Quantum machine learning algorithms promise to deliver near-term, applicable quantum computation on noisy, intermediate-scale systems. While most of these algorithms leverage quantum circuits for generic applications, a recent set of proposals, called analog quantum machine learning (AQML) algorithms, breaks away from circuit-based abstractions and favors leveraging the natural dynamics of quantum systems for computation, promising to be noise-resilient and suited for specific applications such as quantum simulation. Recent AQML studies have called for determining best ansatz selection practices and whether AQML algorithms have trap-free landscapes based on theory from quantum optimal control (QOC). We address this call by systematically studying AQML landscapes on two models: those admitting black-boxed expressivity and those tailored to simulating a specific unitary evolution. Numerically, the first kind exhibits local traps in their landscapes, while the second kind is trap-free. However, both kinds violate QOC theory’s key assumptions for guaranteeing trap-free landscapes. We propose a methodology to co-design AQML algorithms for unitary evolution simulation using the ansatz’s Magnus expansion. Our methodology guarantees the algorithm has an amenable dynamical Lie algebra with independently tunable terms. We show favorable convergence in simulating dynamics with applications to metrology and quantum chemistry. We conclude that such co-design is necessary to ensure the applicability of AQML algorithms.
Quantum machine learning algorithms promise to deliver near-term, applicable quantum computation on noisy, intermediate-scale systems. While most of these algorithms leverage quantum circuits for generic applications, a recent set of proposals, called analog quantum machine learning (AQML) algorithms, breaks away from circuit-based abstractions and favors leveraging the natural dynamics of quantum systems for computation, promising to be noise-resilient and suited for specific applications such as quantum simulation. Recent AQML studies have called for determining best ansatz selection practices and whether AQML algorithms have trap-free landscapes based on theory from quantum optimal control (QOC). We address this call by systematically studying AQML landscapes on two models: those admitting black-boxed expressivity and those tailored to simulating a specific unitary evolution. Numerically, the first kind exhibits local traps in their landscapes, while the second kind is trap-free. However, both kinds violate QOC theory’s key assumptions for guaranteeing trap-free landscapes. We propose a methodology to co-design AQML algorithms for unitary evolution simulation using the ansatz’s Magnus expansion. Our methodology guarantees the algorithm has an amenable dynamical Lie algebra with independently tunable terms. We show favorable convergence in simulating dynamics with applications to metrology and quantum chemistry. We conclude that such co-design is necessary to ensure the applicability of AQML algorithms.
Quantum machine learning promises to deliver near-term practical quantum computation applications using machine learning tools to optimize quantum hardware for scientific applications. Studies in this direction often propose using variational quantum circuits as the underlying hardware. However, these so-called VQCs exhibit flat landscapes with narrow and concentrated local minima, making their optimization inefficient and thwarting their advantage. This dissertation proposes using quantum many-body systems' dynamics for computation instead of circuits. This Analog quantum machine learning approach (AQML) uses tools from optimal control to deliver more manageable optimization landscapes and helps explain what types of computation are suitable for different hardware. Concretely, I show that arrays of Rydberg atoms can be leveraged to implement reservoir computers with enhanced memory storage and retrieval. I also show that two atom-species Rydberg arrays can reproduce the dynamics of the basic building blocks of artificial neural networks and be used for quantum-enhanced sensing and entanglement detection. This study shows that AQML algorithms are theoretically more expressive than their classical counterparts. Lastly, I show that while AQML algorithms can still exhibit problematic optimization landscapes, flatness can be mitigated, and local minima can be circumvented by co-designing the underlying hardware with the desired task. We exemplify this co-design in the context of quantum chemistry calculations. This dissertation shows that AQML algorithms can be used for various scientific and small-scale applications such as sensing, chemical design, entanglement witnessing, and time-series prediction. However, we still have a way to go before seeing full-scale applications. Instead, this dissertation focuses on theoretical studies and answers the question of what we can compute with quantum hardware's analog dynamics.
The convergence of quantum computing and artificial intelligence presents profound opportunities but faces significant hurdles, particularly in the Noisy Intermediate-Scale Quantum (NISQ) era. Quantum Machine Learning (QML) algorithms, while promising, exhibit sensitivity to noise and scalability challenges, hindering the demonstration of practical quantum advantage. Concurrently, Quantum Error Correction (QEC), essential for fault tolerance, imposes substantial resource overheads and is often developed generically, without specific adaptation to the target application's error sensitivity. This paper reviews the current state of the intersection of AI and quantum computing, examining both QML paradigms, e.g., Variational Quantum Algorithms, Quantum Kernels, and the burgeoning use of AI to enhance quantum computing itself, e.g., quantum control, QEC decoding, circuit design. A critical gap identified is the lack of frameworks that systematically co-design QML algorithms and QEC protocols adaptively. To address this, a novel framework is proposed based on Reinforcement Learning (RL). This framework employs an RL agent to dynamically adjust both the QML circuit architecture, e.g., VQC ansatz, and QEC parameters, e.g., decoding strategy, measurement frequency, based on observed application performance and estimated error characteristics. This adaptive co-design loop aims to optimize the trade-off between QML performance and QEC overhead, enhancing noise resilience and resource efficiency. The potential advantages, feasibility, and limitations of this approach are discussed, alongside with future research directions aimed at realizing robust and practical Quantum AI.
Quantum machine learning sits at the intersection of two paradigm-shifting technologies: quantum computing and deep learning. By exploiting quantum mechanical phenomena – superposition, entanglement, and interference – QML promises exponential or quadratic speedups over classical algorithms for certain learning tasks, as well as the ability to model quantum data natively. However, the path to practical quantum advantage remains contested, constrained by noisy intermediate-scale quantum (NISQ) hardware, limited qubit counts (50–1,000), coherence times (50–500 μs), and high gate error rates (10⁻³–10⁻⁴). This paper provides a rigorous, technically deep survey of QML: mathematical foundations (Hilbert spaces, unitary evolution, measurement, quantum circuits), canonical algorithms (quantum kernel methods, variational quantum circuits (VQCs), quantum neural networks (QNNs), QSVMs, quantum generative adversarial networks (QGANs), encoding strategies (angle, amplitude, basis, and Hamiltonian encoding), optimization landscapes (barren plateaus, parameter shift rules, ansatz design), hardware aware implementation on superconducting (IBM, Google), trapped ion (IonQ), and neutral atom (Pasqal) platforms, empirical benchmarks on near term devices for classification, generative modeling, and quantum chemistry, and pathways to fault tolerant quantum machine learning (FTQML) with error correction (surface codes, logical qubits). Researchers critically assess claims of quantum advantage, identify scenarios where QML may outperform classical ML (small data, high-dimensional feature spaces from quantum systems, kernel estimation), and propose concrete evaluation metrics (quantum-classical cross-validation, resource estimation).
Recent advances in Machine Learning have transformed numerous industrial sectors, yet classical paradigms face fundamental limitations: rapidly growing data volumes, rising computational costs, significant energy consumption, and the physical scaling limits of conventional hardware architectures. Quantum computing has emerged as a promising computational paradigm to address these challenges, giving rise to the field of Quantum Machine Learning (QML). In this thesis, the theoretical foundations of QML are investigated, with a focus on near-term and future practical applications. Three central challenges are addressed: the trainability of variational quantum circuits, their expressivity, and their resistance to efficient classical simulation. The trainability of Hamming-weight preserving variational quantum circuits is first studied, and theoretical guarantees are established that resolve an open conjecture on the absence of barren plateaus for this circuit family. Subspace-preserving QML algorithms are then introduced, including photonic circuits and quantum convolutional neural networks, and are designed to mimic classical ML subroutines while offering polynomial quantum advantage. Finally, variational quantum circuits are analyzed as quantum Fourier models, and a framework is derived to jointly characterize expressivity and trainability, from which conditions are obtained under which quantum models provably separate from their classical counterparts. These contributions are intended to advance the theoretical roadmap for harnessing near-term and future quantum technologies in real-world applications.
Quantum machine learning (QML) holds significant promise for accelerating and enhancing machine learning tasks. However, the inherent fragility of quantum systems to noise poses a substantial challenge to the successful implementation of QML algorithms. This paper investigates and proposes novel noise optimization strategies specifically tailored for QML. The core claim is to design a strategy capable of effectively handling noise within QML algorithms, ultimately boosting model training accuracy and robustness. The proposed mechanism leverages either the quantum decoherence principle to suppress noise or quantum entanglement mechanisms to mitigate its effects. We explore various approaches, focusing on adaptive noise mitigation techniques and incorporating error correction methodologies within the quantum computation framework. The presented framework aims to improve the reliability of QML algorithms and contribute to the development of practical, robust quantum machine learning systems. The research addresses a critical gap in the field by offering a systematic approach to noise management in QML, a crucial step towards realizing the full potential of this technology. The ultimate goal is to demonstrate that, with appropriate noise optimization, QML algorithms can achieve performance comparable to, or exceeding, classical machine learning algorithms.
Quantum Machine Learning (QML) unites quantum computing with modern AI to achieve potential gains in expressivity, runtime, and sample efficiency. This paper synthesizes advances in quantum feature maps, variational quantum circuits (VQCs), and hybrid quantum-classical workflows deployable on noisy intermediate-scale quantum (NISQ) hardware. We outline design principles for data encoding, ansatz selection, and trainability, and summarize error-aware practices—error mitigation, shot-frugal estimation, and compiler co-design. Using synthetic but instructive experiments, we illustrate scaling behavior, noise sensitivity, end-to-end inference latency across backends, and optimizer convergence in variational training. We conclude with an agenda emphasizing domain-aligned encodings, rigorous benchmarking, and reproducible open science to validate advantage beyond asymptotic claims.