What Applications Are Suitable for a Quantum Computer?

  • Optimization, planning, and logistics
  • Forecasting
  • Financial modeling
  • Drug design and discovery
  • Genomics
  • Cybersecurity and cryptography
  • Molecular modeling
  • Chemistry modeling, computational chemistry
  • Material design and modeling
  • Aerospace physics
  • Quantum simulation — simulation of physical systems at the quantum mechanical level
  • Artificial intelligence, machine learning
  • Random number generation
  • 1QBit
  • Accenture
  • Airbus
  • BBVA (Banco Bilbao Vizcaya Argentaria, S.A — Spain)
  • Daimler
  • DARPA
  • Department of Energy (DOE)
  • D-Wave Systems
  • Google
  • Honeywell
  • IBM
  • Intel
  • IonQ
  • Microsoft
  • NASA
  • National Science and Technology Council (NSTC)
  • National Strategic Overview for Quantum Information Science
  • NIST
  • Quantum Circuits, Inc. (QCI)
  • QuTech
  • Rigetti Computing
  • Singularity Hub
  • Volkswagen
  • Wikipedia
  • Xanadu
  • Zapata Computing
  • Big Data
  • Partitioning large datasets
  • Search
  • Quantum computer as a coprocessor
  • Complex calculations
  • Text processing
  • Image and media processing
  • Unstructured data
  • Application structure to exploit quantum computing
  • Need for a compelling quantum advantage
  • Lack of clarity and specificity for net effect on performance
  • Presumption of applicability vs. reality
  • ENIAC moment for quantum computing
  • FORTRAN moment for quantum computing

Big Data?

  1. No equivalent of a classical computer terminal to read and display data.
  2. No ability to read and write text or data files.
  3. No ability to access a database to read, write, update, or query data.
  4. No ability to access data over a network.
  5. No ability to send or receive data from web services over a network.
  6. No real-time sensor access.

Partitioning large datasets

Search?

Quantum computer as a coprocessor

Complex calculations

Text processing

Image and media processing

Unstructured data

Application structure to exploit quantum computing

  1. Data retrieval. Fetch input data from data sources. Pure classical code.
  2. Preprocessing and data preparation. Pure classical code. Input data ready to be processed by quantum code.
  3. Quantum state preparation. Pure quantum code. Mapping of classical bits of input data to qubits to initialize the quantum state of the quantum computer.
  4. Invocation of the actual quantum program. Pure classical code which is transitioning to pure quantum code.
  5. Quantum code to do the quantum processing. Pure quantum code. Where all the real quantum action is, the quantum algorithm, exploiting quantum parallelism.
  6. Measurement. Capturing the results of the quantum program. Mapping quantum state of qubits to binary classical bits. Pure quantum code. Note: Measuring the quantum state of a qubit causes the quantum state to collapse into either a classical binary 0 or a classical binary 1. Any superposition, entanglement, or phase of that qubit will be lost.
  7. Post-processing. Additional processing of the quantum results. Not all processing can always be done or is best done on the quantum computer. Pure classical code.
  8. Storage of the post-processed results of the quantum program. After post-processing. The final results of the application.

Need for a compelling quantum advantage

Lack of clarity and specificity for net effect on performance

Presumption of applicability vs. reality

ENIAC moment for quantum computing

FORTRAN moment for quantum computing

The applications

1QBit

  1. Optimization.
  2. Simulation.
  3. Machine learning.
  4. Currently intractable problems.

Accenture

  1. Optimization. Today quantum computing is ideal for solving optimization problems — sorting through vast potential solutions to arrive at the best decision.
  2. Chemistry. Quantum computers can facilitate real-world quantum system simulation, unlocking efficiency in material design and drug analysis.
  3. Machine Learning. Quantum computers can provide reliable data for machine learning algorithms. Each iteration of new data can help artificial intelligence “learn.”
  4. Financial Services. Quantum computing can help determine attractive portfolios and flag key fraud indicators using thousands of assets with interconnecting dependencies.
  5. Life Sciences. Replacing certain protein folding and therapy discovery techniques, quantum computing can help improve drug design.
  6. Manufacturing. As quantum computing evolves, it will help solve supply chain optimization and purchasing challenges.
  7. Resources. Quantum computing can help identify optimal product lifecycle and replacement issues at a system-wide scale.
  8. Media & Technology. Quantum computers are well suited to schedule advertising and maximize ad revenue, tailored on a per-customer basis.

Airbus

  1. Aircraft Climb Optimisation.
  2. Computational Fluid Dynamics.
  3. Quantum Neural Networks for Solving Partial Differential Equations.
  4. Wingbox Design Optimisation.
  5. Aircraft Loading Optimisation.

BBVA (Banco Bilbao Vizcaya Argentaria, S.A — Spain)

  1. Development of quantum algorithms (CSIC).
  2. Static Portfolio Optimization (Fujitsu).
  3. Dynamic portfolio optimization (Accenture, Multiverse).
  4. Credit scoring process optimization (Accenture).
  5. Currency arbitrage optimization (Accenture).
  6. Derivative valuations and adjustments (Zapata Computing).
  1. Improve machine learning algorithms.
  2. Improve energy efficiency.
  3. Help advance toward a more sustainable society.

Daimler

  1. Selection of new materials based on quantum chemistry — e.g., for the development of battery cells.
  2. Efficient and convenient provision of individual mobility — e.g., traffic management for autonomous vehicles in urban environments and megacities.
  3. Logistics planning for delivery vans, where routes need to be planned and updated in real time on the basis of numerous variables.
  4. Optimization of production planning and production processes.
  5. Machine learning to advance the development of artificial intelligence.

DARPA

  1. Understanding complex physical systems.
  2. Hard science modeling problems.
  3. Improving artificial intelligence (AI) and machine learning (ML) and deep learning (DL).
  4. Enhancing distributed sensing.

Department of Energy (DOE)

  1. Solve large, extremely complex problems that lie entirely beyond the capacity of even today’s most powerful supercomputers.
  2. Exquisitely sensitive sensors, with a variety of possible medical, national security, and scientific applications.
  3. Cybersecurity and encryption.
  4. Provide insights into such cosmic phenomena as Dark Matter and black holes.

D-Wave Systems

  1. Optimization. Too many combinations of options to evaluate exhaustively on classical computers.
  2. Machine learning. Detecting recurring patterns in huge amounts of data with an immense number of potential combinations of data elements.
  3. Materials simulation.
  4. Monte Carlo simulation. Complex models, with many different variables.

Google

  1. Artificial intelligence.
  2. Machine learning. Classification and clustering.
  3. Generative and discriminative quantum neural networks.
  4. Discrete optimization.
  5. Simulation of new materials.
  6. Elucidation of complex physics.
  7. Simulation of chemistry.
  8. Simulation of condensed matter models.
  1. Random number generation — generating certifiable random numbers.
  2. Materials science.
  3. Chemistry.

Honeywell

  1. Pharmaceuticals. Improve the efficiency of early-phase drug design and discovery.
  2. Chemicals. Accelerate development of new chemicals.
  3. Finance. Reduce risk through improved portfolio insight.
  4. Aerospace/Defense. Develop new aircraft materials and advanced military technology.
  5. Oil & Gas. Optimize production and expedite exploration.
  6. Data center. Accelerate machine learning and analysis of large data sets.
  7. Manufacturing. Gain visibility into design and production limitations.
  8. Telecommunication. Optimize antenna efficiency and bandwidth utilization.

IBM

  1. Medicine & Materials. Untangling the complexity of molecular and chemical interactions leading to the discovery of new medicines and materials.
  2. Supply Chain & Logistics. Finding the best solutions for ultra-efficient logistics and global supply chains, such as optimizing fleet operations for deliveries during the holiday season.
  3. Financial Services. Finding new ways to model financial data and isolating key global risk factors to make better investments.
  4. Artificial Intelligence. Making facets of artificial intelligence such as machine learning much more powerful when data sets are very large, such as in searching images or video.

Intel

  1. Massive parallelism.
  2. Simulate and analyze natural phenomena.
  3. Individualized genetic medicine.
  4. Astrophysics.
  5. Solving environmental challenges.

Ionq Computing

  1. Simulation.
  2. Hard optimization problems.
  3. Materials.
  4. Pharmaceuticals.
  5. Medicine.
  6. Chemistry. Computational chemistry. Chemicals.
  7. Energy.
  8. Logistics.
  9. Finance.

Microsoft

  1. Chemistry. Molecular interactions.
  2. Material science.
  3. Optimization problems.
  4. Machine learning.

NASA

  1. Planning.
  2. Scheduling.
  3. Fault diagnosis.
  4. Machine learning.
  5. Robustness of communication networks.
  6. Simulation of many-body systems for material science and chemistry.

National Science and Technology Council (NSTC)

  1. Impossible problems.
  2. Greatly improved sensors. Mineral exploration and medical imaging.
  3. Exotic new and emergent states of matter that emerge from collective quantum systems. Including fractional quantum Hall states, topological insulators, new superconducting materials, and new states of matter that arise through quantum phase transitions.
  4. Enable long-lived quantum mechanical states.
  5. Quantum chemistry.
  6. Drug design.
  7. Design and development of new and exotic materials. Including materials for energy systems.
  8. Simulate quantum mechanical systems.
  9. Development of an analog quantum simulator.
  10. Accurate predictions of chemical properties.
  11. Measurements on individual quantum systems.
  12. Implications for national security.
  13. Implications for future economic competitiveness.
  14. Improvements in the global positioning system.
  15. Health care.

National Strategic Overview for Quantum Information Science

  1. Materials development.
  2. New approaches to understanding materials.
  3. New approaches to understanding chemistry.
  4. Chemical calculations.
  5. Modeling of chemical reactions to enhance corrosion-resistant materials.
  6. New approaches to understanding gravity
  7. Novel algorithms for machine learning.
  8. Novel algorithms for optimization
  9. Transformative cyber security systems including quantum-resistant cryptography.
  10. Improvements in effective drug discovery.
  11. Optimization.
  12. Optimizing logistics solutions.
  13. Machine learning.
  14. Quantum sensing.

NIST

  1. Quantum sensors.
  2. Secure data transmission.
  3. Random number generation.
  4. Simulation.
  5. Complex materials.
  6. Molecular dynamics.
  7. QCD. [Modeling Quantum chromodynamics?]
  8. Cryptanalysis. Including post-quantum cryptography.
  9. Quantum chemistry.
  10. Optimization.
  11. Quantum field theory.
  12. Quantum networks.

Quantum Circuits, Inc. (QCI)

  1. Drug design for biotech.
  2. Materials science.
  3. Improved processes for industrial chemicals.
  4. Fintech.
  5. Logistics.
  6. Machine learning.
  7. Energy.

QuTech

Rigetti Computing

  1. Solve previously unsolvable problems.
  2. Address fundamental challenges in medicine, energy, business, and science.
  3. Chemistry. Predicting the properties of complex molecules and materials. Design more effective medicines, energy technologies and more resilient crops.
  4. Machine Learning. Training advanced AI on quantum computers.
  5. Computer vision, pattern recognition, voice recognition, and machine translation.
  6. Optimization. Solve complex optimizations such as ‘job shop’ scheduling and traveling salesperson problems.
  7. Drive critical efficiencies in businesses, military and public sector logistics, scheduling, shipping, and resource allocation.

Singularity Hub

  1. Artificial Intelligence.
  2. Molecular Modeling.
  3. Cryptography.
  4. Financial Modeling.
  5. Weather Forecasting.
  6. Particle Physics.

Volkswagen

  1. Simulate the chemical structure of batteries.
  1. Traffic optimization.
  2. Smart traffic management.
  3. Passenger number prediction. [Not completely clear if this is a quantum component or a classical component which works in conjunction with the quantum route optimization.]
  4. Route optimization, congestion-free route optimization.

Wikipedia

  1. Cryptography.
  2. Quantum search.
  3. Quantum simulation.
  4. Quantum annealing and adiabatic optimization
  5. Solving linear equations

Xanadu

  1. Machine learning.
  2. Chemistry. Drug discovery and material design.
  3. Finance. Accelerating complex pricing models that factor in numerous outcomes and variables changing over time. Portfolio optimization, algorithmic trading, and quantum machine learning for fraud detection.
  4. Optimization.
  5. Sensors. Quantum sensing, using quantum photonics, combined with quantum machine learning techniques, has the potential to make autonomous cars safer and scanning precious biological or chemical samples more accurate.

Zapata Computing

  1. Chemistry Simulation.
  2. Logistics optimization.
  3. Machine Learning.
  4. Financial Tech.
  5. Materials Design.
  6. Pharma Lead Gen.
  7. Bio-informatics.

Military and intelligence?

  • Cybersecurity and cryptography.
  • Optimization, planning, and logistics.
  • Material science.
  • Modeling chemical reactions.
  • Quantum system simulation.
  • Artificial intelligence.

Other

Beyond the unknown

  1. The relentless march of technology, sometimes in giant leaps, mostly in incremental progress, and sometimes long gaps when little progress seems to be being made.
  2. Evolution of how we analyze problems.
  3. Evolution in how we conceptualize solutions.
  4. Evolution of quantum hardware, software, and tools.
  5. Evolution of building blocks for quantum algorithms.
  6. Learning from the work of others.
  7. Waiting for technological advances, or divine inspiration for clever ways to do better with what technology we already have.
  8. Feedback loops between any and all of the above. Co-design of hardware and algorithms, for example.
  9. Rinse and repeat. [Hmmm… that’s a classical concept; what’s the quantum version of rinse and repeat, other than massive parallel rising and repeating!]

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Jack Krupansky

Jack Krupansky

Freelance Consultant

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