Quantum-enhanced Computer Vision:
Going Beyond Classical Algorithms


1Institute for Vision and Graphics, University of Siegen, Germany.
2School of Computer and Mathematical Sciences, University of Adelaide, Australia.
3Department of Computing, Imperial College London, United Kingdom.
4Visual Computing and Artificial Intelligence, MPI fĂŒr Informatik, Germany.


ArXiv 2025

QeCV hybrid encoding pipeline

Quantum- enhanced Computer Vision (QeCV) is a research field at the intersection of quantum computing and computer vision, focused on developing innovative algorithms and techniques that leverage quantum paradigms to surpass classical methods in speed, efficiency, and accuracy.

This webpage accompanies our survey paper, providing a visual overview of QeCV pipelines, detailed concept explanations, and an interactive overview of the (non-exhaustive) published QeCV literature, complementing the theoretical discussion.

Quantum Computing: Conceptual Pipeline

Qubit
→
Multi-Qubits
→
Evolution
↗
↘
Gate-Based QC Discrete unitaries
Adiabatic QC Continuous Hamiltonian
↘
↗
Measurement

Click a concept above to explore its theory.

Qubit

The qubit is the fundamental unit of quantum information, represented by a normalized two-dimensional complex vector \(|\psi\rangle \in \mathbb{C}^2, \| |\psi\rangle \|_2 = 1\).

Mathematically, we can define two orthonormal basis vectors called computational states:
\(|0\rangle = \begin{bmatrix}1\\0\end{bmatrix}, \quad |1\rangle = \begin{bmatrix}0\\1\end{bmatrix}\),
and express a qubit as a linear combination of the basis vectors:
\(|\psi\rangle = \alpha |0\rangle + \beta |1\rangle, \quad \alpha, \beta \in \mathbb{C}, \quad |\alpha|^2 + |\beta|^2 = 1\).

In column vector form, this corresponds to:
\(|\psi\rangle = \begin{bmatrix}\alpha\\\beta\end{bmatrix} = \begin{bmatrix}a+ib\\c+id\end{bmatrix}, \quad a,b,c,d \in \mathbb{R}, \quad a^2+b^2+c^2+d^2=1\).

Geometric Interpretation: Bloch Sphere

Up to a global phase, a qubit state can be visualized on the Bloch sphere:
\(|\psi\rangle = \cos\frac{\theta}{2}|0\rangle + e^{i\phi}\sin\frac{\theta}{2}|1\rangle\).
Angles \(\theta\) and \(\phi\) specify the qubit's position on the sphere.

Bloch sphere representation of a qubit

Qubit Measurement

When a qubit \(|\psi\rangle = \alpha |0\rangle + \beta |1\rangle \in \mathbb{C}^2\) is measured in the computational basis \(\{|0\rangle, |1\rangle\}\), the state collapses to one of the basis vectors with probabilities determined by the amplitudes:

\(|0\rangle \text{ with probability } |\alpha|^2 = |\langle 0|\psi\rangle|^2,\)
\(|1\rangle \text{ with probability } |\beta|^2 = |\langle 1|\psi\rangle|^2\).

In other words, a qubit exists in a superposition of classical states before measurement. Upon measuring, it collapses probabilistically: \(|\psi\rangle = \alpha |0\rangle + \beta |1\rangle \to |0\rangle \text{ or } |1\rangle\) with probabilities \(|\alpha|^2\) and \(|\beta|^2\), respectively. This process is also called the collapse of the wave function.

Multi-Qubit Systems

Multi-qubit gates are unitary operators acting on \(\mathbb{C}^{2^n}\) and can generate entanglement by coupling qubits. Measurement generalizes directly: the probability of observing a specific computational basis state is given by the squared magnitude of its corresponding coefficient, regardless of whether the state is separable or entangled.

Visualizing on the Bloch Sphere

Measurement can be seen as projecting the qubit state \(|\psi\rangle\) onto the chosen axis (here the \(z\)-axis) and it collapses \(|0\rangle\) or \(|1\rangle\) with probability \(|\alpha|^2\) or \(|\beta|^2\). By repeating the measurement multiple times, we can compute the measurement expectation value, which correspond to the squared magnitudes of the projections of \(|\psi\rangle\) onto the measurement axis.

Qubit measurement projections on the Bloch sphere

Multi-Qubit Systems, Entanglement

When multiple qubits \(|\psi_1\rangle, \dots, |\psi_n\rangle\) are considered jointly, their combined state is represented by the tensor (Kronecker) product of the individual states:
\(|\psi\rangle = |\psi_1\rangle \otimes |\psi_2\rangle \otimes \cdots \otimes |\psi_n\rangle \in \mathbb{C}^{2^n}\).
Such a collection of qubits forms a quantum register. A convenient shorthand notation is \(|\psi_1\psi_2\cdots\psi_n\rangle\).

Two-Qubit Example

For two qubits \( |\psi_1\rangle = \alpha|0\rangle + \beta|1\rangle \) and \( |\psi_2\rangle = \gamma|0\rangle + \delta|1\rangle \), the joint state is
\(|\psi\rangle = |\psi_1\rangle \otimes |\psi_2\rangle = \begin{bmatrix} \alpha\gamma \\ \alpha\delta \\ \beta\gamma \\ \beta\delta \end{bmatrix}\).
Product states of this form are called separable; they correspond to rank-1 tensors and can be decomposed into individual qubit states.

Entanglement

Multi-qubit systems are not restricted to separable states. A state that cannot be written as a tensor product of single-qubit states is called entangled. A canonical example is the two-qubit Bell (EPR) state:
\( |\psi\rangle = \frac{1}{\sqrt{2}} (|01\rangle + |10\rangle) = \frac{1}{\sqrt{2}} \begin{bmatrix} 0 \\ 1 \\ 1 \\ 0 \end{bmatrix}\).
No coefficients \(\alpha,\beta,\gamma,\delta\) exist such that this state can be factorized into \( |\psi_1\rangle \otimes |\psi_2\rangle \); hence it is genuinely entangled.

Quantum State Evolution

The state of an \(n\)-qubit quantum system can be actively manipulated over time using controlled external interactions. Let \(|\psi(0)\rangle\) denote the initial state of the system. Its time evolution is governed by the system Hamiltonian \(H(t) \in \mathbb{C}^{2^n \times 2^n}\), which encodes the energies and couplings induced by the experimental setup.

Schrödinger Equation

The time evolution of the quantum state \(|\psi(t)\rangle\) is described by the Schrödinger equation:

\(i\hbar \frac{d}{dt} |\psi(t)\rangle = H(t)\,|\psi(t)\rangle\).

Here, \(i\) denotes the imaginary unit, \(\hbar\) is the reduced Planck constant, and the Hamiltonian \(H(t)\) is a Hermitian operator acting on the \(2^n\)-dimensional Hilbert space.

Hamiltonian

Put simply, and in analogy to classical computing, a Hamiltonian can be viewed as an energy function—a mathematical expression describing how energy is distributed across a quantum system. A time-dependent Hamiltonian defines an evolving energy landscape that governs the system’s dynamics through the Schrödinger equation.
The specific structure of the Hamiltonian, together with how this evolution is realized or approximated in time, fundamentally determines the quantum computing paradigm. In gate-based quantum computing, evolution is discretized into sequences of unitary operations (quantum gates), whereas in adiabatic quantum computing (AQC) the system is steered continuously by slowly varying the Hamiltonian toward the ground state encoding the solution.

Gate-Based Quantum Computing (GQC)

Following the Schrödinger evolution of a quantum system, one of the primary ways to manipulate qubits is via quantum gates. These gates are unitary operators applied to one or multiple qubits, defining the discrete-time evolution of the system. Quantum circuits are sequences of such gates that perform computations analogous to classical logical circuits, but in a reversible and linear-algebraic manner.

Single-Qubit Gates

Single-qubit gates act on a single qubit |ψ⟩. Common examples include the Pauli gates:
\(X = \begin{pmatrix}0 & 1\\1 & 0\end{pmatrix},\; Y = \begin{pmatrix}0 & -i\\ i & 0\end{pmatrix},\; Z = \begin{pmatrix}1 & 0\\ 0 & -1\end{pmatrix}\).

For a qubit state \(|\psi\rangle = \alpha |0\rangle + \beta |1\rangle = \begin{pmatrix}\alpha \\ \beta \end{pmatrix}\), applying X yields:
\(X|\psi\rangle = \beta |0\rangle + \alpha |1\rangle\).
This swaps the amplitudes of the basis states.

The Hadamard gate \(H = \frac{1}{\sqrt{2}}\begin{pmatrix}1 & 1\\ 1 & -1\end{pmatrix}\) creates superposition:
\(H|0\rangle = \frac{|0\rangle + |1\rangle}{\sqrt{2}}, \quad H|1\rangle = \frac{|0\rangle - |1\rangle}{\sqrt{2}}\).

Multi-Qubit Gates

For n qubits, single-qubit gates are tensored to act on the full system. For example, the 2-qubit Hadamard gate is:
\(H^{\otimes 2} = H \otimes H = \frac{1}{2} \begin{pmatrix}1 & 1 & 1 & 1\\ 1 & -1 & 1 & -1\\ 1 & 1 & -1 & -1\\ 1 & -1 & -1 & 1\end{pmatrix}\).
Sequential application of \(H^\otimes2\) twice yields the identity operation \(I_4\).

Controlled gates generate entanglement. Example: the controlled-NOT (CNOT) gate:
\(\text{CNOT} = \begin{pmatrix} 1 & 0 & 0 & 0\\ 0 & 1 & 0 & 0\\ 0 & 0 & 0 & 1\\ 0 & 0 & 1 & 0 \end{pmatrix}, \quad \text{CNOT}|10\rangle = |11\rangle\).

Parameterized Gates and Variational Circuits

Parameterized gates are unitary rotations that depend on a real-valued parameter Ξ:

\(U(\theta) = e^{i \theta G} = \cos(\theta) I + i \sin(\theta) G\).

where G is Hermitian and generates rotations around a Bloch sphere axis. Parameterized Quantum Circuits (PQC) use such gates to prepare states \(|ψ(Ξ)\rangle = U(Ξ)|0\rangle\), optimizing \(\theta\) to minimize a cost function \(\langle \psi(\theta)|M|\psi(\theta)\rangle\).

This approach underpins variational quantum algorithms and Quantum Machine Learning.

Adiabatic Quantum Computing (AQC) & Quantum Annealing

Unlike gate-based QC, AQC evolves a quantum system continuously under a time-dependent Hamiltonian \(H(t)\). The system is prepared in the ground state of an initial Hamiltonian \(H_I\) and evolves slowly to a problem Hamiltonian \(H_P\), encoding the solution to an optimization problem.

Problem Hamiltonian

The typical problem Hamiltonian is an Ising-type Hamiltonian:
\(H_P = \sum_{i,j} J_{i,j} \sigma^z_i \sigma^z_j + \sum_i b_i \sigma^z_i\)
with \(\sigma^z_i\) the Pauli-Z operator on qubit \(i\) embedded in the \(n\)-qubit space:
\(\sigma^z_i = I \otimes \dots \otimes I \otimes \sigma^z \otimes I \otimes \dots \otimes I,\quad \sigma^z = \begin{pmatrix}1 & 0\\ 0 & -1\end{pmatrix},\; I = \begin{pmatrix}1 & 0\\0 & 1\end{pmatrix}\).

The ground state of \(H_P\) corresponds to the solution of the Ising problem:
\(\min_{s \in \{-1,+1\}^n} s^\top J s + s^\top b\)
or equivalently the QUBO formulation:
\(\min_{x \in \{0,1\}^n} x^\top Q x + x^\top c\).

Adiabatic Evolution

The system evolves under a time-dependent Hamiltonian:

\(H(t) = (1-f(t)) H_I + f(t) H_P,\quad f: [0,T] \to [0,1]\).

Starting in the ground state of \(H_I\), a sufficiently slow evolution ensures the system remains in the instantaneous ground state of \(H(t)\) until \(H_P\), according to the Quantum Adiabatic Theorem.

The state can be expanded in the eigenbasis of \(H(t)\):
\(|\psi(t)\rangle = \sum_k c_k(t) |k(t)\rangle, \quad H(t)|k(t)\rangle = \lambda_k(t)|k(t)\rangle\).

Initial Hamiltonian & Spectral Gap

A common choice for the initial Hamiltonian is:
\(H_I = - \sum_i \kappa \sigma^x_i, \quad \sigma^x = \begin{pmatrix}0 & 1\\1 & 0\end{pmatrix}\).
The minimum energy gap between the ground and first excited state during evolution—the spectral gap—governs the required adiabatic runtime \(T\).

For non-degenerate ground states, the gap remains positive, ensuring the adiabatic theorem holds.

Quantum Annealing & Simulated Analogues

Quantum annealers implement AQC physically, possibly non-adiabatically, returning the ground state with some probability. Repeated runs improve the likelihood of finding the optimal solution.

Classical analogues include simulated annealing:
\(P = \min \Big(1, \exp\Big(\frac{E(x^{(i-1)}) - E(x^{(i)}_\text{candidate})}{T_i}\Big)\Big)\).
Simulated quantum annealing extends this by sampling multiple states, simulating tunneling, superposition, and entanglement.

From CV Problems to Adiabatic Quantum Computing

Quantum-enhanced computer vision (QeCV) approaches based on quantum annealing follow a six-step pipeline from problem formulation to solution interpretation. This pipeline can be executed in a single sweep or iteratively, updating and re-annealing the QUBO until convergence.

The six key steps are:

  1. QUBO preparation: Formulate the vision task as a QUBO / Ising problem and compute its weights.
  2. Minor embedding: Map the logical QUBO graph to the quantum annealer hardware graph.
  3. Quantum annealing: Sample low-energy bitstrings through repeated annealing runs.
  4. Unembedding: Recover logical variables from measured physical qubits.
  5. Bitstring selection: Choose candidate solutions, typically those with lowest energies.
  6. Solution interpretation: Decode the selected bitstrings into solutions of the original CV problem.

The flowchart below visualizes this process.
Hover over each element to see in-depth descriptions, mathematical details, and typical methods used at each step.

CV Objective
↓
Binary Encoding x ∈ {0,1}ⁿ, s ∈ {−1,1}ⁿ
↓
QUBO / Ising Formulation
↓
Constraint Handling Penalties · Slack · Hard constraints
↓
Quadratisation (if needed) Ancilla variables / term-wise / global reductions
↓
(ii) Minor Embedding
Map logical QUBO graph → hardware graph Minor embedding (Cai et al., 2014)
↓
(iii) Quantum Annealing
Sample low-energy bitstrings Hundreds–thousands of annealing shots, each ~20”s
↓
(iv–vi) Post-processing
Unembedding · Bitstring Selection · Solution Interpretation
↓
Iterative Variants Update QUBO → re-embed → re-anneal → aggregate

Illustration

QeCV hybrid encoding pipeline

From CV Problems to Gate-based Quantum Computing

Gate-based quantum computing requires classical data to be encoded as quantum states before processing with quantum circuits. Quantum-enhanced computer vision (QeCV) approaches typically follow a workflow of four steps:

  1. Data Encoding: Transform classical data vectors into quantum states.
  2. Quantum Circuit Construction: Build parameterized quantum circuits (PQC) to process encoded data.
  3. Measurement: Measure qubits to obtain classical outcomes.
  4. Classical Post-processing: Aggregate and interpret measurement results in the context of the original task.

The flowchart below visualizes this process.
Hover over each element to see in-depth descriptions, mathematical details, and typical methods used at each step.

(i) Data Preprocessing
Normalize & prepare classical data x ∈ ℝᔐ Ready for quantum encoding
↓
(ii) Quantum Data Encoding
Encode classical data → qubit state |ψ(x)⟩ Basis, angular, amplitude, or higher-order encoding
↓
(iii) Circuit / Ansatz Construction
Build parameterized quantum circuit (PQC) Entanglement & variational parameters applied
↓
(iv) Quantum Processing
Apply circuit to |ψ(x)⟩ Unitary evolution prepares data for measurement
↓
(v) Measurement
Measure qubits → classical bitstrings Multiple shots estimate probabilities
↓
(vi) Classical Post-processing
Interpret measurements → CV output Optional iterative PQC updates

Illustration

QeCV hybrid encoding pipeline

Overview of CV problems solved by Quantum Computing

The table lists some notable quantum-enhanced computer vision (QeCV) approaches in the literature. It categorises methods based on: the quantum hardware used (adiabatic (AQC) or gate-based (GQC)), encoding strategy, problem type, and classical/quantum hybrid techniques.

Filter the table to focus on specific hardware types, encoding methods, or problem classes.

Method Paradigm Problem Input type Problem size # Qubits
QA, CVPR’20 AQC Transformation estimation, Point set alignment Point clouds ≀ 5k points ~140
IQT, CVPR’22 AQC Transformation estimation Point clouds ≀ 1.5k points ~15
QuAnt, ICLR’23 AQC Transformation estimation, Point set alignment, Mesh alignment Point clouds, meshes ≀ 2k points, 5 mesh vertices ~15
QuCOOP, CVPR’25 AQC Point set alignment, Mesh alignment Point clouds, meshes ≀ 50 points, 502 mesh vertices ~36
QGM, 3DV’20 AQC Graph matching Graphs ≀ 4 graph nodes ~50
Q-Match, ICCV’21 AQC Mesh alignment Meshes ≀ 502 mesh vertices ~250
CcuantuMM, CVPR’23 AQC Mesh alignment Meshes ≀ 1k mesh vertices ~40
QSync, CVPR’21 AQC Permutation synchronisation, Graph matching Permutation matrices ≀ 3×3 permutation matrices, 8 views ~72
QSQS, ECCV’20 AQC Object detection Bounding boxes ≀ 45 bounding boxes ~45
QMOT, CVPR’22 AQC Object tracking Tracks, detections ≀ 3 tracks, 5 frames ~100
Doan et al., CVPR’22 AQC Robust fitting Data points ≀ 100 points ~100
DeQUMF, CVPR’23 AQC Multi-model fitting Data points ≀ 1k models, 250 points ~100
Pandey et al., CVPRW’25 AQC Robust multi-model fitting Data points ≀ 2k models, 10k points ~120
Bauckhage et al., LWDA’18 AQC k-means clustering Data points ≀ 16 points ~16
Arthur and Date, QIP’21 AQC Balanced k-means clustering Data points ≀ 21 points, k = 3 ~64
Nguyen et al., ArXiv’23 AQC k-means clustering Feature vectors - -
Zaech et al., CVPR’24 AQC Balanced k-means clustering Data points ≀ 45 points, k = 3 ~45
Choong et al., CVPR’23 AQC Single image super-resolution Images, dictionary 15×20 LR → 45×60 HR ~100
QMSVM, IEEE’23 AQC Multi-class support vector machines Feature vectors ≀ 60 vectors, 3 classes ~360
Zardini et al., ArXiv’24 AQC Multi-class support vector machines Feature vectors ≀ 24 vectors, 3 classes ~144
Q-Seg, IEEE’24 AQC Unsupervised image segmentation Image patches ≀ 32×32 images -
QuMoSeg, ECCV’22 AQC Motion segmentation Landmark points ≀ 16 landmarks, 2 motions ~128
Santos et al., MDPI’18 AQC Stereo matching Stereo images ≀ 15×15 image patches -
Heidari et al., IVCNZ’21 AQC Stereo matching Stereo images ≀ 383×434 image patches -
Braunstein et al., 3DV’24 AQC Stereo matching Stereo images - -
Hur et al., QMI’22 GQC Binary image classification Fashion-MNIST ≀ length-32 feature vectors 8
QDCNN, IOP’20 GQC Multi-class image classification MNIST, GTSRB ≀ 32×32 images -
sQCNN-3D, Elsevier’23 GQC Multi-class point cloud classification ModelNet, ShapeNet ≀ 32×32×32 voxel grids 4
HQNN-Parallel, IOP’24 GQC Multi-class image classification Medical MNIST, CIFAR ≀ {64, 28, 32}ÂČ images 5
ATP, CVPR’25 GQC Multi-class image classification Fashion-MNIST, CIFAR ≀ {64, 28, 32}ÂČ images -
Chin et al., ACCV’20 GQC Robust fitting Data points - -
Yang et al., ECCV’24 GQC Robust fitting Data points ≀ 4 points 19
3D-QAE, BMVC’23 GQC Point cloud auto-encoding Point clouds ≀ 16 points 6
MosaiQ, ICCV’23 GQC Image generation Fashion-MNIST ≀ length-10 noise vectors 5
Huang et al., APS’23 GQC Image generation Handwritten digits ≀ length-32 latent vectors 6
Kolle et al., ArXiv’24 GQC Denoising diffusion model Fashion-MNIST, CIFAR ≀ 32×32 images 7
Piatkowski, ArXiv’22 GQC Bundle adjustment Sets of images ≀ 32×32 image patches -
QIREN, ICML’24 GQC Neural fields, implicit neural representations Coordinates ≀ 64×64 images 6
QVF, ArXiv’25 GQC Neural fields, implicit neural representations Coordinates Images, 3D shapes 6

Citation

@article{meli2025quantum,
  title={Quantum-enhanced Computer Vision: Going Beyond Classical Algorithms},
  author={Meli, Natacha Kuete and Wang, Shuteng and Benkner, Marcel Seelbach and Sasdelli, Michele and Chin, Tat-Jun and Birdal, Tolga and Moeller, Michael and Golyanik, Vladislav},
  journal={arXiv preprint arXiv:2510.07317},
  year={2025}
}

Contact

Natacha Kuete Meli
natacha.kuetemeli@uni-siegen.de
Vladislav Golyanik
golyanik@mpi-inf.mpg.de