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Near-term quantum computing applications

Near-term quantum computing applications

A talk I gave at IQT 2019.

Cad23af0aaa59ed6f44b54872eee1baf?s=128

Travis Scholten

March 20, 2020
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  1. 1 Near-term quantum computing applications — Travis L. Scholten Quantum

    Computing Applications Researcher IBM T.J. Watson Research Center
  2. What is a quantum computer?

  3. None
  4. None
  5. None
  6. How do you program a quantum computer?

  7. None
  8. WorkInProgress

  9. Qiskit: an open-source quantum computing framework for leveraging today’s quantum

    processors https://qiskit.org/
  10. The Qiskit software stack Brings quantum computing out of the

    laboratory and into the laptops of developers - Open Source (Apache 2.0) - Written in Python - Modular and extensible Terra A solid foundation for quantum computing Aqua Algorithms for near-term quantum applications Ignis Compute in the presence of errors Aer A high performance simulator framework for quantum circuits 0.7 0.4 0.1 0.1
  11. Qiskit/Python code: Plots of molecule properties: Example: molecular simulation using

    Qiskit Aqua Configure Aqua Use Aqua to run solvers
  12. How do you create a quantum ecosystem?

  13. Accelerate quantum research Promote education and prepare Develop commercial applications

    IBM Q Network mission
  14. Business Framework and Ecosystem Technology and Enablement Support and Collaboration

    IBM Q Network offers
  15. IBM Q Network Hubs University of Oxford Oak Ridge National

    Laboratory NC State University The University of Melbourne University of Munich Keio University Partners JP Morgan Chase & Co. Samsung Daimler JSR Corporation Accenture ExxonMobil Academia University of Minho MIT EDX.org Virginia Tech National University of Taiwan CERN University of Montpellier Members Honda Barclays Hitachi Metals Nagase Mizuho MUFG Mitsubishi Chemicals Argonne Fermilab Berkeley Lab Startups QC Ware CQC 1QBit Zapata QXBranch Q-CTRL Quantum Benchmark MDR Qu&Co Labber Quantum JoS Quantum SolidStateAI Max Kelsen Strangeworks Boxcat ProteinQure Netramark Entropica Labs IBM Q Network
  16. What do you use a quantum computer for?

  17. A (simplified) view of computational problems Easy problems Hard problems

    for classical computers Quantum possible Factoring Simulating quantum mechanics Quantum Advantage with Shallow Circuits IBM 2018 Applications: Chemistry Materials Machine learning Optimization
  18. Group Name / DOC ID / Month XX, 2018 /

    © 2018 IBM Corporation Possible applications for quantum computing 18 Chemistry Material design, oil and gas, drug discovery Artificial Intelligence Classification, machine learning, linear algebra Financial Services Asset pricing, risk analysis, rare event simulation
  19. IBM Q quantum chemistry research

  20. Where we are today Numerical evaluation of ground state properties

    using optimization Experimental evaluation of ground state properties of small molecules on real hardware Impacts Efficiently compute reaction rates of chemical processes Speed up molecular discovery, materials research, drug design Path to quantum advantage Evaluate properties of molecules that cannot be simulated on classical computer Challenges Larger molecules require larger-depth circuits. Develop new algorithms for evaluating properties. Scale up number of qubits in hardware. Quantum chemistry is a natural application for near-term quantum computers.
  21. © 2017 IBM Corporation 21 1. Propose circuit parameters (trial

    state) 2. Measure Hamiltonian terms 4. Update trial state parameters using classical optimizers Molecular Hamiltonian (physics) Map to qubits (circuit) Variational quantum chemistry (VQE) Molecule properties Near-term quantum chemistry relies on hybrid quantum-classical algorithms. 3. Estimate energy of trial state
  22. Chemistry with a quantum computer

  23. Chemistry results have errors caused by noise on the qubits.

    Error mitigation results for quantum chemistry
  24. Error mitigation lets us handle noise; gave 10x improvement in

    accuracy. Error mitigation results for quantum chemistry
  25. IBM Q quantum machine learning research

  26. Where we are today Impacts Path to quantum advantage Challenges

    Experimentally implemented binary classification using a quantum support vector machine Use “quantum kernels” in classical machine learning algorithms for classification Use quantum circuits directly for classifying data New feature maps that cannot be efficiently simulated classically Classification with higher-dimensional data sets Extend technique(s) to more qubits Improved error mitigation to scale up qubit count Methods for selecting quantum feature maps on practical datasets Quantum machine learning: trendy buzzword, or useful near-term application?
  27. Classical computing: Dimensionality and classification Linearly inseparable datasets may become

    linearly separable by increasing dimensionality. Use “quantum-enhanced” feature spaces to tackle new classification problems
  28. Quantum-enhanced feature spaces offer two paths for improving machine learning.

    Quantum kernel estimator: estimates a kernel used by a classical classifier K(fm, fj) = |h (fm)| (fj)i|2 = |h0n|U† (fm) U (fj ) |0ni|2 = Pr(z1 = z2 = · · · = 0) <latexit sha1_base64="pepuVaCp4GVtwA04nG0DxXSG/dk=">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</latexit> <latexit 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  29. Entanglement and classification X 2 X 1 black gold Generate

    a quantum feature map Function we are learning Process the quantum data Shallow-depth quantum circuits as classifiers
  30. Coding the problem using Qiskit Terra Prepare the quantum computer

    in superposition. Train the quantum classifier by updating the quantum network. Using entanglement, map the training data to a quantum state. 0 1 2 0 1 2 1 2 0
  31. The moment of truth Data to be classified X 2

    X 1
  32. IBM Q experimental results # of entanglement steps Classification success

    Try it yourself! https://research.ibm.com/quantumai
  33. IBM Q quantum risk analysis research

  34. Where we are today Impacts Path to quantum advantage Challenges

    Monte Carlo analysis can be sped up using a quantum computer. Improve convergence of simulations Decrease time-to- solution for the analysis Proof-of-principle demonstration for two (financial) toy models Analyzed toy models using real hardware. Leverages an existing quantum algorithm with known speedup Algorithms: Reduce required circuit depth Hardware: Increase depth by increasing the coherence times and reducing error rates
  35. Quantum computers can also speed up Monte Carlo calculations using

    the amplitude estimation algorithm. Example: computing expected value of a T-bill (single-period binomial-tree model) Monte Carlo sampling SWAP gate Inverse Quantum Fourier transform Quadratic improvement compared to classical Monte Carlo methods
  36. Estimation error Number of samples Example problem: T-bill modeling

  37. Group Name / DOC ID / Month XX, 2018 /

    © 2018 IBM Corporation 37 Chemistry Material design, oil and gas, drug discovery Artificial Intelligence Classification, machine learning, linear algebra Financial Services Asset pricing, risk analysis, rare event simulation
  38. IBM Q Experience Explore the IBM Q Experience and start

    using real machines today Qiskit Learn about and start using Qiskit software development kit IBM Q Network Collaborate, research, and start quantum computing through the IBM Q Network IBM Q Discover quantum computing with IBM Q, IBM’s quantum computing initiative Start your quantum journey today with IBM Q
  39. Thank you