Skip to content


TQml: Democratizing Advanced Quantum ML

Build, Analyze, and Run Quantum-Enhanced Models

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.

TQml_Powerful-Quantum-Software

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.
In the TQml Toolbox

TQml is a complete ecosystem built to transform your machine learning workflows with quantum-enhanced tools that work seamlessly together.

TQnet
TQnet
Build custom hybrid quantum-classical neural networks with our comprehensive library.
TQcirc
TQcirc
Analyze and optimize quantum circuits for improved model performance and efficiency.
TQtune
TQtune
Optimize your model's hyperparameters efficiently and quickly.
TQbench
TQbench
Compare performance across different hardware and software backends.

Key Features

TQml-proprietary-framework

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.

Quantum-Ready-Architecture-1

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-1

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.

TQml_Interface

Intuitive Interface

Build complex quantum-classical hybrid models with ease using our user-friendly interface. TQml simplifies the development process, reducing the learning curve.

Architecture Overview
Inside TQnet
TQnet is the heart of TQml, offering a rich library of quantum and classical layers to construct powerful hybrid models. Our unique approach allows you to seamlessly integrate quantum computing capabilities into your neural networks.
pure-quantum-layers
Pure Quantum Layers
hybrid-quantum-layers
Hybrid Quantum Layers
quantum-inspired-layers
Quantum-Inspired Layers
Whether you're looking to optimize your supply chain, enhance financial forecasting, or push the boundaries of AI research, TQnet enables you to create models tailored to your specific challenges.
Benefits of Hybrid vs. Classical Approaches to QML
TQml's hybrid quantum-classical approach offers significant advantages over traditional machine learning methods:
  • 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
Problem Types and Applications
TQml excels across a wide range of machine learning tasks, offering quantum-enhanced solutions for problems such as:
  • 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:

  • 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:

Featured Case Study
Revolutionizing Renewable Energy Forecasting with TQml

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

Forecasting the Steam Mass Flow in a Power Plant Using the Parallel Hybrid Network

Sep 2024

Hybrid Quantum Cycle Generative Adversarial Network for Small Molecule Generation

Jul 2024

Quantum Physics-informed Neural Networks for Simulating Computational Fluid Dynamics in Complex Shapes

May 2024

Hybrid Quantum Image Classification and Federated Learning for Hepatic Steatosis Diagnosis

Mar 2024

Photovoltaic Power Forecasting Using Quantum Machine Learning

Dec 2023

Hybrid Quantum ResNet for Car Classification and Its Hyperparameter Optimization

Sep 2023

A Supervised Hybrid Quantum Machine Learning Solution to the Emergency Escape Routing Problem

Jul 2023

Quantum Algorithms Applied to Satellite Mission Planning for Earth Observation

Jun 2023

Parallel Hybrid Networks: an interplay between quantum and classical neural networks

May 2023

An exponentially growing family of universal quantum circuits

May 2023

Benchmarking simulated and physical quantum processing units using quantum and hybrid algorithms

May 2023

Dr. Alexey Melnikov

Dr. Alexey Melnikov

Global Director of Artificial Intelligence

At Terra Quantum, we develop quantum software solutions using hybrid quantum neural networks. Our algorithms, backed by academic research, seamlessly integrate parameterized quantum circuits into cutting-edge machine learning architectures, delivering industry-specific solutions with time series analysis, generative AI, large language models, and physics-driven simulation. Leveraging our extensive experience in providing software solutions across industries, we offer our clients the quantum algorithmic TQml Python library, SDK, and web platform. This comprehensive offering is further enhanced by specialized training from our expert quantum AI team.
Leverage TQml Your Way
Platforms: TQml and TQ42 Support your Growth

• 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 

TQml-GenAi-customer-avatar

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

Coding

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

Academy BG

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.