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
  • 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?

Sorry, but despite the hype, quantum computing is not appropriate for directly processing large quantities of data — so-called Big Data. Rather, classical software must preprocess data and spoon-feed it to a quantum computer in very small and manageable chunks.

  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

Since quantum computers generally cannot handle Big Data directly, it is necessary to use classical software to break (partition) large problems and large datasets into smaller problems and smaller datasets, each of which can be handled by a sufficiently powerful quantum computer.


Despite the hype, centered on Grover’s algorithm, quantum computers are particularly ill-suited for large-scale search applications, such as an internet search engine or querying of databases. In particular, this is due to the fact that a quantum computer has no access to external or classical data, having direct access to only the relatively few bits of information which can be represented in the limited qubits of a quantum computer.

Quantum computer as a coprocessor

A quantum computer is generally not a full, standalone, general purpose computer in the same sense as any classical computer. Rather, a quantum computer is more of a coprocessor — classical software must perform any data retrieval, preprocessing and data preparation, invocation of the actual quantum program, and then post-processing and storage of the results of the quantum program must also be performed by classical software.

Complex calculations

Despite the focus on massive parallelism, quantum computers are ill-suited for very complex calculations — and better suited for large numbers of relatively simple calculations.

Text processing

Text processing is rather ill-suited for quantum computing at present. Generally, a problem needs to be reduced to a linear range of integers or energy states — a structure that very closely mirrors the physics embodied in the qubits of a quantum computer.

Image and media processing

Large-scale image and media processing is also rather ill-suited for quantum computing at present. Again, generally, a problem needs to be reduced to a linear range of integers or energy states — a structure that very closely mirrors the physics embodied in the qubits of a quantum computer.

Unstructured data

Unstructured data is also rather ill-suited for quantum computing at present. Again, generally, a problem needs to be reduced to a linear range of integers or energy states — a structure that very closely mirrors the physics embodied in the qubits of a quantum computer.

Application structure to exploit quantum computing

Much of the code for an application, any application, won’t be able to exploit the power of a quantum computer and must be executed on a classical computer. It is an open question as to how to best structure a particular application so that a significant fraction of the application components can be executed on a quantum computer.

  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

As big a challenge as it will be to get an application running on a quantum computer at all, all is for naught if the net result is not a quantum advantage, namely that the quantum application is able to achieve a result that is either impossible to achieve with a classical computer or a performance improvement that is absolutely mind-boggling, like 1,000 to a million times faster or even much more, a result of the exponential speedup promised by the proponents of quantum computing.

Lack of clarity and specificity for net effect on performance

Most of the applications listed in this paper have a fair degree of complexity, much of which is not appropriate for a quantum computer, so much of the application must be executed on a classical computer while only key portions are executed on the quantum computer as a coprocessor.

Presumption of applicability vs. reality

In all honesty, the applications listed in this paper are virtually all presumed to be reasonable potential applications for quantum computing, but the reality is that this is mostly speculation and inference rather than reality since none of the presumed applications has been proven in realistic, nontrivial actual, real-world applications.

ENIAC moment for quantum computing

The ENIAC computer was unveiled in 1946 as the first successful digital computer, focused on a specific application — computing artillery firing tables. It wasn’t simply a bare piece of technology, but demonstrated a real and compelling application.

FORTRAN moment for quantum computing

The FORTRAN programming language was the first widely successful high-level programming language and programming model for classical computing. There were some earlier attempts, but FORTRAN made the big splash and opened up computing to a much wider market. Before FORTRAN, programmers had no choice but to code in assembly or raw machine language — the world of bits, words, registers, memory, machine instructions, and raw hardware I/O. It was very, very, VERY tedious. But FORTRAN allowed programmers to write and think in the higher-level terms of variables, integers, real numbers, arrays, control structures (conditionals, loops, and function calls), and even formatted I/O. Programmers became MUCH more productive. Other high-level languages followed, such as COBOL, LISP, and BASIC, but it was FORTRAN which opened the doors (or floodgates!) wide open in the first place.

The applications

Enough with the preliminaries and caveats. On with listing actual (oops — potential) applications for quantum computing, as indicated from specific sources…


Source: https://1qbit.com/

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


Source: https://www.accenture.com/us-en/insights/technology/quantum-computing

  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 Quantum Computing Challenge — Bringing flight physics into the Quantum Era.

  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)

BBVA is “following six lines of research, working hand in hand with Spain’s Senior Council for Scientific Research (CSIC), Accenture, Fujitsu, Zapata Computing, and Multiverse.”

  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.


Source: https://www.daimler.com/innovation/pioneering/quantencomputing-2.html

  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.


Source: https://www.fbo.gov/index?s=opportunity&mode=form&id=2dc9cb27145bc5a144d6e818bb090f21&tab=core&_cview=0

  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)

Source: https://www.energy.gov/articles/department-energy-announces-218-million-quantum-information-science

  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

Source: https://www.dwavesys.com/quantum-computing/applications

  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.


Source: https://ai.google/research/teams/applied-science/quantum-ai/

  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.


Source: https://www.honeywell.com/quantumsolutions

  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.


Source: https://www.research.ibm.com/ibm-q/learn/quantum-computing-applications/

  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.


Source: https://www.intel.com/content/www/us/en/research/quantum-computing.html

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

Ionq Computing

Source: https://ionq.co/news/december-11-2018
Source: https://ionq.co/news/february-26-2019

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


Source: https://www.microsoft.com/en-us/quantum/quantum-computing-applications

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


Source: https://arxiv.org/abs/1905.02860From Ansätze to Z-gates: a NASA View of Quantum Computing

  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)

Source: http://calyptus.caltech.edu/qis2009/documents/FederalVisionQIS.pdfA Federal Vision for Quantum Information Science (2008)

  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

Source: https://www.whitehouse.gov/wp-content/uploads/2018/09/National-Strategic-Overview-for-Quantum-Information-Science.pdf

  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.


Source: https://www.nist.gov/sites/default/files/documents/2018/10/15/3._carl_williams_update_on_nist_quantum_plans.pdf

  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)

Source: https://quantumcircuits.com/news-and-publications/quantum-circuits-inc-qci-a-quantum-computer-startup-out-of-yale-opens-lab-in-new-haven

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


Source: https://qutech.nl/

Rigetti Computing

Source: https://www.rigetti.com/

  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

Source: https://singularityhub.com/2017/06/25/6-things-quantum-computers-will-be-incredibly-useful-for/

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


Source: https://www.volkswagenag.com/en/news/2018/06/volkswagen-tests-quantum-computing-in-battery-research.html

  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.


Source: https://en.wikipedia.org/wiki/Quantum_computing

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


Source: https://www.xanadu.ai/

  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

Source: https://www.zapatacomputing.com/services

  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?

There are certainly plenty of applications of quantum computing for defense and intelligence for national security, but other than what is briefly mentioned for DARPA, little is publicly known. That said, most of the applications from other areas generally apply equally well for the military and intelligence agencies, including:

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


As mentioned at the outset, the list of applications enumerated in this paper is not intended to be exhaustive — there are undoubtedly many other potential applications, as yet undiscovered, or maybe just not yet known to me.

Beyond the unknown

As we contemplate what applications can be handled by a quantum computer we are unfortunately limited or biased by the lens and blinders of classical computers, so that we are not even ready to begin to deeply contemplate alternative ways to even conceptualize problems using a quantum mindset, let alone contemplate solutions using a quantum mindset.

  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!]



Freelance Consultant

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