Quantum Information Capsule and Its Applications to Communication Through Quantum Fields

Koji Yamaguchi, Naoki Watamura, Masahiro Hotta

Koji Yamaguchi, Masahiro Hotta

Mohammad H. Ansari

Awal Ahmed Fime, Tasfia Zaman Samiha, Saika Zaman +4 more

Variational quantum algorithms are often limited by barren plateaus: gradients vanish as circuit size and depth increase, making quantum neural networks difficult to train. We propose Q-Capsule, a localized capsule-based quantum neural architecture that mitigates this problem through register partitioning, local readout, sparse inter-capsule coupling, trainable data re-uploading, and Quantum Fisher Information Matrix (QFIM)-guided adaptive depth growth. By restricting the dominant support of each observable to a small capsule and controlling inter-capsule entanglement, Q-Capsule preserves useful gradient signals while retaining communication between local quantum representations. As the register width increases, Q-Capsule consistently maintains stable gradient variance, whereas globally entangling baselines exhibit exponential suppression with a log-gradient-variance slope near -ln 2 per qubit. Q-Capsule also produces more structured optimization landscapes, higher parameter efficiency, improved robustness to depolarizing noise, and lower measurement requirements. Its adaptive policy achieves 98.1% accuracy on binary classification and 97.7% on four-class classification, while using approximately 73% fewer two-qubit gates than the fixed-deep model on the multiclass task.

Koji Yamaguchi, Aida Ahmadzadegan, Petar Simidzija +2 more

Given that any communication is communication through quantum fields, we study here the scenario where a sender, Alice, causes information-carrying disturbances in a quantum field. We track the exact spread of these disturbances in space and time by using the technique of quantum information capsules (QIC). We find that the channel capacity between Alice and a receiver, Bob, is enhanced by Bob placing detectors not only inside but in addition also outside the causal future of Alice's encoding operation. Intuitively, this type of superadditivity arises because the field outside the causal future of Alice is entangled with the field inside Alice's causal future. Hence, the quantum noise picked up by Bob's detectors outside Alice's causal future is correlated with the noise of Bob's detectors inside Alice's causal future. In effect, this correlation allows Bob to improve the signal-to-noise ratio of those of his detectors which are in the causal future of Alice. Further, we develop the multimode generalization of the QIC technique. This allows us to extend the analysis to the case where Alice operates multiple localized and optionally entangled emitters. We apply the new techniques to the case where Alice enhances the channel capacity by operating multiple emitters that are suitably lined up and pretimed to generate a quantum shockwave in the field.

Zidu Liu, Pei-Xin Shen, Weikang Li +2 more

Abstract Capsule networks (CapsNets), which incorporate the paradigms of connectionism and symbolism, have brought fresh insights into artificial intelligence (AI). The capsule, as the building block of CapsNets, is a group of neurons represented by a vector to encode different features of an entity. The information is extracted hierarchically through capsule layers via routing algorithms. Here, we introduce a quantum capsule network (dubbed QCapsNet) together with an efficient quantum dynamic routing algorithm. To benchmark the performance of the QCapsNet, we carry out extensive numerical simulations on the classification of handwritten digits and symmetry-protected topological phases, and show that the QCapsNet can achieve an enhanced accuracy and outperform conventional quantum classifiers evidently. We further unpack the output capsule state and find that a particular subspace may correspond to a human-understandable feature of the input data, which indicates the potential explainability of such networks. Our work reveals an intriguing prospect of QCapsNets in quantum machine learning, which may provide a valuable guide towards explainable quantum AI.