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Quantum Machine Learning — Research & Applications

Quantum-enhanced machine learning research and applications. VQE, QAOA, quantum neural networks, and hybrid quantum-classical algorithms for enterprise use cases.

GitHub Website

SoftQuantus®'s Quantum Machine Learning research and applications portfolio. We develop and deploy quantum-enhanced algorithms for drug discovery, financial modeling, optimization, and materials science.

Overview

Quantum Machine Learning (QML) combines quantum computing with machine learning to solve problems intractable for classical computers. Our research focuses on practical applications with near-term quantum advantage.

Research Areas

Variational Quantum Algorithms

  • VQE (Variational Quantum Eigensolver): Ground state energy calculations for molecular simulation
  • QAOA (Quantum Approximate Optimization): Combinatorial optimization problems
  • VQC (Variational Quantum Classifiers): Quantum-enhanced classification

Quantum Neural Networks

  • Parameterized Quantum Circuits: Trainable quantum layers for hybrid models
  • StronglyEntanglingLayers: High-expressivity ansatz for QML
  • Barren Plateau Mitigation: Techniques for trainable deep quantum circuits

Hybrid Quantum-Classical

  • Quantum Feature Maps: Embedding classical data into quantum states
  • Classical Pre/Post-Processing: Optimal partitioning of hybrid pipelines
  • Transfer Learning: Leveraging pre-trained quantum models

Use Cases

Drug Discovery

Molecular simulation for pharmaceutical research. Quantum advantage for complex molecular modeling and drug-target interactions.

  • Ground state energy calculations
  • Molecular dynamics simulation
  • Protein folding approximations

Financial Services

Quantum-enhanced portfolio optimization and risk analysis.

  • Portfolio optimization with quantum annealing
  • Credit risk modeling
  • Fraud detection with quantum kernels

Materials Science

Discovering new materials with quantum simulation.

  • Superconductor modeling
  • Battery material optimization
  • Catalyst design

Optimization

Solving complex combinatorial problems.

  • Supply chain optimization
  • Scheduling problems
  • Route optimization

Platform Integration

All QML workloads integrate with the SoftQuantus® stack:

  • QCOS®: Execute circuits with provider-agnostic orchestration
  • SynapseX®: Intelligent routing across quantum and classical backends
  • QCOS Bench™: Reproducible benchmarking for QML algorithms

Example: VQE for H2 Molecule

from qcos import QCOSClient
from qcos.algorithms import VQE
from qcos.molecules import H2
 
client = QCOSClient(api_key="your-key")
 
# Define molecule
molecule = H2(bond_distance=0.74)
 
# Run VQE
vqe = VQE(
    molecule=molecule,
    optimizer="COBYLA",
    ansatz="UCCSD"
)
 
result = client.run_algorithm(vqe, shots=8192)
print(f"Ground state energy: {result.energy} Ha")

Resources