arXiv:2401. 07039v5 Announce Type: replace-cross Abstract: Mixed quantum states are the native description of many physically important quantum systems, making their generation a fundamental task in quantum information processing.
arXiv:2512. 06695v3 Announce Type: replace Abstract: Quantum generative models exploit quantum superposition and entanglement to enhance learning efficiency for both classical and quantum data.
Quantum diffusion models provide a physics-consistent route to generative learning by formulating noising and denoising directly on quantum states. However, applying such models to classical high-dimensional data is constrained by the qubit cost of state encoding and the computational burden of simulating large density operators.
arXiv:2606. 27561v1 Announce Type: new Abstract: Generative models have achieved remarkable success in data synthesis, though recent advances driven by increasing model scale have introduced challenges in computational cost and efficiency.
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. 07072v1 Announce Type: new Abstract: Quantum diffusion models provide a physics-consistent route to generative learning by formulating noising and denoising directly on quantum states.
arXiv:2603. 09789v3 Announce Type: replace-cross Abstract: Accurate financial volatility forecasting is crucial but challenged by the non-linear, highly correlated nature of market data.
The paper investigates quantum reservoir computing using highly scrambling quantum systems modeled by high‑order unitary designs. It shows that in noiseless settings, measurement readouts become exponentially concentrated as the reservoir size grows, but this concentration does not worsen with repeated iterations, while memory of early inputs decays exponentially with both reservoir size and iterations. In noisy environments, memory also decays exponentially over time for local noisy channels, and the study introduces new proof techniques for bounding concentration in temporal quantum models. Numerical results indicate that even physical reservoirs, such as an Ising model in a quantum‑chaotic phase, can exhibit exponential concentration, whereas reservoirs in a many‑body localized phase or at the edge of chaos do not.
arXiv:2305. 06177v1 Announce Type: cross Abstract: We present a thermodynamic analysis of a quantum engine that uses a single quantum particle as its working fluid, inspired by Szilard's classical single-particle engine.
arXiv:2506. 21324v3 Announce Type: replace-cross Abstract: Neuromorphic and quantum computing have recently emerged as promising paradigms for advancing artificial intelligence, each offering complementary strengths.
arXiv:2608.21700v1 Announce Type: cross Abstract: Continuous-time flow and diffusion models are widely used across many application domains, from large-scale deployment in computer vision and protein...
arXiv:2606. 24933v1 Announce Type: cross Abstract: Recent advances in quantum machine learning have motivated efficient models for sequential data processing.