We introduce a physics-informed Generative Adversarial Network framework that recasts quantum resource-state generation as an inverse-design task. By embedding task-specific utility functions into training, the model learns to generate valid two-qubit states optimized for teleportation and entanglement broadcasting. Comparing decomposition-based and direct-generation architectures reveals that structural enforcement of Hermiticity, trace-one, and positivity yields higher fidelity and training stability than loss-only approaches. The framework reproduces theoretical resource boundaries for Werner-like and Bell-diagonal states with fidelities exceeding ~98%, establishing adversarial learning as a lightweight yet effective method for constraint-driven quantum-state discovery. This approach provides a scalable foundation for automated design of tailored quantum resources for information-processing applications, exemplified with teleportation and broadcasting of entanglement, and it opens up the possibility of using such states in efficient quantum network design.
We introduce a physics-informed Generative Adversarial Network framework that recasts quantum resource-state generation as an inverse-design task. By embedding task-specific utility functions into training, the model learns to generate valid two-qubit states optimized for teleportation and entanglement broadcasting. Comparing decomposition-based and direct-generation architectures reveals that structural enforcement of Hermiticity, trace-one, and positivity yields higher fidelity and training stability than loss-only approaches. The framework reproduces theoretical resource boundaries for Werner-like and Bell-diagonal states with fidelities exceeding ~98%, establishing adversarial learning as a lightweight yet effective method for constraint-driven quantum-state discovery. This approach provides a scalable foundation for automated design of tailored quantum resources for information-processing applications, exemplified with teleportation and broadcasting of entanglement, and it opens up the possibility of using such states in efficient quantum network design.
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.
Quantum state and process tomography constitute essential diagnostic tools in quantum information science, yet their standard formulations suffer from prohibitive computational scaling as the number of qubits grows. In this work, we introduce a physics-constrained conditional generative adversarial network that bypasses iterative constrained inversion by directly learning a forward generative mapping conditioned on Pauli expectation values. The generator embeds a differentiable Cholesky layer at its output, which enforces Hermiticity, positive semidefiniteness, and unit trace by construction. Our experiments reveal that the strength of the L1 penalty critically governs the emergence of GHZ coherence during training: an excessively large penalty postpones the coherence onset and yields a prolonged low-fidelity plateau, whereas an intermediate value enables the fastest stable convergence. Moreover, for high-temperature thermal states, an over-complete measurement basis proves necessary to prevent sustained late-stage fluctuations. By extending the same Cholesky constraint to the Choi-matrix representation, the framework naturally accommodates quantum process tomography. For systems with n≥6 qubits, the exponential growth of the underlying 2n×2n density matrix remains the fundamental bottleneck; we discuss how integrating tensor-network structures can contain the per-iteration cost while preserving reconstruction fidelity. Altogether, these results suggest that physically constrained generative learning offers a scalable and amortizable pathway toward data-driven tomography for noisy intermediate-scale quantum devices.
Abstract Generative Adversarial Networks (GANs) have revolutionized Deep learning-based generative models are still challenging to train. This is due to issues such as mode collapse, instability, and non-convergence. This article provides a comprehensive analysis of the challenges faced by classical GANs, offering insights into their computational flow and limitations. Additionally, it explores Quantum Generative Adversarial Networks (QuGANs), detailing their unique architecture and the mechanism of quantum information generation. To further advance the field, this work presents an in-depth analysis of a hybridized model combining classical GANs and QuGANs, leveraging the strengths of both paradigms to enhance learning and stability. Furthermore, a novel algorithm for training quantum–classical GANs is proposed, with a detailed complexity analysis to assess its computational efficiency. The integration of quantum principles into classical frameworks offers a promising direction to mitigate existing GANs limitations while unlocking new possibilities for quantum machine learning. The proposed approach aims to bridge the gap between classical and quantum generative models, improving training dynamics and sample quality. The objective of this investigation is to establish a foundation for improving the efficiency and comprehension of hybrid GANs architectures, which will allow the design of more efficient and durable generative models in quantum computing.
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.
Quantum machine learning holds the promise of harnessing quantum advantage to achieve speedup beyond classical algorithms. Concurrently, research indicates that dissipation can serve as an effective resource in quantum computation. In this paper, we introduce a novel dissipation-driven quantum generative adversarial network (DQGAN) architecture specifically tailored for generating classical data. Our DQGAN comprises two interacting networks: a generative network and a discriminative network, both constructed from qubits. The classical data is encoded into the input qubits of the input layer via strong tailored dissipation processes. This encoding scheme enables us to extract both the generated data and the classification results by measuring the observables of the steady state of the output qubits. The network coupling weight, i.e., the strength of the interaction Hamiltonian between layers, is iteratively updated during the training process. This training procedure closely resembles the training of conventional generative adversarial networks (GANs). By alternately updating the two networks, we foster adversarial learning until the equilibrium point is reached. Our preliminary numerical test on a simplified instance of the task substantiate the feasibility of our DQGAN model.
Quantum state preparation is a fundamental and often rate-limiting step in many quantum information processing tasks, including quantum computation, quantum communication, and quantum metrology. Traditional methods for preparing complex quantum states rely on iterative techniques, which can be computationally expensive and time-consuming. This paper proposes a novel approach leveraging Generative Adversarial Networks (GANs) to directly generate complex quantum states from classical inputs. The core idea is to train a GAN to learn the probability distribution of quantum states, effectively learning a mapping between classical data and corresponding quantum states. We formulate the problem as a minimax game between a generator and a discriminator, where the generator aims to produce realistic quantum states, and the discriminator aims to distinguish between generated and real quantum states. The mathematical formulation of this process is detailed below. We hypothesize that this approach will offer significant speedups compared to traditional state preparation methods, particularly for complex states. The success of this approach hinges on the ability of the GAN to accurately capture the intricate correlations inherent in quantum state distributions. This work provides a foundational framework for exploring the application of GANs in quantum state preparation and lays the groundwork for future research in this area.