networks (Zoufal et al., 2019). The GAN model will be trained using a carefully selected dataset that includes established quantum circuits and their corresponding required quantum operations (Zoufal et al., 2019). This dataset will form the basis for the training process. Following this, the quantum circuits that are produced will be thoroughly assessed in terms of fidelity, efficiency, and resource allocation (Zoufal et al., 2019). Additionally, the objective of this work is to refine and optimize the circuits that are formed by employing reinforcement learning and gradient-based techniques (Zoufal et al., 2019). In addition to investigating circuit production, this research will delve into the practical implications and consequences of quantum circuits formed by Generative Adversarial Networks (GANs) on the development of quantum algorithms (Zoufal et al., 2019). The study's value is in its capacity to accelerate the creation of quantum algorithms through the automation of circuit design (Zoufal et al., 2019). This research makes a valuable contribution to the field of quantum computing by improving the efficiency and resource utilization of quantum circuits (Zoufal et al., 2019). These developments are crucial for the development of practical quantum computing applications and will play a significant role in the evolution of quantum algorithms and processing capabilities in the future (Zoufal et al., 2019).

DOI
10.60087/jaigs.v4i1.226

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Quantum circuit optimization is a critical challenge in the development and deployment of quantum computers. The complexity of optimizing large quantum circuits grows exponentially with circuit size, making traditional methods computationally prohibitive. This work proposes a novel approach utilizing Generative Adversarial Networks (GANs) to address this problem. The core idea is to train a GAN to learn a compressed, lower-dimensional representation of quantum circuits. This representation can then be used to efficiently guide the optimization process using gradient-based methods. We demonstrate, theoretically, that the GAN's ability to learn complex mappings from high-dimensional space to lower-dimensional space can significantly accelerate the optimization process compared to conventional techniques. The key formulation involves a generator network, G(s), which maps a quantum circuit (s) to a lower-dimensional latent space, and a discriminator network, D(z), which distinguishes between real and generated latent representations. The optimization process hinges on the adversarial training between G and D. We present a framework for this process, outlining the mathematical foundations and potential advantages. This work lays the groundwork for future research exploring GANs as a powerful tool for quantum circuit optimization.
The synthesis of quantum circuits, crucial for realizing quantum algorithms, faces significant challenges in satisfying both functional requirements and physical limitations inherent in quantum hardware. Traditional circuit synthesis methods often struggle to efficiently explore the vast design space while adhering to constraints such as gate fidelity, qubit connectivity, and circuit depth. This work explores the application of Generative Adversarial Networks (GANs) to automate quantum circuit synthesis, directly incorporating physical constraints into the learning process. We propose a framework where a generator network creates quantum circuits, and a discriminator network evaluates these circuits against a defined set of physical constraints. Through adversarial training, the generator learns to produce circuits that are not only functionally correct but also physically realizable, offering a novel approach to quantum circuit design. The core claim of this research is that GANs can be effectively trained to generate quantum circuits satisfying both functional requirements and physical constraints. This approach represents a significant step towards practical quantum circuit synthesis, addressing a critical bottleneck in the development of quantum computing.
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The development of Generative Adversarial Networks (GANs) revolutionized the creation of synthetic data and signified the start of a revolutionary age in artificial intelligence. GANs have been used over the years in a variety of domains, including video production, natural language processing, and image synthesis. However, issues with classical GANs include computational efficiency, limited scalability for high-dimensional data and significant processing costs. Quantum GANs (QGANs) have been developed to overcome these drawbacks by integrating adversarial training with the concepts of quantum computing. In order to improve efficiency and stability, QGANs make use of quantum superposition and entanglement. This makes them an effective choice for producing high-quality synthetic data. By using noisy intermediate scale quantum (NISQ) devices for effective, noise-tolerant computations, quantum GANs (QGANs) allow for high-dimensional data synthesis using fewer resources than traditional techniques. Among their many uses, QGANs are particularly good at picture synthesis, making use of quantum circuits to produce realistic images quickly. This research studies the architecture of QGANs, particularly quantum circuit designs, implementation strategies for QGANs, and a comparison of several performance metrics.
This paper investigates the application of Quantum Generative Adversarial Networks (QGANs) for the generation of synthetic data. Traditional Generative Adversarial Networks (GANs) often face challenges in producing high-quality synthetic data, especially when dealing with complex data distributions. We propose a novel framework utilizing quantum circuits to construct both the generator and discriminator within a QGAN architecture. The core innovation lies in harnessing quantum superposition and interference during the adversarial training process to potentially improve the realism and diversity of the generated synthetic data. Our approach introduces a theoretical foundation and a conceptual design for QGANs, paving the way for future research and development in this emerging field. The primary goal is to demonstrate the potential benefits of incorporating quantum mechanical principles into the GAN framework, leading to enhanced synthetic data generation capabilities. The performance of a QGAN is expected to be superior to a classical GAN due to the unique properties of quantum systems.