arXiv:2411. 19896v2 Announce Type: replace-cross Abstract: Understanding the capabilities of classical simulation methods is key to identifying where quantum computers are advantageous.
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:2510. 12430v2 Announce Type: replace-cross Abstract: Translating a general quantum circuit on a specific hardware topology with a reduced set of available gates, also known as transpilation, comes with a substantial increase in the length of the equivalent circuit.
arXiv:2608. 04252v1 Announce Type: cross Abstract: The dynamical Lie algebraic (DLA) theory of variational quantum algorithms (VQAs) predicts commonplace exponentially vanishing loss and gradient variances for sufficiently deep parametrized circuits.
arXiv:2606. 29636v1 Announce Type: cross Abstract: An important task in quantum computing is unitary circuit synthesis compatible with physical hardware constraints.
arXiv:2608. 05110v1 Announce Type: cross Abstract: Near-term quantum hardware limits circuit depth and often imposes geometrically local connectivity for quantum generative models, restricting the output distributions accessible to shallow unitary Born models.
arXiv:2411.10406v4 Announce Type: replace-cross Abstract: In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, sm...
The paper introduces Q-Edge, a quantum framework that leverages symmetry to reduce the complexity of simulating high‑dimensional extreme events. By representing dependence in orbit space, the method collapses millions of angular states into a few symmetry classes, cutting the required qubits from about 21 to 8 for a 30‑dimensional problem. This symmetry‑reduced approach enables scalable simulation and digital twins of structured extreme systems.
The paper introduces the Quantum Scrambling Born Machine, a quantum generative model that uses a fixed entangling unitary—acting as a scrambling reservoir—to generate multi‑qubit entanglement while only optimizing single‑qubit rotations. Three types of entanglers are examined: a Haar random unitary, a finite‑depth brickwork random circuit, and analog time evolution under nearest‑neighbor spin‑chain Hamiltonians. The study finds that once the entangler achieves near‑Haar‑typical entanglement, the model can learn benchmark distributions with little sensitivity to the specific scrambler, and that making the Hamiltonian couplings trainable turns the task into a variational Hamiltonian problem with performance competitive with classical generative models at comparable parameter counts.
arXiv:2607. 21409v1 Announce Type: cross Abstract: A central challenge in quantum machine learning is understanding the scaling behavior of parameterized quantum circuits (PQCs).
arXiv:2604. 01197v4 Announce Type: replace-cross Abstract: Learning quantum states from measurement data is a central problem in quantum information and computational complexity.
Quantum MeanFlow (QMF) is a new quantum generative sampling method that enables single‑step sample generation by learning an average velocity field over a time interval, unlike the multi‑step quantum flow matching (QFM) which requires sequential integration of an ordinary differential equation. Using parameterized quantum circuits, the authors benchmark QMF and QFM on the MNIST dataset, finding that QMF produces lower image quality than multi‑step QFM but outperforms single‑step QFM at every shot count. Both models were executed on IBM quantum computers, and best‑of‑N rejection sampling mitigates device noise without circuit modification, demonstrating QMF’s practicality for efficient single‑step quantum generative sampling.
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.