QML Library: Explore Quantum Machine Learning Resources & Tools

Power your quantum computing journey with Ingenii's open-source Quantum Machine Learning Algorithm Library

Python Code, Visualization Libraries & Step-by-Step Tutorials

Explore quantum states with State Space, Q-Sphere, Phase Disk, and Bloch Sphere representations.

Click below to download the Python package and gain access to the documentation.

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Explore Leading QML Algorithms for Real-World Applications

Hybrid Quantum-Classical 2D/3D CNN

It is designed to reduce the complexity of the classical 2D/3D CNN while maintaining its prediction performance. The hybrid CNN replaces a convolutional layer with a quantum convolutional layer.

Tensor Network Decomposition

One of the most effective tensor decompositions for compressing convolutional layers is the Tucker decomposition. This method breaks down the original four-dimensional weight tensor of a convolutional layer into multiple smaller tensors.

Hybrid Quantum-Classical QNN

Designed to construct hybrid quantum-classical neural networks, where the quantum layer is a fully-connected layer.

Quantum Optimization for Image Segmentation

Provides the graph mapping for image segmentation and the formulation as a QUBO problem. Many quantum and quantum-inspired algorithms, such as quantum annealing and QAOA, can then be used to find the optimal segmentation mask.

Quantum Hadamard Edge Detection (QHED)

Performs edge detection for 2D data (images) and 3D data (volumes) using quantum operations.

Quantum Neural Network Statistics

Provides metrics to evaluate the balance between performance and complexity in quantum neural networks, including Expressibility and Entangling Capacity.

Quantum Fusion

Designed to efficiently integrate the extracted features from two classical neural network models to produce enhanced predictions. The proposed model strategically integrates 3D-CNNs and SG-CNNs to leverage their respective strengths in processing diverse facets of the training data.

Quantum-Inspired Image Filter

Especially useful for highlighting regions with varying contrast and identifying regions of interest. This transformation adjusts pixel intensity based on its local contrast and overall neighborhood contribution, enhancing segmentation by emphasizing boundaries and transitions in the image.

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How QML is Transforming Biotech, Pharma & Clinical Trials

We’re already working with collegiate and research institutions to make a difference in science fields.

Detection and diagnosis of oncological images

Object detection or image matching for early detection of diseases

Molecular simulation of electronic properties and chemical reactions

Detecting outliers in clinical data 

Enhancing satellite imaging

Explore our case studies

Advantages of Quantum Neural Networks

Quantum neural networks (QNNs) represent the cutting edge of the intersection between quantum computing and deep learning. By combining the power of quantum computing with the flexibility and expressiveness of neural networks, they have the potential to revolutionize fields such as quantum chemistry, materials science, and optimization. Quantum neural networks offer enhanced representation of quantum interactions, improved prediction accuracy, faster data processing, and the ability to tackle computationally intractable problems. These networks hold the promise of unlocking new frontiers in machine learning and enabling breakthroughs in solving complex scientific challenges.

  • Quantum neural networks capture the intricate quantum interactions that play a crucial role in various scientific and computational problems. By incorporating quantum principles into the network architecture, such as superposition and entanglement, quantum neural networks provide a more accurate representation of complex quantum phenomena, enabling better modeling and prediction capabilities.

  • Quantum neural networks offer the potential for quantum advantage in machine learning tasks. By leveraging quantum effects and quantum algorithms, these networks can outperform classical machine learning models in specific domains, providing faster training and inference times, improved accuracy, and the ability to tackle complex optimization problems more effectively.

  • Quantum neural networks show promise in solving computationally intractable problems that are beyond the reach of classical approaches. Tasks such as factorization, optimization, and simulation of quantum systems can be tackled more efficiently by exploiting the unique properties of quantum neural networks, opening up new avenues for solving complex problems in various scientific and technological domains.

  • Quantum neural networks enable quantum transfer learning, where knowledge gained from one quantum task can be transferred to another related task. This capability allows models to leverage previously learned quantum representations and insights, accelerating the learning process for new tasks and optimizing resource utilization in quantum computing systems.

  • Quantum neural networks harness the computational power of quantum computers to accelerate data processing. Quantum computing's ability to perform parallel computations and handle exponentially large state spaces can significantly speed up tasks that require complex calculations, enabling faster data analysis and decision-making.

  • Quantum neural networks have the potential to surpass the prediction accuracy of classical neural networks, especially when applied to quantum-related tasks. By leveraging the power of quantum computing and deep learning, these networks can effectively capture the subtleties of quantum systems, leading to more precise predictions in areas such as quantum chemistry, material science, and quantum information processing.