Quantum generative models (QGMs), including Variational Quantum Circuits (VQCs) and Quantum GANs, hold significant potential in generating complex data distributions beyond the capabilities of classical generative approaches. However, robust monitoring and evaluation of QGMs remain underdeveloped due to hardware constraints, stochastic quantum behavior, and reproducibility limitations. This paper proposes a scalable and modular framework using Apache Spark and MLflow to monitor, evaluate, and track the performance of QGMs. The framework enables ingestion of quantum-generated data, distributed computation of performance metrics such as fidelity, entanglement entropy, distributional divergence, and experiment tracking via MLflow. I validate our methodology using Qiskit-based simulated QGMs and demonstrate the effectiveness of classical big data tools in bridging the evaluation gap in quantum ML research.
Quantum generative models (QGMs), including Variational Quantum Circuits (VQCs) and Quantum GANs, hold significant potential in generating complex data distributions beyond the capabilities of classical generative approaches. However, robust monitoring and evaluation of QGMs remain underdeveloped due to hardware constraints, stochastic quantum behavior, and reproducibility limitations. This paper proposes a scalable and modular framework using Apache Spark and MLflow to monitor, evaluate, and track the performance of QGMs. The framework enables ingestion of quantum-generated data, distributed computation of performance metrics such as fidelity, entanglement entropy, distributional divergence, and experiment tracking via MLflow. I validate our methodology using Qiskit-based simulated QGMs and demonstrate the effectiveness of classical big data tools in bridging the evaluation gap in quantum ML research.
As the quantum computing community gravitates towards understanding the practical benefits of quantum computers, having a clear definition and evaluation scheme for assessing practical quantum advantage in the context of specific applications is paramount. Generative modeling, for example, is a widely accepted natural use case for quantum computers, and yet has lacked a concrete approach for quantifying success of quantum models over classical ones. In this work, we construct a simple and unambiguous approach to probe practical quantum advantage for generative modeling by measuring the algorithm's generalization performance. Using the sample-based approach proposed here, any generative model, from state-of-the-art classical generative models such as generative adversarial networks (GANs) to quantum models such as quantum circuit born machines, can be evaluated under the same conditions on a concrete well-defined framework. In contrast to other sample-based metrics for probing practical generalization, we leverage constrained optimization problems (e.g., cardinality-constrained problems) and use these discrete datasets to define specific metrics capable of unambiguously measuring the quality of the samples and the model's generalization capabilities for generating data beyond the training set, but still within the valid solution space. Additionally, our metrics can diagnose trainability issues such as mode collapse and overfitting, as we illustrate when comparing GANs to quantum-inspired models built out of tensor networks. Our simulation results show that our quantum-inspired models have up to a 68 times enhancement in generating unseen unique and valid samples compared to the GANs explored here, and a ratio of 61:2 for generating samples with better quality than those observed in the training set. We foresee these metrics as valuable tools for rigorously defining practical quantum advantage in the domain of generative modeling, where finding a winning model remains an ongoing challenge as new architectures are developed.
Quantum generative models offer a promising new direction in machine learning by leveraging quantum circuits to enhance data generation capabilities. In this study, we propose a hybrid quantum-classical image generation framework that integrates variational quantum circuits into a diffusion-based model. To improve training dynamics and generation quality, we introduce two novel noise strategies: intrinsic quantum-generated noise and a tailored noise scheduling mechanism. Our method is built upon a lightweight U-Net architecture, with the quantum layer embedded in the bottleneck module to isolate its effect. We evaluate our model on MNIST and MedMNIST datasets to examine its feasibility and performance. Notably, our results reveal that under limited data conditions (fewer than 100 training images), the quantum-enhanced model generates images with higher perceptual quality and distributional similarity than its classical counterpart using the same architecture. While the quantum model shows advantages on grayscale data such as MNIST, its performance is more nuanced on complex, color-rich datasets like PathMNIST. These findings highlight both the potential and current limitations of quantum generative models and lay the groundwork for future developments in low-resource and biomedical image generation.
Quantum AI is a new way to think about the future of Artificial Intelligence through the merging of two technologies: quantum computing and advanced machine learning. Quantum AI has the potential to provide solutions to computationally difficult problems that traditional systems cannot solve. Quantum AI's continued success will rely on advancements in hybrid quantum-classical solutions, as well as the expansion of quantum hardware; hence, research remains necessary to conduct systematic benchmarks as well as performance analysis in order to determine both how well quantum AI algorithms perform practically, and where their limitations lie. In this chapter, detailed examinations of benchmarks that have been developed specifically for quantum AI are presented alongside evaluative metrics across various key areas of performance including algorithmic efficiency, scalability, noise immunity, and resource use for various types of quantum architectures. The chapter integrates dialogue between theory and practice to demonstrate ways in which performance metrics (i.e., quantum circuit depth, fidelity, convergence behaviour and computational complexity) can effectively compare Quantum AI algorithms to classical algorithms. Particular attention is given to hybrid variational algorithms, quantum-enhanced optimization algorithms, and quantum machine learning. Using benchmark datasets, simulation frameworks and new evaluation methods, there are several challenges in terms of reproducibility, hardware variability and algorithm/hardware co-design that emerge through analysis of the above metrics as well. Aside from focusing on a more broad view of how Performance Evaluation relates to developing more effective Quantum AI Solutions. In addition to viewing Benchmarking as a technical exercise solely, this chapter provides a more holistic view in relation to a human perspective of the situation. This paper outlines how performance evaluation practices can be used as a considerable and well-designed means of bridging the gap between the promise of Quantum Technology at an abstract level, and the ability of Quantum Technology to produce tangible outcomes in the real world by adopting a more comprehensive Governance-oriented approach to Benchmarking Quantum AI Systems. The purpose of this chapter is to assist the growing community of researchers, practitioners, and Strategic Technology Professionals by providing them with a clear and structured framework for Evaluation of Quantum AI Algorithms. There is a significant focus of the chapter on establishing important reference points that organizations can use to determine when an intelligent system has successfully achieved a true and validated quantum advantage. The ultimate goal is to assist in guiding the responsible development and implementation of Quantum AI to enable the Potential Benefits of Quantum AI to be realized, in ways that are meaningful and impactful.
Abstract Quantum machine learning offers powerful advantages in processing high-dimensional data, but loading classical probability distributions into quantum states remains a major challenge. This work extends quantum generative adversarial networks (qGANs) to overcome two critical limitations of early approaches: poor robustness to noise on near-term devices and limited capacity to model complex, multi-modal distributions. We introduce hybrid qGAN architectures that combine Wasserstein GAN with gradient penalty (WGAN-GP) and maximum mean discrepancy (MMD) losses with expressive quantum circuits, including quantum convolutional neural networks (QCNNs) and EfficientSU2 ansätze. Training is performed using seamless PyTorch–Qiskit integration, enabling stable optimization on both simulators and real quantum hardware.Experiments on 2D Gaussian and log-normal distributions—directly relevant to financial modeling—show faster convergence and higher fidelity than prior qGAN designs, with up to 80% lower Wasserstein distance under 5% depolarizing noise. The framework is further validated through European call option pricing via quantum amplitude estimation (QAE), achieving pricing errors below 1% on IBM’s 20-qubit superconducting systems. Theoretical analysis confirms gradient stability under noise, while empirical results include comprehensive noise sweeps, t-SNE latent space visualizations, and rigorous statistical tests (KL divergence, KS tests). This work establishes a practical path toward quantum advantage in generative modeling, with fully open-source code for reproducibility.
As quantum computing advances from theoretical promise to experimental reality, the need for rigorous experiment tracking becomes critical. Drawing inspiration from best practices in machine learning (ML) and artificial intelligence (AI), we argue that reproducibility, scalability, and collaboration in quantum research can benefit significantly from structured tracking workflows. This paper explores the application of MLflow in quantum research, illustrating how it enables better development practices, experiment reproducibility, decision making, and cross-domain integration in an increasingly hybrid classical-quantum landscape.
As quantum computing advances from theoretical promise to experimental reality, the need for rigorous experiment tracking becomes critical. Drawing inspiration from best practices in machine learning (ML) and artificial intelligence (AI), we argue that reproducibility, scalability, and collaboration in quantum research can benefit significantly from structured tracking workflows. This paper explores the application of MLflow in quantum research, illustrating how it enables better development practices, experiment reproducibility, decision making, and cross-domain integration in an increasingly hybrid classical-quantum landscape.