Re-imagining Intelligence with Nature and Quantum-Inspired Algorithms
Heisenberg Quantum AI redefines intelligence by blending nature-inspired principles with quantum-driven algorithms.
A++ Performance
26+
Ongoing Projects
4+
Years of Experience
5+
Quantum-AI Solutions
Our Services
Quantum Innovation, Real Solutions
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Innovation in Field of ML,AI and QiML with Heisenberg Quantum AI
At Heisenberg Quantum AI, we integrate quantum mechanics principles with classical computing to enhance your existing machine learning models.
Quantum Algorithm Development
Design and simulation of quantum algorithms using formal frameworks like Heisenberg, Schrödinger, and tensor networks.
AI Interpretability & Optimization
Making neural networks interpretable using novel mathematical methods and optimizing AI systems using quantum-inspired techniques.
Generative AI & Data Encoding
Building generative models using quantum-inspired architectures and developing quantum encoding for structured learning.
Quantum Systems Simulation
Simulation of quantum and hybrid systems for real-world applications like material science, quantum gravity, and renewable energy.
About Us
Shaping AI with Quantum and Nature-Inspired Innovation
Heisenberg Quantum AI startup Aims at providing Quantum Inspired and Classical ML and NN based interpretable and Simulation Solutions to each and every Problem of the World that is Computationally Modellable.
- Innovative Solutions
- Bridging Theory & Practice
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Our Projects
Quantum Inspired Machine Learning For Classification of Classical Dataset
Linear Feature Extraction of given Dataset to make them Quantum Circuit Seperable For various infinitely possible countable Quantum Encodings.
Quantum Algorithms for Classification
Quantum Singular Value Transformation Various Architechtures – Unified Quantum Algorithms Framework that can simulate any other Quantum Algorithms
Various Quantum Formalisms for Classification
Heisenberg Formalism – That is where the Name of Startup Originated from , Schrodinger Formalism , And Various other formalisms of Quantum Mechanics…
Quantum Versions of Classical ML and NN architectures
Quantum Versions of Classical ML and NN architectures (Not conventional QNN’s) for Classification using Quantum Activation or Lagrange Multipliers.
Adiabatic and Non-Adiabatic Quantum Computing
Adiabatic and Non-Adiabatic Quantum Computing Gate Based Algorithms for Classification as well as Parameterizing the dataset with Quantum Systems for Generative AI Applications (Perturbation Theory , Adiabatic Theorem etc)
Classical and Quantum Search Based Algorithms for Board Games and QiRL
This project explores the integration of classical and quantum search algorithms to enhance decision-making strategies in board games.
Quantum Inspired Generative Adversarial Networks for Generative AI.
Quantum-Inspired Generative Adversarial Networks (QiGANs) to enhance the capabilities of generative AI.
Interpretability of Neural Networks
Finding the Exact Functions of Dataset or Neural Networks – Paper Link Using the Novel Max-min and Min--Max Approach discussed in our paper.
Quantum Inspired Machine Learning and NN approximate solution
Quantum Inspired Machine Learning and NN approximate solution to Goldbeck Conjecture of decomposing the sum of even number greater than 2 in sum of two prime numbers
Quantum Inspired Machine Learning for Representation Learning
This project explores the application of quantum-inspired machine learning techniques to enhance representation learning in Graph Neural Networks (GNNs).
Weather Forecasting for QiML Applications
This project focuses on utilizing Quantum-Inspired Machine Learning (QiML) techniques for accurate and efficient weather forecasting.
Quantum Hopfield Networks and Boltzmann Machines for Classification
Develop quantum analogs of Hopfield Networks and Boltzmann Machines to enhance pattern recognition and classification tasks, leveraging quantum superposition and entanglement for improved performance.
Bridging Search-Based AI and Conventional Neural Networks
Integrate search-based AI methodologies with traditional neural networks to create hybrid models that combine the strategic depth of search algorithms with the learning capabilities of neural networks.
Quantum-Inspired Multimodal Encodings for QiML Approaches
Develop quantum-inspired techniques for encoding and integrating data from multiple modalities (e.g., text, image, audio) to improve the performance of machine learning models in complex tasks.
Quantum-Inspired Optimization Algorithms for Game Theory and Neural Networks
Design optimization algorithms inspired by quantum computing principles to solve min-max and max-min problems in game theory and enhance training processes in neural networks.
Quantum Gravity Simulations
Simulate aspects of quantum gravity using quantum computing frameworks to explore and understand the unification of general relativity and quantum mechanics.
Quantum Circuit Finding by Transformers Architecture
Quantum Circuit Finding by Transformers Architecture – Potential First Applications of Fault Tolerant Quantum Computer of higher than classical compute limit scale.
Quantum Teleportation Faster Than Speed of Light
Investigate theoretical models and preprints that propose mechanisms for quantum teleportation potentially exceeding the speed of light, challenging conventional physics paradigms.
Parameterizing Classical Datasets with Quantum Systems for Generative AI
Quantum Systems for Parameterizing the classical datasets per class for Generative AI Applications – Feynman Quote – “Quantum Systems can Simulate Quantum Systems” , Quantum Encoded Classical Datasets => Quantum Systems whose Solution of Schrodinger Equations is Exactly Known.
Extracting Quantum Properties from Quantum-Encoded Classical Datasets
Extraction of Quantum Properties from Quantum Encoded Classical Datasets forming Quantum Systems.
Approximating Quantum Many-Body Problems via Hamiltonian Imaging
Quantum Many Body Problems Approximation by Hamiltonian Imaging and then Super-resolution.
Material Simulation Applications
Material Simulation Applications of , 1. Superconductivity at Higher Temperature and Pressure , 2. Negative Capacitances , 3. Renewable Energy Resources – Solar Panels Simulation , 4. Electric Battery Simulation
Conversion from Quantum Communication Photonic Based to Quantum Computing Gate Based Circuits
Conversion from Quantum Communication Photonic Based to Quantum Computing Gate Based Circuits
Quantum Data Compression
Develop and implement quantum data compression algorithms to efficiently store and transmit quantum information, optimizing resource usage in quantum computing systems.
Quantum Inspired Architectures for SLM and then LLM
Design quantum-inspired architectures to enhance the capabilities of Statistical Language Models (SLM) and Large Language Models (LLM), improving their performance in natural language processing tasks.
Analytical Solutions of NN , RL , and Polynomial of Higher than 4 degree.
Finding Analytical Solutions of NN dependent of dataset , as well as independent of dataset
Alternatives To Neural Networks for Function Approximations
Researching into Alternatives to Neural Networks for function Approximations for various applications of ML.
Why Choose Us
Why you should choose us?
Heisenberg Quantum AI stands at the forefront of innovation, blending quantum-inspired principles with nature-driven intelligence to tackle complex computational challenges. With deep research roots and a passion for interpretability, we create cutting-edge, custom AI models that bridge classical and quantum systems.
Quality Support
We provide dedicated, expert assistance to ensure seamless implementation and satisfaction.
Quantum Inspired Solutions
Our quantum-inspired AI models are engineered for lightning-fast processing and unmatched efficiency.
Deep Research & Interpretability
Every solution is built on rigorous research, offering transparency and deep insights into model behavior.
Questions & Answers
Answers to Your Quantum AI Questions
Explore solutions and insights to all your Quantum AI queries here.
Contact usHeisenberg Quantum AI is a pioneering company that specializes in quantum-inspired machine learning and computational models. We develop advanced AI algorithms that bridge the gap between quantum theory and real-world AI applications.
Quantum-inspired algorithms leverage principles from quantum mechanics, such as superposition and entanglement, to enhance the efficiency and performance of classical AI models. These algorithms enable faster processing and more accurate results, particularly for complex datasets.
Our quantum-inspired AI solutions can benefit a wide range of industries, including finance, healthcare, data science, telecommunications, energy, and more. We offer customized solutions to address specific industry needs, driving innovation and efficiency.
Yes, we work with businesses and researchers to integrate quantum-inspired machine learning models into their existing systems. Our team provides consultation, support, and customized solutions to enhance the performance of your projects.
Absolutely! We are open to research collaborations with academic institutions, companies, and research organizations. Our goal is to push the boundaries of AI and quantum computing through joint efforts and innovative projects.
Research
Our Latest Research
Heisenberg Quantum AI develops nature-inspired, quantum-driven AI models for advanced computational problem-solving. We bridge quantum theory and real-world applications to shape the future of intelligent systems.