QiT is a Quantum‑Inspired Transformer designed for visual recognition tasks. It replaces quantum neural network concepts with scalable classical operations: angle‑inspired encoding of image tokens, self‑attention over periodic features approximating quantum fidelity kernels, and gated multiplicative emulation of variational circuit interactions. The model achieves competitive performance on image‑classification benchmarks, matching a classical Transformer while avoiding the high runtime costs of simulated quantum models.
By Badri N. Patro, Vijay Agneeswaran
arXiv:2604. 06135v2 Announce Type: replace-cross Abstract: Efficient data loading remains a bottleneck for near-term quantum machine learning.
By Basil Kyriacou, Viktoria Patapovich, Maniraman Periyasamy, Alexey Melnikov
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.
By Bodo Rosenhahn, Tobias J. Osborne, Christoph Hirche
arXiv:2608. 11884v1 Announce Type: cross Abstract: Quantum generative adversarial networks (QGANs) have attracted increasing attention for image generation using parameterized quantum circuits.
By Xue Yang, Rigui Zhou, ShiZheng Jia, Dax Enshan Koh, Siong Thye Goh, Young-Wook Cho, YaoChong Li, Xuezhi Ma, Hongyu Chen, Xin Wang
Quantum Implicit Neural Representations for Novel View Synthesis introduces 3D Quantum Implicit Scene Representation (3D-QISR), a hybrid quantum‑classical radiance‑field model that replaces the classical NeRF backbone with parameterised quantum circuits. Two architectures are proposed: Full 3D-QISR, which uses a unified quantum state, and Dual‑Branch 3D-QISR, which separates spatial and view‑dependent embeddings to reduce complexity and improve scalability. Experiments on moderate‑resolution novel‑view synthesis benchmarks show that 3D‑QISR on simulated quantum hardware matches or outperforms classical baselines while using fewer than half the trainable parameters.
By Daniele Lizzio Bosco, Shuteng Wang, Giuseppe Serra, Vladislav Golyanik
arXiv:2606. 26312v1 Announce Type: cross Abstract: Autoencoders transformed classical machine learning by solving the curse of dimensionality, enabling principled weight initialization and learning compact, structured representations.
By Aldo Lamarre, Dominik \v{S}afr\'anek
Quantum generative adversarial networks (QGANs) have attracted increasing attention for image generation using parameterized quantum circuits. Existing amplitude-based approaches face two key limitations: pixel locations are typically encoded by computational-basis indices or address qubits, causing quantum resources to grow with image resolution; meanwhile, jointly decoding many pixels from normalized quantum states introduces probability competition among pixels and limits precise pixel-wise control.
arXiv:2603. 00233v2 Announce Type: replace-cross Abstract: Quantum generative modeling is a rapidly evolving discipline at the intersection of quantum computing and machine learning.
By Jonas J\"ager, Florian J. Kiwit, Carlos A. Riofr\'io
The paper introduces a Transformer-based Quantum State Characterizer (TQSC) designed to accurately reconstruct photonic polarization states in remote state preparation (RSP) experiments subject to complex scattering and dynamic Gaussian noise. TQSC achieves over 99.999% fidelity for noisy measurements and demonstrates significant error reduction in a practical MNIST image transmission task, while its attention mechanisms offer physically grounded insights into observable correlations. The model’s robustness is further validated on Qiskit-simulated Bloch-ball states, indicating its potential for intelligent quantum information processing.
By Bo Tang, Zixuan Liao, Hao Li, Yilin Yang, Jiani Lei, Zengya Li, Jing Qiu, Zhaohui Dong, Zhengyang Mao, Yuanhua Li, Yuanlin Zheng, Xianfeng Chen
arXiv:2607. 08928v1 Announce Type: cross Abstract: This work presents a study of an implementation of a novel Quantum Convolutional Neural Network (QCNN) for binary classification of images from the Modified National Institute of Standards and Technology (MNIST) dataset.
By Lawrence Nguyen, Hiu Yung Wong
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: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.
By Qipeng Qian, Keli Deng, Yuntao Qian