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.
By Brian Coyle, Snehal Raj, Virag Umathe, El Amine Cherrat, Elham Kashefi
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.
By Ruchira V Bhat, Rahul Bhowmick, Avinash Singh, Krishna Kumar Sabapathy
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.
By Seongmin Kim, Abhinav Rijal, Yuri Alexeev, Nora Bauer, Martin Roetteler, Mina Yoon, George Siopsis, In-Saeng Suh
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 ($\mathcal{O}(1/2^n)$).
By Syed Farhan Ahmad, Gregory T. Byrd
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.
By Timothy Heightman, Elena Orlova, Philip Mantrov, Aleksei Ustimenko
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.
By Johannes Jakubik, Isabelle Wittmann, Zaheed Gaffoor, Gciniwe Simphiwe Baloyi, Craig Mahlasi, Etienne Vos, Thomas Brunschwiler, Daniel J. Egger