FUSING QUANTUM MECHANICS & ARTIFICIAL INTELLIGENCE

Quantum Bionic™ AI Software

Quantum Bionic™ AI is a cloud-based framework for Extreme QML Pair Modeling and Training of hybrid quantum-classical ML models, designed for the problems of NISQ-era quantum machine learning. The framework offers high-level abstractions for reinforcement learning algorithms designed to create powerful quantum-inspired AI systems, enable quantum quantization of classical machine learning models, mitigate errors and produce higher quality quantum gates.  

PRODUCT OVERVIEW

Quantum Bionic™ AI service provides high-frequency bridge between artificial intelligence and quantum computing. The hybrid quantum-classical cloud-based framework offers a single point of access to a variety of quantum computing technologies. 

  • Build, train, optimize quantum and hybrid machine learning algorithms.

  • Test and measure machine learning algorithms on different quantum circuit simulators.

  • Deploy machine learning algorithms on different types of quantum computers

  • Experience rapid QML Pair Modeling and Training of hybrid quantum-classical ML models,

Quantum Bionic™ AI is built on three fundamental core services: Classical/Quantum Data Loader™, BionicML™ enabling hybrid quantum-classical model and CircuitFusion™ service accelerators providing a rich ecosystem of backend integrated quantum circuits

Whether you’re aiming to build, train and enhance classical machine learning algorithms by promoting difficult computational calculations to a near term quantum device or build, train quantum circuits and optimize quantum algorithms such Variational Quantum Eigensolver (VQE) or a Quantum Approximation Optimization Algorithm (QAOA), BionicML™ Toolkit is the unified standard service interface to both your favorite classical machine learning libraries.

CircuitFusion™ provides an accelerator bridge to powerful quantum circuit programming libraries such Cirq, Q#, Qiskit, Forest and Ocea, targeting a wide range of quantum hardware including gate-based quantum computers and quantum annealing systems

QUANTUM BIONIC™ AI LOGICAL ARCHITECTURE

Built-in support for existing machine learning libraries

Bionic ML provides Quantum-inspired classical algorithms and supervised learning quantum classifiers APIs with intelligent allocation of the quantum processing unit (QPU) but it may also use the CPU of your laptop compute. Available in both C++ and Python for hybrid quantum and classical models, Bionic ML is designed for hybrid classical and near-term quantum computing devices, supporting various quantum information paradigms to train quantum circuit models on different backends.

The unified device architecture of provides quantum computation and optimization support APIs that connects quantum circuit models running on real quantum devices or simulators to a broad range of frameworks and libraries.

Bionic ML™ provides a seamless backend integration with a wide range of classical machine learning libraries.

Bionic ML™ Framework Architecture

Built-in support for several quantum devices and simulators

CircuitFusion™ provides an accelerator bridge to powerful quantum circuit programming libraries such Cirq, Q#, Qiskit, Forest and Ocea, targeting a wide range of quantum hardware including gate-based quantum computers and quantum annealing systems.

CircuitFusion™ is a cross-device quantum-hub library that effortlessly manages the evaluation, training and compute of gradients quantum variational circuits on quantum devices, then retrieves the measurement and state results (including samples, measurement statistics, and probabilities).

Quantum devices can be easily enabled through CircuitFusion Device Management Console providing a broad range of quantum circuit programming libraries with an integrated ecosystem of quantum hardware.

Quantum Classifiers

Quantum classifiers require pre-processing to encode classical data into quantum data, map the training data to a quantum feature map, incorporate more entanglement and quantum properties and then re-train the quantum classifier by updating the QNN , finally validate the model to determine its accuracy.

We harness the unique properties of quantum mechanics (QM) and quantum computing (QC) to remove the obstacles to achieve AGI (Artificial General Intelligence) This is why we use quantum computing for extreme programming and training of machine learning models in order create optimized quantum artificial intelligence algorithms. Our research and developments are focused on neuromorphic cognitive models, adaptive machine learning, and quantum reasoning under uncertainty.