TQml: Democratizing Advanced Quantum ML
Terra Quantum's TQml combines advanced quantum architectures and encoding techniques with classical ML, enabling you to build high-performance hybrid quantum models. Leverage its friendly interface to gain deeper insights, improve accuracy, and tackle complex problems across various domains.
Why TQml?
Superior Performance
Achieve higher accuracy and faster convergence, outperforming classical machine learning models.
Data Efficiency
Extract key patterns from smaller datasets, reducing data acquisition and preparation costs while accelerating insights.
Scalability by Design
Our hybrid approach offers quantum utility today, transitioning smoothly to future quantum hardware.
Unmatched Versatility
Solve diverse problems, from time series prediction to enhanced image processing, physics-driven simulation and generative AI.
TQml is a complete ecosystem built to transform your machine learning workflows with quantum-enhanced tools that work seamlessly together.
Key Features
Cutting-edge Proprietary Quantum ML Framework
Leverage Terra Quantum's world-class R&D to unlock powerful capabilities. Our proprietary framework provides you with state-of-the-art tools that enhance your ML models.
Flexible Architecture
Customize your models to suit your specific challenges. TQml offers a modular design, allowing you to fuse quantum and classical layers into the most effective solutions.
Scalable Performance
Start with existing hardware and transition smoothly to quantum processors as the technology evolves. Our hybrid approach ensures that your models are future-proof.
Intuitive Interface
Build complex quantum-classical hybrid models with ease using our user-friendly interface. TQml simplifies the development process, reducing the learning curve.
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Enhanced Accuracy
Capture complex patterns beyond classical model capabilities -
Improved Generalization
Learn effectively from smaller datasets, reducing data collection and preparation costs -
Faster Convergence
Reach optimal performance with fewer training iterations -
Efficient Handling of High-Dimensional Data
Process complex data structures effectively -
Futureproofing
Seamlessly adapt to advancing quantum hardware capabilities
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Generative AI
Enhance Your Models with Quantum Elements
- Superior Model Performance
Integrate our proprietary quantum components to boost your foundation models.
- Model Compression Without Compromise
Reduce model sizes while maintaining accuracy, lowering computational costs.
- Advanced Fine-Tuning with QRLoRA
Improve reasoning capabilities through quantum-enhanced fine-tuning with Quantum Low-Rank Adaptation.
- Consistent and Accurate Outputs
Overcome challenges like hallucinations using our ensemble approach with multiple state-of-the-art LLMs.
- Multilingual Customer Service Transformation
Deploy reliable, avatar-based agents capable of operating in any language.
- Quantum-Enhanced Drug Design
Achieve up to 30% improvement in drug-likeness, synthetic accessibility, and logP scores.
Key Applications:
Customer Service Agents and Chatbots
Intelligent Document Processing
Molecular Generation for Drug Design
Featured Publication:
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Time Series Analysis
Leveraging Hybrid Quantum Neural Networks to Achieve Highly Accurate Predictions
- Enhanced Pattern Recognition
Our HQNNs utilize the Hilbert space to recognize patterns more effectively with less data.
- Quantum Time Series Forecasting
Attain higher accuracy and speed in temporal data analysis using quantum algorithms.
- Real-Time Anomaly Detection
Quickly and accurately identify irregular patterns and outliers in large datasets.
- Optimized Decision-Making
Integrate time series predictions into holistic optimization models for better outcomes.
Key Applications:
Renewable Energy Forecasting
Predictive Maintenance
Financial Time Series Analysis
Weather Prediction Modeling
Featured Publications:
-
Visual Processing
Quantum-Enhanced Image Analysis: Revolutionize Image Processing with HQNNs
- Advanced Feature Extraction
Enhanced data encoding and quantum-assisted feature extraction for superior pattern recognition.
- Real-Time Segmentation and Classification
Achieve higher accuracy with fewer parameters—8x fewer than traditional models on the MNIST dataset.
- Learn from Limited Data
Our models generalize faster and perform better even with limited training data.
- Reduced Overfitting
Quantum-based regularization methods turn noise into a beneficial factor.
Key Applications:
Earth Observation and Satellite Imagery Analysis
Medical Imaging Diagnostics
Autonomous Vehicle Perception Systems
Manufacturing Quality Control
Security and Surveillance Systems
Featured Publications:
-
Physics-Driven Simulation
Accelerate Complex Simulations with Q-PINN and Efficiently Model Physical Processes
- Quantum Physics-Informed Neural Networks (Q-PINN)
Speed up simulations and optimizations for complex physical phenomena.
- Cross-Disciplinary Generalization
Neural networks offer more versatile capabilities than traditional modeling approaches.
Key Applications:
Industrial Process Optimization
Computational Fluid Dynamics (CFD)
Climate and Weather Modeling
Heat Transfer and Thermodynamic Simulation
Materials Science Research
Structural Analysis (Stress and Strain)
Featured Publications:
-
Planning and Optimization
Optimize Complex Scheduling and Routing with Quantum-Powered Decision Making
- Dynamic Decision-Making
Employ Hybrid Quantum Reinforcement Learning for real-time, optimized solutions.
- Handle Complex Challenges
Efficiently address multi-objective and multi-constraint problems in dynamic environments.
- Significant Performance Gains
Improved satellite mission planning cost functions by 70% on average in collaboration with Thales.
Key Applications:
Satellite Mission Planning
Supply Chain Optimization
Emergency Escape Routing
Operations Disruption Management
Featured Publications:
-
Fundamental QAI Research
Exploring New Frontiers in Quantum AI
- Revolutionizing AI and Computation
Exploring how quantum computers can transform AI and computational methods fundamentally.
- Necessity for New Algorithms
Alongside fault-tolerant hardware, new algorithms and techniques in quantum machine learning are essential to harness the full power of quantum computing for AI.
- Pushing Boundaries
Our scientists are researching across multiple challenges—both known and unknown—to expand the possibilities of quantum AI.
Research Focus Areas:
Enhancing Foundation Models with Quantum Technologies
Novel Hybrid Quantum Machine Learning (QML) Architectures
Efficient Quantum-Classical Data Encoding and Decoding
Addressing the Barren Plateau Problem
Quantum Processing Unit (QPU) Embedding
Featured Publications:
Enhance Your Models with Quantum Elements
- Superior Model Performance
Integrate our proprietary quantum components to boost your foundation models.
- Model Compression Without Compromise
Reduce model sizes while maintaining accuracy, lowering computational costs.
- Advanced Fine-Tuning with QRLoRA
Improve reasoning capabilities through quantum-enhanced fine-tuning with Quantum Low-Rank Adaptation.
- Consistent and Accurate Outputs
Overcome challenges like hallucinations using our ensemble approach with multiple state-of-the-art LLMs.
- Multilingual Customer Service Transformation
Deploy reliable, avatar-based agents capable of operating in any language.
- Quantum-Enhanced Drug Design
Achieve up to 30% improvement in drug-likeness, synthetic accessibility, and logP scores.
Key Applications:
Customer Service Agents and Chatbots
Intelligent Document Processing
Molecular Generation for Drug Design
Featured Publication:
Leveraging Hybrid Quantum Neural Networks to Achieve Highly Accurate Predictions
- Enhanced Pattern Recognition
Our HQNNs utilize the Hilbert space to recognize patterns more effectively with less data.
- Quantum Time Series Forecasting
Attain higher accuracy and speed in temporal data analysis using quantum algorithms.
- Real-Time Anomaly Detection
Quickly and accurately identify irregular patterns and outliers in large datasets.
- Optimized Decision-Making
Integrate time series predictions into holistic optimization models for better outcomes.
Key Applications:
Renewable Energy Forecasting
Predictive Maintenance
Financial Time Series Analysis
Weather Prediction Modeling
Featured Publications:
Quantum-Enhanced Image Analysis: Revolutionize Image Processing with HQNNs
- Advanced Feature Extraction
Enhanced data encoding and quantum-assisted feature extraction for superior pattern recognition.
- Real-Time Segmentation and Classification
Achieve higher accuracy with fewer parameters—8x fewer than traditional models on the MNIST dataset.
- Learn from Limited Data
Our models generalize faster and perform better even with limited training data.
- Reduced Overfitting
Quantum-based regularization methods turn noise into a beneficial factor.
Key Applications:
Earth Observation and Satellite Imagery Analysis
Medical Imaging Diagnostics
Autonomous Vehicle Perception Systems
Manufacturing Quality Control
Security and Surveillance Systems
Featured Publications:
Accelerate Complex Simulations with Q-PINN and Efficiently Model Physical Processes
- Quantum Physics-Informed Neural Networks (Q-PINN)
Speed up simulations and optimizations for complex physical phenomena.
- Cross-Disciplinary Generalization
Neural networks offer more versatile capabilities than traditional modeling approaches.
Key Applications:
Industrial Process Optimization
Computational Fluid Dynamics (CFD)
Climate and Weather Modeling
Heat Transfer and Thermodynamic Simulation
Materials Science Research
Structural Analysis (Stress and Strain)
Featured Publications:
Optimize Complex Scheduling and Routing with Quantum-Powered Decision Making
- Dynamic Decision-Making
Employ Hybrid Quantum Reinforcement Learning for real-time, optimized solutions.
- Handle Complex Challenges
Efficiently address multi-objective and multi-constraint problems in dynamic environments.
- Significant Performance Gains
Improved satellite mission planning cost functions by 70% on average in collaboration with Thales.
Key Applications:
Satellite Mission Planning
Supply Chain Optimization
Emergency Escape Routing
Operations Disruption Management
Featured Publications:
Exploring New Frontiers in Quantum AI
- Revolutionizing AI and Computation
Exploring how quantum computers can transform AI and computational methods fundamentally.
- Necessity for New Algorithms
Alongside fault-tolerant hardware, new algorithms and techniques in quantum machine learning are essential to harness the full power of quantum computing for AI.
- Pushing Boundaries
Our scientists are researching across multiple challenges—both known and unknown—to expand the possibilities of quantum AI.
Research Focus Areas:
Enhancing Foundation Models with Quantum Technologies
Novel Hybrid Quantum Machine Learning (QML) Architectures
Efficient Quantum-Classical Data Encoding and Decoding
Addressing the Barren Plateau Problem
Quantum Processing Unit (QPU) Embedding
Featured Publications:
Challenge
A leading European energy provider collaborated with Terra Quantum to enhance the accuracy of their photovoltaic (solar) power output forecasts. Precise forecasting is critical for optimizing grid management, balancing supply and demand, and supporting the integration of renewable energy sources.
Solution
We implemented a TQml-powered Hybrid Quantum Long Short-Term Memory (HQLSTM) model to predict steam mass flow in a biomass power plant.
Results
- 40% Improvement in Forecasting Accuracy: The HQNN model significantly enhanced the prediction reliability of photovoltaic power output compared to previous models.
- 5.7x Reduction in Mean Squared Error: Achieved substantially lower prediction errors compared to the existing production model.
- Enhanced Grid Efficiency: Improved alignment of energy production with consumption patterns, reducing reliance on non-renewable backups.
Impact
- Operational Cost Savings: Reduced need for expensive fossil fuel-based energy sources.
- Environmental Benefits: Lower carbon emissions, supporting the company's sustainability goals.
- Strategic Advantage: Positioned the energy provider at the forefront of technological innovation in renewable energy forecasting.
Read the publication for a detailed presentation of our approach: Forecasting The Steam Mass Flow in a Powerplant Using The Parallel Hybrid Network
Dive Deeper with Our Quantum ML Research Publications
Dr. Alexey Melnikov
Global Director of Artificial Intelligence
• Get started with the TQml toolbox in a user-friendly interface for learning, model building and experimentation
• For production implementations, use TQ42’s Python SDK for secure, cloud-based collaboration and deployment
• Training and support are available to get you up to speed quickly
Quantum-Enhanced Customer Avatars
• Access our cutting-edge quantum-enhanced generative AI for customer service
• Implement hyper-realistic, 24/7 available chatbot video avatars
• Leverage quantum LoRA and quantum neural model enhancement for superior performance
Work with Our Experts for Custom Development Services
• Collaborate with our team of quantum ML experts
• Benefit from end-to-end development of production-ready Quantum ML applications
• Get ongoing support and optimization for your specific uses
Empowering Your Team with Quantum ML Skills
Our comprehensive Quantum ML training program is designed to equip your team with the knowledge and skills needed to leverage quantum-enhanced machine learning effectively.
QML Training Program
Quantum Computing Fundamentals
Understand the principles behind quantum-enhanced Machine Learning
Hands-on TQml Experience
Practical sessions using our cutting-edge toolset
Guided Use Case Exploration
Collaborative workshops to identify high-value applications
Custom Model Development
Guidance on building and optimizing quantum-enhanced ML models
Ready to enhance your computational capabilities?
Let's discuss how TQml can solve your specific challenges.