Quantum Super-resolution by Adaptive Non-local Observables

• URL: http://arxiv.org/abs/2601.14433v1
• Date: Tue, 20 Jan 2026 19:40:59 GMT
• Title: Quantum Super-resolution by Adaptive Non-local Observables
• Authors: Hsin-Yi Lin, Huan-Hsin Tseng, Samuel Yen-Chi Chen, Shinjae Yoo,
• Abstract summary: Super-resolution (SR) seeks to reconstruct high-resolution (HR) data from low-resolution (LR) observations.<n>We propose a framework based on Variational Quantum Circuits (VQCs) with emphAdaptive Non-Local Observable (ANO) measurements.<n>ANO introduces trainable multi-qubit Hermitian observables, allowing the measurement process to adapt during training.<n>Experiments demonstrate that ANO-VQCs achieve up to five-fold higher resolution with a relatively small model size.
• Score: 34.914387170069844
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Super-resolution (SR) seeks to reconstruct high-resolution (HR) data from low-resolution (LR) observations. Classical deep learning methods have advanced SR substantially, but require increasingly deeper networks, large datasets, and heavy computation to capture fine-grained correlations. In this work, we present the \emph{first study} to investigate quantum circuits for SR. We propose a framework based on Variational Quantum Circuits (VQCs) with \emph{Adaptive Non-Local Observable} (ANO) measurements. Unlike conventional VQCs with fixed Pauli readouts, ANO introduces trainable multi-qubit Hermitian observables, allowing the measurement process to adapt during training. This design leverages the high-dimensional Hilbert space of quantum systems and the representational structure provided by entanglement and superposition. Experiments demonstrate that ANO-VQCs achieve up to five-fold higher resolution with a relatively small model size, suggesting a promising new direction at the intersection of quantum machine learning and super-resolution.

Related papers

• Enhancing Quantum Diffusion Models for Complex Image Generation [0.8373057326694194]
  In this study, we explore a Hybrid Quantum-Classical U-Net architecture integrated with Adaptive Non-Local Observables (ANO)<n>By compressing classical data into a dense quantum latent space and utilizing trainable observables, our model aims to extract non-local features that complement classical processing.<n> Experimental results on the full MNIST dataset (digits 0-9) demonstrate that the proposed architecture is capable of generating structurally coherent and recognizable images for all digit classes.
  arXiv  Detail & Related papers  (2026-02-03T11:32:19Z)
• Quantum-Efficient Reinforcement Learning Solutions for Last-Mile On-Demand Delivery [1.8262547855491453]
  We investigate quantum computing to solve the large-scale Capacitated Pickup and Delivery Problem with Time Windows.<n>A novel problem-specific encoding quantum circuit with an entangling and variational layer is proposed.
  arXiv  Detail & Related papers  (2025-08-07T13:50:43Z)
• Quantum Reinforcement Learning by Adaptive Non-local Observables [10.617463958884528]
  We introduce an adaptive non-local observable (ANO) paradigm within variational quantum circuits (VQCs)<n>ANO-VQC architecture serves as the function approximator in Deep Q-Network (DQN) and Asynchronous Advantage Actor-Critic (A3C) algorithms.<n>Our results demonstrate that adaptive multi-qubit observables can enable practical quantum advantages in reinforcement learning.
  arXiv  Detail & Related papers  (2025-07-25T18:57:16Z)
• Solving the Hubbard model with Neural Quantum States [66.55653324211542]
  We study the state-of-the-art results for the doped two-dimensional (2D) Hubbard model.<n>We find different attention heads in the NQS ansatz can directly encode correlations at different scales.<n>Our work establishes NQS as a powerful tool for solving challenging many-fermions systems.
  arXiv  Detail & Related papers  (2025-07-03T14:08:25Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• Adaptive Non-local Observable on Quantum Neural Networks [10.617463958884528]
  We propose an adaptive non-local measurement framework for quantum circuits.<n>Inspired by the Heisenberg picture, we show that optimizing VQC rotations corresponds to tracing a trajectory in the observable space.<n>We show that properly incorporating variational rotations with non-local observables enhances qubit interaction and information mixture.
  arXiv  Detail & Related papers  (2025-04-18T02:20:12Z)
• Enhancing Quantum Diffusion Models with Pairwise Bell State Entanglement [35.436358464279785]
  This paper introduces a novel quantum diffusion model designed for Noisy Intermediate-Scale Quantum (NISQ) devices. By leveraging quantum entanglement and superposition, this approach advances quantum generative learning.
  arXiv  Detail & Related papers  (2024-11-24T20:14:57Z)
• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [62.46800898243033]
  Recent progress in quantum learning theory prompts a question: can linear properties of a large-qubit circuit be efficiently learned from measurement data generated by varying classical inputs?<n>We prove that the sample complexity scaling linearly in $d$ is required to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.<n>We propose a kernel-based method leveraging classical shadows and truncated trigonometric expansions, enabling a controllable trade-off between prediction accuracy and computational overhead.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• Scaling Limits of Quantum Repeater Networks [62.75241407271626]
  Quantum networks (QNs) are a promising platform for secure communications, enhanced sensing, and efficient distributed quantum computing. Due to the fragile nature of quantum states, these networks face significant challenges in terms of scalability. In this paper, the scaling limits of quantum repeater networks (QRNs) are analyzed.
  arXiv  Detail & Related papers  (2023-05-15T14:57:01Z)
• Towards Neural Variational Monte Carlo That Scales Linearly with System Size [67.09349921751341]
  Quantum many-body problems are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors. The combination of neural networks (NN) for representing quantum states, and the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems. We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm.
  arXiv  Detail & Related papers  (2022-12-21T19:00:04Z)
• Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution [79.97180849505294]
  We propose a novel coupled unmixing network with a cross-attention mechanism, CUCaNet, to enhance the spatial resolution of HSI. Experiments are conducted on three widely-used HS-MS datasets in comparison with state-of-the-art HSI-SR models.
  arXiv  Detail & Related papers  (2020-07-10T08:08:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.