Quantum Neural Networks (QNNs) harness quantum superposition and entanglement, offering promising advantages for machine learning tasks. However, noise in quantum computers frequently disrupts QNN training, wasting computational resources and extending queue times. This paper introduces the first QNN checkpointing framework to address this challenge. Through experiments on various quantum devices, we demonstrate that QNN behavior is fundamentally hardware-dependent, with the same model performing differently across platforms. This key finding shows that quantum checkpoints require additional metadata about hardware specifics and shot counts unique to quantum systems. Our framework requires minimal storage (only 186.6KB for a 100-qubit QNN) and negligible overhead, enabling frequent checkpointing to enhance training resilience and reproducibility in the NISQ era.
Quantum Neural Networks (QNNs) harness quantum superposition and entanglement, offering promising advantages for machine learning tasks. However, noise in quantum computers frequently disrupts QNN training, wasting computational resources and extending queue times. This paper introduces the first QNN checkpointing framework to address this challenge. Through experiments on various quantum devices, we demonstrate that QNN behavior is fundamentally hardware-dependent, with the same model performing differently across platforms. This key finding shows that quantum checkpoints require additional metadata about hardware specifics and shot counts unique to quantum systems. Our framework requires minimal storage (only 186.6KB for a 100-qubit QNN) and negligible overhead, enabling frequent checkpointing to enhance training resilience and reproducibility in the NISQ era.
In the noisy intermediate-scale quantum (NISQ) era, one of the key questions is how to deal with the high noise level existing in physical quantum bits (qubits). Quantum error correction is promising but requires an extensive number (e.g., over 1,000) of physical qubits to create one "perfect" qubit, exceeding the capacity of the existing quantum computers. This paper aims to tackle the noise issue from another angle: instead of creating perfect qubits for general quantum algorithms, we investigate the potential to mitigate the noise issue for dedicate algorithms. Specifically, this paper targets quantum neural network (QNN), and proposes to learn the errors in the training phase, so that the identified QNN model can be resilient to noise. As a result, the implementation of QNN needs no or a small number of additional physical qubits, which is more realistic for the near-term quantum computers. To achieve this goal, an application-specific compiler is essential: on the one hand, the error cannot be learned if the mapping from logical qubits to physical qubits exists randomness; on the other hand, the compiler needs to be efficient so that the lengthy training procedure can be completed in a reasonable time. In this paper, we utilize the recent QNN framework, QuantumFlow, as a case study. Experimental results show that the proposed approach can optimize QNN models for different errors in qubits, achieving up to 28% accuracy improvement compared with the model obtained by the error-agnostic training.
In the noisy intermediate-scale quantum (NISQ) era, one of the key questions is how to deal with the high noise level existing in physical quantum bits (qubits). Quantum error correction is promising but requires an extensive number (e.g., over 1,000) of physical qubits to create one ”perfect” qubit, exceeding the capacity of the existing quantum computers. This paper aims to tackle the noise issue from another angle: instead of creating perfect qubits for general quantum algorithms, we investigate the potential to mitigate the noise issue for dedicate algorithms. Specifically, this paper targets quantum neural network (QNN), and proposes to learn the errors in the training phase, so that the identified QNN model can be resilient to noise. As a result, the implementation of QNN needs no or a small number of additional physical qubits, which is more realistic for the near-term quantum computers. To achieve this goal, an application-specific compiler is essential: on the one hand, the error cannot be learned if the mapping from logical qubits to physical qubits exists randomness; on the other hand, the compiler needs to be efficient so that the lengthy training procedure can be completed in a reasonable time. In this paper, we utilize the recent QNN framework, QuantumFlow, as a case study. Experimental results show that the proposed approach can optimize QNN models for different errors in qubits, achieving up to 28% accuracy improvement compared with the model obtained by the error-agnostic training.
Quantum machine learning (QML) is promising for potential speedups and improvements in conventional machine learning (ML) tasks. Existing QML models that use deep parametric quantum circuits (PQC) suffer from a large accumulation of gate errors and decoherence. To circumvent this issue, we propose a new QML architecture called QNet. QNet consists of several small quantum neural networks (QNN). Each of these smaller QNN’s can be executed on small quantum computers that dominate the NISQ-era machines. By carefully choosing the size of these QNN’s, QNet can exploit arbitrary size quantum computers to solve supervised ML tasks of any scale. It also enables heterogeneous technology integration in a single QML application. Through empirical studies, we show the trainability and generalization of QNet and the impact of various configurable variables on its performance. We compare QNet performance against existing models and discuss potential issues and design considerations. In our study, we show 43% better accuracy on average over the existing models on noisy quantum hardware emulators. More importantly, QNet provides a blueprint to build noise-resilient QML models with a collection of small quantum neural networks with near-term noisy quantum devices.
As the boundaries of quantum computing continue to expand, its intersection with artificial intelligence (AI) is opening new avenues for computational efficiency, model expressiveness, and problem-solving. One such intersection is the emergence of quantum neural networks (QNNs), which synergize the principles of quantum mechanics with classical machine learning frameworks. The practical realization of QNNs, however, is constrained by today’s noisy intermediate-scale quantum (NISQ) devices, characterized by limited qubit counts, decoherence, and noise. This chapter explores how QNNs can be implemented and trained effectively on near-term hardware. We review current quantum computing technologies, examine the architecture of QNNs, discuss hybrid quantum-classical training strategies, and present a practical case study on digit classification. We also review implementation tools such as Qiskit, PennyLane, and TensorFlow Quantum. The chapter concludes with an outlook on future directions and ongoing challenges in realizing scalable, reliable QNNs.
Quantum neural networks (QNNs) are a revolutionary paradigm placed at the frontier of machine learning and quantum computing. By utilizing quantum mechanical principles like superposition, entanglement, and quantum parallelism, QNNs facilitate the solution of complex machine learning problems that were thought to be impossible according to conventional models. This chapter provides a detailed study of QNNs, from elementary principles to practical applications. It begins with a rundown of the most significant concepts in machine learning and quantum computing, followed by QNN architecture, with important points including quantum gates and parameterized quantum circuits. The chapter addresses challenges in training QNNs, with special attention given to hybrid quantum-classical optimization algorithms and their utility in successful model training. It also examines multiple applications of QNNs, including simulation in quantum chemistry, optimization problems, and real-world machine learning applications, to demonstrate how they have the potential to outperform conventional algorithms in high dimensional or highly entangled data environments. This chapter provides a comparative assessment of quantum machine learning frameworks, quantum hardware platforms, and hybrid optimization methods and aids in determining the most suitable tools to design QNNs. It finally discusses possible areas of future research and research issues in the field, emphasizing the urgent need to improve quantum hardware, increase noise tolerance, and scale up. As quantum computing technology evolves, QNNs are expected to be at the center of solving some of the most complex and computationally intensive problems encountered in various industries.
Quantum Neural Networks (QNNs) represent a ground-breaking confluence of quantum computing and neural network architectures, offering the potential to revolutionize artificial intelligence (AI). By leveraging the principles of quantum mechanics, such as superposition and entanglement, QNNs promise unparalleled capabilities in solving complex computational problems and advancing fields like quantum chemistry, financial modeling, and secure communications. Despite their transformative potential, the development and deployment of QNNs is hindered by significant challenges, including noisy hardware, barren plateaus (BP) in training, security and privacy concerns, and the lack of standardized benchmarks and appropriate evaluation metrics.This position paper explores the current state of QNNs, emphasizing their unique advantages, such as enhanced expressibility and resource efficiency, while addressing their limitations. We propose actionable opportunities for advancements, including hardware innovations and scalable algorithm designs, algorithmic improvements to overcome trainability, security, privacy, and interdisciplinary collaborations. Moreover, we highlight the critical need for ethical considerations and equitable accessibility to ensure inclusive participation in the quantum era. Looking ahead, the paper outlines future directions for achieving scalable, fault-tolerant QNNs and their integration into real-world applications. By charting this strategic path, we aim to foster dialogue and drive progress in harnessing the full potential of QNNs to shape the future of AI and quantum computing.