The paper investigates the use of classically simulated quantum layers in vision‑language models, specifically adding or replacing components in the LLaVA‑OneVision backbone. Experiments with 4‑ and 8‑qubit circuits show that inserting a quantum layer after the visual projector can raise development accuracy by 2.28 percentage points, while replacing the final decoder feed‑forward sublayer reduces trainable parameters by 13.07 million with only a modest drop in accuracy. The study also evaluates robustness against six attack scenarios, mapping where quantum layers improve or degrade performance and security in multimodal models.
arXiv:2606. 29966v1 Announce Type: cross Abstract: Quantum computing provides a powerful paradigm for representing and transforming high-dimensional information through superposition, entanglement, and measurement-induced nonlinear features.
The paper presents an evolutionary framework, EXAQC, that automatically discovers parameterized quantum circuits (PQCs) for use as intermediate modules in hybrid quantum‑classical neural networks for image classification. By evolving PQCs while keeping classical feature‑extraction and prediction layers fixed, the authors achieve high accuracies on MNIST, Fashion‑MNIST, and CIFAR‑10 with gate counts comparable to other quantum architecture‑search methods. The evolved hybrid models match or exceed the performance of classical networks while using far fewer trainable parameters, and the choice of encoding (rotation‑based vs amplitude) significantly impacts accuracy.
arXiv:2607. 10057v1 Announce Type: cross Abstract: Can AI agents visually comprehend quantum circuit diagrams and generate verified executable code--and at what cost?
arXiv:2504. 05336v4 Announce Type: replace-cross Abstract: A recurring weakness in quantum machine learning (QML) is that reported ``quantum advantages'' are seldom tested against a \emph{capacity-matched} classical control, leaving it unclear whether a gain comes from the quantum substrate or from the architectural change that accompanies it.
QART is a quantum‑classical hybrid architecture that augments a language model with quantum encoding, CIM‑based QUBO optimization, and quantum decoding to improve long‑horizon reasoning. The authors claim that, under certain assumptions, QART can maintain a non‑zero probability of recovering an optimal reasoning path while traditional autoregressive LLMs see their acceptance probability drop to zero as cumulative risk grows. Experiments on six benchmarks with three backbone models show that QART outperforms the baselines in 14 of 15 pairings, with relative gains up to 84.0% on SciCode.
Multimodal vision-language systems typically fuse image and text embeddings through classical operators such as concatenation, attention, bilinear pooling, or tensor interactions. We propose Quantum E...
arXiv:2610.08216v1 Announce Type: new Abstract: Multimodal vision-language systems typically fuse image and text embeddings through classical operators such as concatenation, attention, bilinear pool...
Mu-DisCoCat is a multimodal variational quantum learning framework that maps Compositional Distributional Semantics (DisCoCat) onto Variational Quantum Circuits (VQCs) to achieve compositional concept generalization (CoCoGen). The pipeline first learns stable object representations from single-object image-text pairs, then fixes these to learn relations in multi-object scenarios. In classical simulations it outperformed a CLIP baseline on out‑of‑distribution relational accuracy, and on noisy quantum emulators and real IBM and IQM hardware it maintained strong fidelity correlations, reliably distinguishing unseen similar and dissimilar pairs.
arXiv:2604. 23931v2 Announce Type: replace-cross Abstract: Variational quantum circuits (VQCs) are a leading approach to quantum machine learning on near-term devices, yet it remains unclear which circuit architecture yields the best accuracy-parameter trade-off on classical tabular data.
arXiv:2607. 12780v1 Announce Type: cross Abstract: Quantum circuit optimization for fault-tolerant computing requires exact functional equivalence while minimizing expensive non-Clifford resources such as T gates.
arXiv:2605. 27923v2 Announce Type: replace-cross Abstract: The rapid growth of computer vision and increasingly complex image recognition tasks has exposed fundamental computational limitations of classical machine learning models, motivating the exploration of quantum computing as an emerging new paradigm.
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.