Quantum + Agentic AI

SuperQuantX

The Foundation for the Future of Agentic and Quantum AI

SuperQuantX unified API for the next wave of Quantum AI. Build powerful Quantum Agentic AI systems with a single interface to Qiskit, Cirq, PennyLane, and more.

Open Source 📚 Documentation

"Your launchpad into the world of Quantum + Agentic AI"

SuperQuantX - Quantum Computing Made Simple

$pip install superquantx

✅ Successfully installed SuperQuantX

$python

>>> import superquantx as sqx

>>> backend = sqx.get_backend('simulator')

>>> circuit = backend.create_circuit(num_qubits=2)

>>> circuit.h(0) # Put qubit 0 in superposition

>>> circuit.cx(0, 1) # Entangle qubits 0 and 1

>>> circuit.measure_all()

>>> result = backend.run(circuit, shots=1000)

>>> counts = result.get_counts()

🎯 Quantum entanglement achieved!

Measurement results: {'00': 523, '11': 477}

⚛️ Only |00⟩ and |11⟩ states - perfect entanglement!

Quantum

⚛️ What is SuperQuantX?

SuperQuantX is a cutting-edge experimental quantum AI research platform that provides a unified API for quantum-agentic systems research. It bridges the gap between multiple quantum computing frameworks and makes quantum machine learning accessible to researchers, developers, and students building autonomous quantum-enhanced AI systems.

🔬 The Vision

Unifying the fragmented quantum computing landscape into a single, powerful interface for agentic AI research.

Bridge multiple quantum frameworks (PennyLane, Qiskit, Cirq, Braket, TKET, D-Wave)
Enable seamless backend switching without code changes
Accelerate quantum machine learning research
Make quantum computing accessible to AI researchers
Power the next generation of quantum-enhanced agents

🚀 Why Now?

The quantum computing landscape is fragmented across different frameworks, each with unique strengths but incompatible APIs.

SuperQuantX solves this with:

One consistent API across all quantum frameworks
Research-first design for experimentation
Built-in quantum ML algorithms and agents
Educational resources and comprehensive documentation

Your launchpad into the world of Quantum + Agentic AI

🎬 See SuperQuantX in Action

Watch how SuperQuantX simplifies quantum computing and makes it accessible for everyone

Launch Demo

•

Live Quantum Computing

•

Real-time Examples

✨ Core Features

Everything you need to build, research, and deploy quantum-enhanced AI systems

🔗 Unified API

Single interface for multiple quantum computing backends - switch frameworks without changing your code

🎯 Agentic AI Focus

Specialized tools for quantum agent development and autonomous decision-making systems

🚀 Multi-Backend Support

PennyLane, Qiskit, Cirq, Amazon Braket, TKET, D-Wave Ocean - all in one platform

📊 Advanced Algorithms

Pre-built quantum machine learning and optimization algorithms ready for research

🛠️ Developer Friendly

Comprehensive documentation, examples, and educational resources

⚡ High Performance

Optimized for both research experimentation and production workloads

🧪 Research-First Design

Reproducibility, flexibility, and experimentation built into every feature

📈 Visualization Tools

Built-in quantum circuit visualization and result analysis capabilities

☁️ Cloud Integration

Seamless access to cloud quantum computers and managed simulators

🎯 Supported Quantum Backends

Access the entire quantum computing ecosystem through one unified interface

🍃

PennyLane

Xanadu

Differentiable programming
ML integration
Multi-vendor support

🔷

Qiskit

IBM

IBM hardware access
Advanced transpilation
Noise modeling

🔴

Cirq

Google

Google hardware
NISQ algorithms
Circuit scheduling

🟡

Amazon Braket

AWS

Multi-vendor access
Cloud computing
S3 integration

⚪

TKET

Quantinuum

Advanced optimization
H-Series access
High fidelity

🌊

D-Wave Ocean

D-Wave

Quantum annealing
5000+ qubits
Optimization problems

🚀 Get Started in Minutes

Experience the power of unified quantum computing with these hands-on examples

Your First Quantum Circuit

# Create quantum entanglement

import superquantx as sqx

backend = sqx.get_backend('simulator')

circuit = backend.create_circuit(2)

# Add quantum gates

circuit.h(0) # Superposition

circuit.cx(0, 1) # Entanglement

circuit.measure_all()

result = backend.run(circuit, shots=1000)

# Result: {'00': 523, '11': 477}

Quantum Machine Learning

# Quantum SVM in action

from sklearn.datasets import make_classification

X, y = make_classification(n_samples=100)

# Create Quantum SVM

qsvm = sqx.QuantumSVM(

backend='simulator',

feature_map='ZFeatureMap'

)

qsvm.fit(X_train, y_train)

accuracy = qsvm.score(X_test, y_test)

# Quantum advantage achieved! 🎯

Easy Backend Switching

# Same code, different backends

backends = ['simulator', 'pennylane', 'qiskit']

for backend_name in backends:

qsvm = sqx.QuantumSVM(backend=backend_name)

qsvm.fit(X, y)

accuracy = qsvm.score(X, y)

print(f"{backend_name}: {accuracy:.3f}")

# Cross-platform validation ✅

Advanced Quantum Algorithms

# Variational Quantum Eigensolver

import numpy as np

H = np.array([[1, 0], [0, -1]]) # Hamiltonian

vqe = sqx.VQE(backend='simulator', hamiltonian=H)

ground_energy = vqe.find_ground_state()

# Quantum Neural Network

qnn = sqx.QuantumNeuralNetwork(

n_qubits=4, n_layers=2

)

# Research-grade algorithms ready! 🔬

Try the Full Quickstart Guide

🔬 Research Applications

SuperQuantX accelerates research across multiple quantum computing domains

Quantum Machine Learning

QSVM, QNN, quantum feature maps, and hybrid classical-quantum algorithms

Quantum Optimization

QAOA, VQE, quantum annealing for complex optimization problems

Quantum Agents

Decision-making quantum systems and autonomous quantum-enhanced AI

Hybrid Algorithms

Classical-quantum hybrid approaches for real-world applications

NISQ Applications

Near-term quantum device algorithms optimized for current hardware

Educational Research

Interactive quantum education and accessible learning platforms

Ready to Enter the Quantum Age?

Join researchers worldwide building the future of quantum-enhanced AI systems

Open Source

MIT license, community-driven development

Well Documented

Comprehensive guides and API references

Active Community

Researchers, developers, and educators

Get Started on GitHub 📚 Read the Documentation

Quick Start: Install with pip install superquantx and start building quantum-enhanced AI systems in minutes.