Explore our quantum machine learning library

Power your quantum computing journey with Ingenii's open-source Quantum Machine Learning Algorithm Library. Built by our community for our community. Includes quantum and hybrid algorithms that are already packaged in python, ready to be implemented, and capable of running on any quantum hardware.

the algos

Hybrid quantum-classical 2D/3D CNN

Designed to reduce complexity of the classical CNN, while maintaining its prediction performance.

Hybrid quantum-classical QNN

The quantum NN replaces the last fully connected layers of the network, instead of the initial convolutional layers.

Quantum Hadamard edge detection (QHED)

QHED algorithm for 2D images and 3D volumes.

A better neural network convergence than classical fusion models that provides ~5% improvement in prediction performance​.

Quantum fusion

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ADVANTAGES

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.

Potential use cases

  • Object detection or image matching for early detection of diseases

  • Detection and diagnosis of oncological images

  • Molecular simulation of electronic properties and chemical reactions

  • Detecting outliers in clinical data 

  • Enhancing satellite imaging

  • Climate change sustainability coordination

Have another use case? Add it to our Roadmap.

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