The paper introduces HELIA, a Hardware‑Efficient and Dynamical Lie‑Algebra Supported Ansatz, and two hybrid training methods that combine classical‑enhanced gradient computation with the quantum Parameter‑Shift Rule. By distributing gradient estimation between classical and quantum hardware, the authors reduce quantum‑resource usage while improving accuracy in Variational Quantum Eigensolver (VQE) ground‑state estimation for 6‑ to 18‑qubit Hamiltonians and in quantum phase classification for up to 12 qubits. The methods achieve up to a 60 % reduction in logical quantum evaluations for VQE and a 2.8‑percentage‑point gain in peak test accuracy for classification, suggesting a promising path toward training larger‑qubit quantum models.
arXiv:2606. 09734v1 Announce Type: cross Abstract: Training parameterised quantum circuits (PQCs) on quantum hardware is bottlenecked by the measurement cost of gradient estimation, which under the parameter-shift rule scales linearly in the number of trainable parameters and dominates the total shot budget of training at scale.
The paper introduces two meta‑learning algorithms—Meta‑Variational Quantum Thermalizer (Meta‑VQT) and Neural Network Meta‑VQT (NN‑Meta VQT)—to efficiently prepare quantum Gibbs states for parametrized many‑body Hamiltonians on NISQ devices. Meta‑VQT employs a fully quantum ansatz, while NN‑Meta VQT uses a quantum‑classical hybrid architecture, both optimizing over a finite training set to generate accurate thermal states at unseen parameter values. The methods are validated on Transverse Field Ising Models up to eight qubits, a two‑qubit Heisenberg model, a 3‑qubit Kitaev ring, and are applied to train a Quantum Boltzmann Machine, yielding significant speedups and improved accuracy over existing techniques.
arXiv:2607. 20225v1 Announce Type: cross Abstract: While combinatorial optimization problems are central to many scientific and engineering applications, their solution remains challenging due to exponentially large search spaces.
arXiv:2606. 01291v1 Announce Type: cross Abstract: Training Variational Quantum Circuits (VQCs) under Noisy Intermediate-Scale Quantum (NISQ) constraints introduces severe computational limitations: classical statevector simulation memory scales exponentially ($\mathcal{O}(2^n)$), and global cost functions suffer from barren plateaus where gradient variance decays exponentially ($\mathcal{O}(1/2^n)$).
arXiv:2608. 11911v1 Announce Type: cross Abstract: A central promise of useful quantum advantage is the ability to compute ground states of Hamiltonian systems beyond the reach of classical simulation methods.
The paper presents a method for learning the mapping from combinatorial optimization problem instances to high‑quality variational angles for the Quantum Approximate Optimization Algorithm (QAOA). By training models that predict these angles, the authors replace the expensive per‑instance classical optimization with a single forward pass, achieving near‑identical solution quality while being several orders of magnitude faster. The learned models generalize to unseen graph topologies, and experiments on quantum hardware confirm that the sampled solutions are indistinguishable from those obtained with traditional solver‑derived angles.
arXiv:2606. 31536v1 Announce Type: new Abstract: As Quantum Machine Learning (QML) transitions toward practical implementation, the field faces a critical architectural bottleneck that challenges the fundamental assumptions of classical statistical learning theory.
arXiv:2607. 20943v1 Announce Type: cross Abstract: Quantum Phase Estimation (QPE) is a foundational algorithm for molecular ground-state energy estimation, but its deep circuit requirements make direct hardware execution impractical on Noisy Intermediate-Scale Quantum (NISQ) devices.
arXiv:2607. 01329v1 Announce Type: cross Abstract: The geometric and topological structure of quantum cost landscapes (QCLs) governs the optimization and thus the predictive power of variational quantum algorithms (VQAs).
The review examines how machine learning can adapt quantum combinatorial optimization workflows by learning decisions such as formulation, penalties, shot budgets, and backend selection. It categorizes the literature into three paradigms—AI for quantum optimization, quantum for AI-driven optimization, and AI‑quantum co‑optimization—and analyzes 119 papers, finding stronger evidence for AI assisting quantum methods than for quantum enhancing learned optimizers. The authors highlight limited experimental controls, introduce an M0‑M5 evidence hierarchy, and argue that scaling is a systems problem requiring careful allocation of classical and quantum resources.
arXiv:2606. 19486v1 Announce Type: cross Abstract: Characterizing the features of a Hamiltonian that governs a quantum system serves as a fundamental subroutine of quantum device calibration, signal sensing, and error correction.
arXiv:2606. 14822v1 Announce Type: cross Abstract: 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.