Contact Us
Back to Insights
Emerging Tech

Quantum Machine Learning: The Future of AI

Explore the intersection of quantum computing and machine learning. Current research and future possibilities.

Rottawhite Team13 min readNovember 28, 2024
Quantum MLQuantum ComputingFuture Tech

Quantum Computing Meets ML

Quantum machine learning explores how quantum computing can enhance AI capabilities.

Quantum Computing Basics

Qubits

  • Superposition
  • Entanglement
  • Quantum states
  • Quantum Advantage

  • Exponential speedup for some problems
  • Parallel computation
  • Novel algorithms
  • QML Approaches

    Quantum-Enhanced Classical

  • Quantum sampling
  • Quantum optimization
  • Feature mapping
  • Quantum Neural Networks

  • Parameterized quantum circuits
  • Variational algorithms
  • Hybrid classical-quantum
  • Quantum Kernels

  • Quantum feature spaces
  • SVM-like approaches
  • Current Capabilities

    What's Possible Now

  • Small-scale experiments
  • Proof of concepts
  • Hybrid algorithms
  • Limitations

  • Noisy hardware
  • Limited qubits
  • Error rates
  • Potential Applications

  • Optimization problems
  • Drug discovery
  • Financial modeling
  • Cryptography
  • Materials science
  • Key Players

  • IBM Quantum
  • Google Quantum AI
  • IonQ
  • Rigetti
  • D-Wave
  • Timeline Expectations

    Near-term (1-5 years)

  • Niche applications
  • Hybrid approaches
  • Continued research
  • Medium-term (5-15 years)

  • Error-corrected systems
  • Broader applications
  • Commercial viability
  • Conclusion

    Quantum ML holds promise for the future, though practical applications remain mostly ahead.

    Share this article:

    Need Help Implementing AI?

    Our team of AI experts can help you leverage these technologies for your business.

    Get in Touch