Quantum-Enhanced Computer Vision

• Quantum-enhanced Computer Vision (QeCV) is a field that uses quantum principles like superposition and entanglement to transform image processing and pattern recognition.
• It integrates gate-based methods, quantum annealing, and hybrid quantum-classical architectures to address complex tasks such as clustering, stereo matching, and segmentation.
• QeCV offers improved efficiency, scalability, and robustness while tackling challenges in data encoding, hardware noise, and optimization trade-offs.

Quantum-enhanced Computer Vision (QeCV) applies quantum-computational paradigms, including gate-based quantum computing and quantum annealing, to the core problems of computer vision—image representation, signal processing, pattern recognition, and optimization. By leveraging quantum mechanical properties such as superposition, entanglement, and quantum parallelism, QeCV aims to overcome classical limitations in speed, scalability, and solution quality, while opening fundamentally new directions for algorithmic design. The field involves both direct quantum reformulations of vision problems (such as energy minimization and clustering) and hybrid approaches that integrate quantum modules into deep learning architectures. QeCV requires new methodologies compatible with quantum hardware and introduces unique trade-offs in data encoding, computational principles, and hardware constraints (Meli et al., 8 Oct 2025).

1. Quantum Data Encoding and Representations

Fundamental to QeCV is the translation of visual data (e.g., images, spatial coordinates, features) into quantum states. Methods such as amplitude encoding, basis encoding, phase encoding, and higher-order feature maps enable classical pixel arrays or feature vectors to be embedded into exponentially large Hilbert spaces. Flexible Representation of Quantum Images (FRQI), Novel Enhanced Quantum Representation (NEQR), and Quantum Probability Image Encoding (QPIE) are commonly used, with FRQI encoding grayscale as

$$|I(\theta)\rangle = \frac{1}{2^n} \sum_{i=0}^{2^n-1} [\cos(\theta_i)|0\rangle+\sin(\theta_i)|1\rangle] \otimes |i\rangle$$

and NEQR encoding pixel values into basis states (Anand et al., 2022, Warrier et al., 19 Apr 2024). Amplitude encoding is especially resource-efficient, requiring only $O(\log_2 N)$ qubits for $N$ input dimensions, as in whole-image processing for transformers (Zhang et al., 3 Apr 2025). The selection of encoding scheme critically determines the kind of quantum feature extraction and subsequent processing possible.

2. Quantum Algorithms and Circuit-based Methods

Gate-based quantum computing constructs algorithms from parameterized quantum circuits (PQCs), which implement complex nonlinear transformations, feature extraction, and classification layers. For image classification, hybrid architectures such as Quanvolutional Neural Networks (QNNs) process local patches through quantum circuits (e.g., with gates $R_y(\pi/\theta)$) and extract expectation values using Pauli-Z measurements (Reese et al., 2022). In quantum transformers and attention models, key operations (e.g., matrix multiplication, attention score computation) are mapped to quantum linear algebra primitives via block encoding, quantum singular value transformation (QSVT), and swap tests (Xue et al., 29 Feb 2024, Zhang et al., 3 Apr 2025). Self-supervised contrastive learning exploits quantum circuits’ ability to encode subtle feature correlations; gradients are computed with the parameter shift rule (Jaderberg et al., 2021).

Quantum convolution and pooling are realized through translationally invariant matrix multiplications and max-pooling on real/imaginary input parts (Parthasarathy et al., 2020). Quantum non-local neural networks use feature maps such as Qiskit’s ZFeatureMap and variational circuits with entanglers (CNOTs), efficiently capturing global relationships that are expensive to model classically (Gupta et al., 26 Jul 2024).

3. Quantum Annealing and Discrete Optimization

Many vision problems—stereo matching, segmentation, labeling—are expressed as discrete energy minimization over probabilistic graphical models (MRFs, CRFs). Quantum annealing approaches formulate these tasks as Quadratic Unconstrained Binary Optimization (QUBO) problems. The annealing process evolves a system from a simple initial Hamiltonian $H_I$ to the problem Hamiltonian $H_P$, with the evolution

$H(t) = (1-f(t)) H_I + f(t) H_P$

By encoding costs as diagonal entries and constraints via penalty terms, quantum annealers efficiently explore high-dimensional, rugged energy landscapes, providing faster convergence for NP-hard problems relative to classical approaches (Heidari et al., 2023, Meli et al., 8 Oct 2025). Hybrid schemes with D-Wave or similar platforms demonstrate competitive or superior energy minimization in fundamental tasks such as stereo matching (Heidari et al., 2023), industrial defect detection (QBoost ensemble learning) (Guijo et al., 2022), and graph matching.

4. Hybrid Quantum–Classical Deep Learning Architectures

Quantum modules are often embedded within classical neural networks to form hybrid architectures. The hybrid quantum vision transformer (HQViT) encodes the entire image via amplitude encoding and offloads the computation of self-attention matrices to quantum circuits, achieving reductions in classical compute scaling as $O(T^2d)$ (Zhang et al., 3 Apr 2025). Quantum Vision Transformer (QViT) uses modular quantum linear algebra and arithmetic units, with inter-layer communication managed by Discrete Chebyshev Decomposition (DCD) for compressing quantum states and enabling quantum residual connections (Xue et al., 29 Feb 2024).

Quantum convolutional neural networks (QCNNs), implemented in underwater robotics, leverage quantum feature extraction and flattening, enabling comparable classification efficiency with reduced run-time and smaller dataset requirements (Warrier et al., 19 Apr 2024). Variational quantum circuits can be integrated into the channel-mixing components of architectures such as RWKV, providing enhanced nonlinear representation and superior performance on datasets characterized by noisy or subtle class boundaries (Chen, 7 Jun 2025).

5. Computational Efficiency, Scalability, and Robustness

Quantum algorithms offer distinct advantages in computational efficiency and scalability for computer vision:

• Matrix operations, feature extraction, and kernel evaluations can be performed with theoretical exponential or at least polynomial speedup via quantum linear algebra routines. For instance, quantum forward propagation of deep networks achieves complexity $O(n(n+b))$ as opposed to $O(bn^2)$ classically (Parthasarathy et al., 2020).
• Memory requirements scale logarithmically with input size; for a deep network with 10 layers and batch size 200, quantum regimes dramatically lower parameter count and energy consumption (from the picojoule to attojoule regime).
• Quantum methods exhibit robust generalization (distributed error profiles in confusion matrices), high Matthews Correlation Coefficient (MCC $\sim 0.97$), and ROC curves approaching ideal rectangular shapes, indicating stable decision boundaries (Parthasarathy et al., 2020).
• Empirical studies on NISQ hardware, such as IBM’s ibmq_paris, demonstrate parity in classification performance between quantum and classical networks, given appropriate circuit recompilation and depth reduction strategies (Jaderberg et al., 2021).

These advantages position quantum-enhanced models as candidates for large-scale, real-time computer vision applications—autonomous driving, medical imaging, industrial quality control—where data volume and the cost of computation represent major bottlenecks.

6. Algorithmic Challenges and Information Preservation

Despite their promise, quantum encoding pipelines can induce information loss—termed the Quantum Information Gap (QIG)—manifesting as a mismatch between classical feature similarity (e.g., cosine similarity) and quantum measurement outcomes. This loss impairs discriminative power and clustering. Quantum Information Preserving (QIP) loss functions mitigate QIG by penalizing the divergence between classical and post-quantum similarities during training, yielding superior clustering and classification accuracy in vision tasks (Nguyen et al., 30 May 2024).

Variational quantum circuits must cope with hardware-induced noise (decoherence, amplitude damping) and training pathologies (barren plateaus). Related works deploy error mitigation techniques, shallow circuit design, and noise modeling via Kraus operators to address NISQ limitations (Mandadapu, 1 Apr 2024). Choices in encoding, measurement observable, and loss construction are central to retaining the structure of visual features.

7. Applications, Open Problems, and Research Resources

Quantum-enhanced computer vision is demonstrated across domains including:

• Defect detection in manufacturing (QSVM, QBoost ensemble learning) (Guijo et al., 2022).
• Stereo matching, segmentation, and labeling via energy minimization and annealing (Heidari et al., 2023, Meli et al., 8 Oct 2025).
• Real-time underwater image classification on autonomous vehicles (Warrier et al., 19 Apr 2024).
• Clustering of high-dimensional visual features (faces, landmarks) with QIP loss (Nguyen et al., 30 May 2024).
• Optical imaging under weak-signal conditions, with quantum schemes surpassing classical SNR limits (Mokeev et al., 11 Sep 2025).
• Implicit neural representations for 2D/3D field completion and 3D shape interpolation using energy-manifold driven neural amplitude encoding (Wang et al., 14 Aug 2025).

Open challenges include data encoding at scale, hybrid algorithm integration, hardware noise, reproducibility, scalability, and the development of novel quantum-compatible methodologies in learning and optimization (Meli et al., 8 Oct 2025). Recommended tools for simulation and experimentation include IBM Qiskit, Google Cirq, PennyLane, D-Wave Ocean, and NVIDIA cuQuantum SDKs. Educational resources range from canonical texts (Nielsen & Chuang, Das & Chakrabarti) to online platforms (IBM Quantum Learning, D-Wave Leap) and QCVML workshops.

QeCV continues to develop quantum-native algorithms and hybrid pipelines, aiming for advances not only in computational efficiency and accuracy but also in resource scaling and energy consumption. The field is thus poised at the interface of quantum hardware evolution, algorithmic innovation, and real-world computer vision applications.