When Will Quantum Computing Advance Beyond Mere Laboratory Curiosity?

  1. What is a laboratory curiosity?
  2. Criteria from the definition of laboratory curiosity
  3. Summary of how quantum computing stacks up relative to these criteria
  4. Short answer: Not soon
  5. Decent progress, but…
  6. Much research is needed
  7. More lab time is needed
  8. When Will Quantum Computing Have Its ENIAC Moment?
  9. Maybe an ENIAC moment for each application category
  10. When Will Quantum Computing Have Its FORTRAN Moment?
  11. ENIAC was still a laboratory curiosity
  12. Possibly between the ENIAC and FORTRAN moments
  13. The proof point for quantum computing
  14. A few good applications
  15. What can’t a quantum computer compute?
  16. No, not all compute-intensive applications are appropriate for quantum computing
  17. Intellectual property (IP) — boon or bane?
  18. Open source is essential
  19. Not yet a candidate for release from the lab
  20. Yes, quantum computing remains a mere laboratory curiosity
  21. No, quantum computing is not ready for prime-time production-scale applications
  22. Hedge: Maybe some narrow niche applications
  23. All of this could change with just a few key breakthroughs
  24. Moment of truth — imminent deployment
  25. Actual deployment vs. mere intent
  26. Evaluation of deployment
  27. Okay, but When?
  28. Milestones from today to post-laboratory curiosity
  29. Moore’s law for qubits
  30. Quantum ready
  31. Quantum insurance
  32. Setting expectations
  33. Papers, books, conferences, conventions, trade shows, seminars, online communities, and meetups
  34. Quantum volume
  35. Beyond success of consultants
  36. Critical mass of interest, but…
  37. Need a critical mass of technology
  38. Technological deficits
  39. The greatest challenges for quantum computing are hardware and algorithms
  40. Not clear what the ideal qubit technology will be
  41. The ideal qubit technology has not been invented yet
  42. Hybrid applications — how best to blend quantum and classical computing
  43. Google — no commercial machine yet
  44. Microsoft and Intel — no machines yet
  45. Honeywell — an initial splash, but follow-through needed
  46. Rigetti — losing steam?
  47. IonQ — some initial progress, but waiting for follow-through
  48. IBM — lots of machines, but still too limited
  49. Other machine vendors
  50. How many qubits does a production system need?
  51. Subsidiary technologies
  52. Need a critical mass of algorithms and applications
  53. Need a critical mass of algorithmic building blocks
  54. Need a critical mass of design patterns
  55. Need a critical mass of application frameworks
  56. Is NISQ an obstacle?
  57. Is quantum error correction needed?
  58. What if quantum error correction is required?
  59. Gate fidelity is important
  60. What algorithm advances are needed?
  61. Quantum advantage
  62. Need benchmarks for quantum advantage
  63. Quantum advantage is mandatory
  64. There’s no point to quantum computing without quantum advantage
  65. Quantum supremacy
  66. Didn’t Google achieve quantum supremacy?
  67. Which application category will be first to achieve quantum advantage for a production-scale application?
  68. When will a practical algorithm be implemented for more than 32 qubits?
  69. Quantum advantage today: true random number generation
  70. Need for higher performance quantum simulators
  71. Need for a new model for design of scalable algorithms
  72. Need to move beyond the lunatic fringe of early adopters
  73. How scalable is your quantum algorithm or application?
  74. Do we need a universal quantum computer?
  75. Quantum computer as a coprocessor
  76. Tools and support software are essential
  77. Need for Principles of Operation documentation and specifications
  78. Need for detailed personas, use cases, access patterns
  79. How are companies using quantum computing today?
  80. Isn’t Monte Carlo simulation good enough for most applications?
  81. Quantum-inspired algorithms
  82. What about D-Wave Systems?
  83. Is money a significant issue at all?
  84. Is more venture capital needed?
  85. Limited talent pool
  86. Repurpose existing technical talent
  87. Obsession over Grover search algorithm even though not exponential advantage
  88. Shor’s algorithm is still cited and treated as if it was already implemented even though very impractical
  89. Can we expect quantum computing to cure cancer, hunger, poverty, and inequality?
  90. Never underestimate the power of human cleverness and intuition
  91. Would Rip Van Winkle miss much if he slept for the next 2 years? 5 years?
  92. Will two or three years be enough? Very unlikely
  93. Some say three to five years, but I don’t see it
  94. Five years? Outside possibility, but still unlikely
  95. Seven years? Maybe, if researchers finally get their acts together
  96. Ten years? One would hope, but on the verge of being a zombie technology
  97. Fifteen years? Seems like a slam dunk, but you never know
  98. Twenty years? If not by then, maybe never?
  99. Prospect of a quantum winter?
  100. Mixed messages from the National Science Foundation (NSF)
  101. Ethical considerations
  102. Regulatory considerations
  103. Conclusions
  104. What’s next?

What is a laboratory curiosity?

  • A laboratory curiosity is a scientific discovery or engineering creation which has not yet found practical application in the real world.
  • A laboratory curiosity is a scientific discovery or engineering creation which has not yet been effectively transformed into a product or service which economically delivers substantial real-world value and which can be used outside of the laboratory. It still requires the careful attention of the research technical staff for its use, and faces significant ongoing research and development. It promises to deliver fantastic benefits, but has not yet done so, and doesn’t yet have a very short-term path to doing so. It is not yet ready for prime time — for production-scale real-world applications. A new technology needs to offer clear, substantial, and compelling benefits of some sort over existing technology, whether they be new functions and features, performance, less-demanding resource requirements, or economic or operational benefits. There may well be papers, books, conferences, conventions, trade shows, seminars, online communities, and meetups focused on the technology and its potential applications, but they may focus more on academic topics and evaluation and experimentation — proofs of concept and prototypes — rather than focusing on actual delivery of substantial real-world value — they are necessary but not sufficient to advance beyond mere laboratory curiosity.
  • delivers substantial real-world value

Criteria from the definition of laboratory curiosity

  1. product or service
  2. economically delivers
  3. delivers substantial real-world value
  4. can be used outside of the laboratory
  5. requires the careful attention of the research technical staff for its use
  6. faces significant ongoing research and development
  7. promises to deliver fantastic benefits, but has not yet done so
  8. … doesn’t yet have a very short-term path to doing so
  9. not yet ready for prime time
  10. … for production-scale real-world applications
  11. offers clear, substantial, and compelling benefits over existing technology
  12. … new functions
  13. … new features
  14. … performance
  15. … resource requirements
  16. … economic benefits
  17. … operational benefits.
  18. papers, books, conferences, conventions, trade shows, seminars, online communities, and meetups — but not focusing on actual delivery of substantial real-world value.

Summary of how quantum computing stacks up relative to these criteria

  1. Rigetti and IBM are offering remote access over the Internet, but the machines themselves remain in laboratory environments.
  2. Almost anybody can use the systems, remotely, although no mere mortal outside the laboratories can operate or maintain the systems themselves.
  3. Product or service? Depends how you want to define that. Is remote, shared access sufficient? As a service, yes, as a product, no.
  4. Economically delivered? Well, it’s essentially free right now since the vendors are giving it away, but for production-scale use we have no hint as to what it might cost. I have no expectation that quantum computing at a production scale will be free. Essentially, the hardware vendors are currently eating 100% of the costs — I wouldn’t call that economic delivery.
  5. Can it be used outside the laboratory? Well, indirectly, using remote access, but the machines themselves remain closeted in the laboratories.
  6. Quantum computers do deliver one small function which classical Turing machines can’t even theoretically offer: true random number generation — it’s inherent in the probabilistic nature of quantum mechanics and quantum computers. Classical computers can generate pseudo-random numbers, but not true random numbers, although special, non-digital hardware can be used to collect entropy from the environment to generate true random numbers. This function is available today, even on the simplest of quantum computers.
  7. Plenty of papers, conferences, conventions, trade shows, seminars, and meetups, but focused more on academic topics and evaluation and experimentation, such as proofs of concept and prototypes, and setting speculative expectations for speculative future use, rather than delivery of substantial real-world value in the present.
  1. Quantum advantage is mandatory, but not yet achieved. There is only one benefit that quantum computing promises to offer — dramatically greater performance than even the best classical computers — known as quantum advantage. Without quantum advantage, quantum computers have no inherent advantage over classical computing. Quantum supremacy is a key promise of quantum computing as well — performance so incredible that computations are now possible which were not possible on classical computers at all, even given years, decades, or centuries of running time, but not mandatory at the early stages. Are we there yet? No, not even close, for either quantum advantage or quantum supremacy.
  2. A number of the announced hardware entrants in the sector have not yet fielded working systems, even in the laboratory. (Intel, Microsoft, Xanadu?)
  3. Some of the machines up and running are not yet available to the outside world, either at all or generally other than via special arrangements. (Google, IonQ, Honeywell)
  4. Robust documentation and detailed specifications are not generally available.
  5. Not yet delivering substantial real-world value, and no clear pathway to that end.
  6. Facing significant ongoing research and development. Still only in the early stages, far short of the hardware needed for production-scale applications.
  7. Promises to deliver fantastic benefits, but has not yet done so
  8. … doesn’t yet have a very short-term path to doing so
  9. not yet ready for prime time — not even close
  10. … for production-scale real-world applications
  1. Hardware — not enough qubits.
  2. Hardware — poor fidelity — coherence, gate errors, measurement errors.
  3. Hardware — no clear sense of whether quantum error correction is essential or whether NISQ will be good enough.
  4. Hardware — minimal circuit depth.
  5. Hardware — quite a few of the announced machines are not yet available.
  6. No interesting level of algorithmic building blocks for building applications.
  7. Little in the way of design patterns.
  8. Need for application frameworks. Minimize reinvention of the wheel by each application.
  9. Few examples of realistic algorithms (quantum circuits). Mostly proof of concept, not production-scale.
  10. Proof of concept and prototype stage. People are attempting to develop algorithms and applications for real-world use cases, but they are still at the proof of concept and prototyping stage — very limited input data, limited function, and no clear path for scaling up to production-scale real-world use cases.
  11. No reasonable high-level programming model. Forced to work at the level of the raw physics — Bloch sphere rotations and unitary matrices
  12. No easy way to transform classical applications or algorithms to quantum computing.
  13. Quantum advantage — still no meaningful examples of quantum algorithms for a practical real-world application actually outperforming classical solutions in a truly dramatic manner.
  1. No clear picture of where we’re really going — what a quantum application will look like when quantum computing is ready for prime-time, production-scale real-world applications? Quantum error correction? Post-NISQ? Algorithmic building blocks? Design patterns? Application frameworks? High-level programming model? Quantum-specific programming language(s)?
  2. In what time frame can we expect any or all of that? Five to ten years? Sooner? Much sooner? Later? Much later? What sort of roadmap?

Short answer: Not soon

Decent progress, but…

Much research is needed

  • Theoretical research. I’m not persuaded that all of the needed theory has been fully elaborated, especially when it comes to programming models and algorithmic building blocks, as well as scaling beyond a few to a couple of dozen qubits to hundreds and thousands, possibly even millions. Yes, there’s a lot of applied research needed, but it should have a much firmer bedrock of theory under it than I perceive at present. One example that concerns me is the granularity of phase — quite a few algorithms treat phase as if it were an infinitely-fine continuous value — both theory and basic research are needed to determine the truth about what assumptions algorithm designers can make about the granularity of phase.
  • Basic research. Additional quantum phenomena which can be exploited for qubits. Controlling noise, errors, coherence, and environmental interference. Algorithm research at a basic level.
  • Applied research. Large ensembles of qubits and their connectivity. Engineering as well as science — how to actually build working qubits, ensembles of qubits, and entire quantum computers. Development of algorithmic building blocks, higher-level programming models, and design patterns and application frameworks to serve as a foundation for algorithm designers and application developers.

More lab time is needed

When Will Quantum Computing Have Its ENIAC Moment?

  • ENIAC moment. The stage at which a nascent technology is finally able to demonstrate that it is capable of solving a significant real-world problem — actually solving a problem and delivering substantial real-world value, in a manner which is a significant improvement over existing technologies. The moment when promises have been fulfilled.
  1. Sufficient hardware capabilities.
  2. Sufficient algorithm sophistication to solve a real-world problem.
  3. Sufficient algorithm sophistication to solve the problem is a way that is dramatically superior to classical solutions.
  4. Sufficient application development sophistication to put the whole application together.

Maybe an ENIAC moment for each application category

  1. Characterization of a complex molecule.
  2. Characterization of a complex chemical reaction.
  3. Design of a new material.
  4. Design of a new drug.
  5. Optimization of a business process.
  6. A finance application.
  7. A dramatic advance in machine learning.

When Will Quantum Computing Have Its FORTRAN Moment?

ENIAC was still a laboratory curiosity

Possibly between the ENIAC and FORTRAN moments

The proof point for quantum computing

A few good applications

What can’t a quantum computer compute?

No, not all compute-intensive applications are appropriate for quantum computing

Intellectual property (IP) — boon or bane?

Open source is essential

Not yet a candidate for release from the lab

  1. Raise the prospect of release to begin considering whether the technology is ready for release from the lab.
  2. Go through a vetting process to determine if that preliminary decision is worthy of being finalized.

Yes, quantum computing remains a mere laboratory curiosity

No, quantum computing is not ready for prime-time production-scale applications

Hedge: Maybe some narrow niche applications

All of this could change with just a few key breakthroughs

Moment of truth — imminent deployment

Actual deployment vs. mere intent

Evaluation of deployment

Okay, but When?

  1. A lot of years of research are needed. 5? 7? 10? 12? 15? 20? Take your pick.
  2. Much basic hardware research is needed. How to build a better qubit. How to build a large number of qubits. How to connect them all.
  3. Much more basic research in quantum algorithms.
  4. Much more basic research in analyzing real-world problems and transforming them into a form that is amenable to the programming model of quantum circuits.
  5. Much more engineering research into building and controlling machines with large numbers of qubits.
  1. ENIAC moment in 5–7 years.
  2. Another 2–5 years to reach FORTRAN moment.
  3. 7–12 years total.
  4. Call it 10 years to have a round number.
  5. Maybe 5–7 years if we catch a bunch of lucky breaks.
  6. Maybe 12–15 years if we run into too many walls.
  7. Bottom line: 5 years as a most optimistic estimate. But don’t hold me to that!

Milestones from today to post-laboratory curiosity

  1. Numbers of qubits. 32, 48, 64, 72, 80, 96, 100, 128, 192, 256, 512, 1024, 2048, 4K, 8K, 16K, 32K, 64K, 128K, 256K, 512K, 1M, 2M, 4M, 16M.
  2. Algorithm improvements. Well beyond today.
  3. Advanced, high-level programming model.
  4. Sophisticated algorithmic building blocks.
  5. Design patterns. Some general. Some category or domain-specific.
  6. Application frameworks. Some general. Some category or domain-specific. But how much of this is needed for the ENIAC moment?
  7. Reached the ENIAC moment. A first credible production-scale real-world quantum application.
  8. High-level quantum programming language. Conceived and under development with preliminary experimentation and evaluation.
  9. Reached the FORTRAN moment. Widespread use, although may not have achieved deployment of applications under development.
  10. Done. Quantum computing is no longer considered a laboratory curiosity. Widespread use and a significant number of production-scale real-world quantum applications.

Moore’s law for qubits

  • Qubit count of general-purpose quantum computers will double roughly every one to two years, or roughly every 18 months on average.

Quantum ready

  • We are currently in a period of history when we can prepare for a future where quantum computers offer a clear computational advantage for solving important problems that are currently intractable. This is the “quantum ready” phase.
  • Think of it this way: What if everyone in the 1960s had a decade to prepare for PCs, from hardware to programming over the cloud, while they were still prototypes? In hindsight, we can all see that jumping in early would have been the right call. That’s where we are with quantum computing today. Now is the time to begin exploring what we can do with quantum computers, across a variety of potential applications. Those who wait until fault-tolerance might risk losing out on much nearer-term opportunities.

Quantum insurance

  1. Build out a large team, paying top dollar for elite technical staff, year after year after year, with no visibility on timing of payoff. Pray that your bosses are okay with such extreme pending.
  2. Dedicate a small team to simply keep an eye on the emerging sector, raising the flag when the technology is finally on the verge of being ready.
  3. Assign a fairly small number of senior technical staff to do the monitoring of the merging sector — on a part-time basis. Minimal cost, more of a distraction.
  4. Hire a consulting firm to brief you on the technology, at intervals.
  5. Hire a consulting firm to outsource development of a small number of exploratory research projects to determine if the technology is close to being ready for use.
  6. Do a little light reading (or attending seminars) periodically to monitor the field, but don’t even think about expending significant resources on the other five options until the technology finally does seem on the verge of practical application.

Setting expectations

  1. Too low or not at all. There’s no ready and enthusiastic audience or market to take the technology and run with it when it is ready. The technology may end up fizzling and dying off. Not a problem for quantum computing at this stage.
  2. Too high. Disappointment and outright disenchantment can set in. People may simply walk away in frustration when the technology doesn’t meet expectations and perform as expected. This is a real and looming problem for quantum computing at this stage, not in the sense of the technology failing, but simply that the technology isn’t close to being ready.

Papers, books, conferences, conventions, trade shows, seminars, online communities, and meetups

  1. Academic research.
  2. Experimentation.
  3. Evaluation.
  4. Proofs of concept.
  5. Prototypes.
  6. Interest in the technology.
  7. Discussions and interactions among potential users.
  8. Consultants.

Quantum volume

Beyond success of consultants

Critical mass of interest, but…

Need a critical mass of technology

Technological deficits

  1. Hardware — not enough qubits.
  2. Hardware — poor fidelity.
  3. Hardware — no clear sense of whether quantum error correction is essential or whether NISQ will be good enough.
  4. Hardware — only minimal circuit depth.
  5. Hardware — quite a few of the announcements are not yet available.
  6. No interesting level of algorithmic building blocks for building applications.
  7. Little in the way of design patterns.
  8. Need for application frameworks. Minimize reinvention of the wheel by each application.

The greatest challenges for quantum computing are hardware and algorithms

Not clear what the ideal qubit technology will be

The ideal qubit technology has not been invented yet

Hybrid applications — how best to blend quantum and classical computing

Google — no commercial machine yet

Microsoft and Intel — no machines yet

Honeywell — an initial splash, but follow-through needed

Rigetti — losing steam?

IonQ — some initial progress, but waiting for follow-through

IBM — lots of machines, but still too limited

Other machine vendors

How many qubits does a production system need?

  1. 128 qubits.
  2. 256 qubits.
  3. 512 qubits.
  4. 1K qubits.
  5. 2K qubits.
  6. 4K qubits.
  7. 8K qubits.
  8. 16K qubits.
  9. 32K qubits.
  10. 64K qubits.

Subsidiary technologies

  1. Independent technologies for designing and fabricating a quantum computer (e.g., a particular qubit technology.)
  2. Components which come together to produce a quantum computer. Including software.
  3. Components which are shared between distinct approaches to designing and fabricating a quantum computer.

Need a critical mass of algorithms and applications

Need a critical mass of algorithmic building blocks

Need a critical mass of design patterns

Need a critical mass of application frameworks

Is NISQ an obstacle?

Is quantum error correction needed?

What if quantum error correction is required?

  • What if quantum error correction is required?

Gate fidelity is important

What algorithm advances are needed?

  1. More advanced algorithms.
  2. More refined algorithmic building blocks.
  3. Richer programming model.
  4. Higher-level programming model.
  5. Design patterns.
  6. Application frameworks.
  7. Richer example applications.
  1. Much better hardware.
  2. Richer support for algorithms.
  3. Better algorithms.
  4. Applications based on those algorithms.
  5. Skill at translating application requirements into applications using quantum algorithms.

Quantum advantage

Need benchmarks for quantum advantage

Quantum advantage is mandatory

There’s no point to quantum computing without quantum advantage

Quantum supremacy

Didn’t Google achieve quantum supremacy?

Which application category will be first to achieve quantum advantage for a production-scale application?

When will a practical algorithm be implemented for more than 32 qubits?

Quantum advantage today: true random number generation

Need for higher performance quantum simulators

Need for a new model for design of scalable algorithms

Need to move beyond the lunatic fringe of early adopters

How scalable is your quantum algorithm or application?

  • How scalable is your quantum algorithm or application?

Do we need a universal quantum computer?

Quantum computer as a coprocessor

Tools and support software are essential

Need for Principles of Operation documentation and specifications

Need for detailed personas, use cases, access patterns

  • Personas. The many roles of individuals who will be involved in any way in the development and deployment of quantum computers and quantum applications.
  • Use cases. The many specific applications of quantum computing. Specific real-world problems to be solved.
  • Access patterns. How specifically quantum computing is used. Including design patterns, application frameworks, variational methods, hybrid quantum/classical applications, in-house hardware, remote and cloud access, simulators, etc.

How are companies using quantum computing today?

  1. Learning about the technology. Reading. Training. Attending conferences and seminars.
  2. Experimenting with the technology. Primitive hardware available today. Limited quantum simulators available today.
  3. Proofs of concept. At a very small scale.
  4. Prototypes. At a very small scale. Really just proofs of concept.
  5. Using quantum simulators. Easier to use and more configurable than real quantum computers. Can run some algorithms which don’t yet work on limited real hardware.
  6. Evaluation. Assessing whether the technology has value relative to the particular needs of a particular organization. Some of this comes before experimentation — looking at the experiences of others, and the rest comes after experimentation, proofs of concept, and prototypes — evaluating how well the results demonstrate delivering real-world value to the organization.
  7. Speaking at conferences and seminars. Relating their experimental results to date and elaborating on their expectations for future applications.

Isn’t Monte Carlo simulation good enough for most applications?

  1. The optimal solution. Or at least a better solution than the MCS solution?
  2. An acceptable result in much less time and resources — quantum advantage.

Quantum-inspired algorithms

What about D-Wave Systems?

  1. They have very few commercial customers. Granted, they now have a cloud-based remote access solution which does not require purchase of a complete system, but at least at this stage there is no evidence of any truly widespread usage.
  2. Even their 2000Q system with 2048 qubits is roughly a 45 x 45 grid, so it still can handle only fairly small problems.
  3. Even their upcoming Pegasus system with roughly 5000 qubits would support only roughly a 70 by 70 grid, still supporting only fairly small problems.
  4. The system supports a very constrained optimization algorithm. That may work well for a niche class of problems, but lacks the generality of universal gate-based quantum computers.

Is money a significant issue at all?

  1. Research. Basic research and applied research. And theory as well.
  2. Product engineering? I don’t think that’s a gating factor right now.
  3. Marketing? Ditto.
  4. Training? Ditto.
  5. Education? Some expansion of the talent pool is needed, especially for basic research, but it seems premature to puff up actual application development, deployment, and operation.
  6. Venture capital? Seems premature to me. Much more basic research is needed. It’s inappropriate to use venture capital to fund basic research. Venture capital should be reserved for developing products and services using off-the-shelf technology — technology that is no longer a laboratory curiosity, or a laboratory curiosity which is in fact ready for product development without further research.
  7. Strategic investment and joint ventures. Mostly too early, especially for applications with a short-term focus. Focus on applied research and algorithm research could be of significant value.

Is more venture capital needed?

Limited talent pool

  1. In the lab itself. For research.
  2. In product engineering. To develop products and services.
  3. In the field. For development and deployment of applications of the technology.

Repurpose existing technical talent

Obsession over Grover search algorithm even though not exponential advantage

Shor’s algorithm is still cited and treated as if it was already implemented even though very impractical

Can we expect quantum computing to cure cancer, hunger, poverty, and inequality?

  1. Discover new drugs.
  2. Develop new materials.
  3. Discover more efficient batteries.
  4. Optimize even the most difficult business problems.
  1. Discovery of new drugs for treating — and curing — cancer.
  2. Discovery of new food crops to boost food production even in areas with poor soil.
  3. Optimize economic and financial systems to more equitably distribute money and wealth.

Never underestimate the power of human cleverness and intuition

Would Rip Van Winkle miss much if he slept for the next 2 years? 5 years?

Will two or three years be enough? Very unlikely

Some say three to five years, but I don’t see it

Five years? Outside possibility, but still unlikely

Seven years? Maybe, if researchers finally get their acts together

Ten years? One would hope, but on the verge of being a zombie technology

Fifteen years? Seems like a slam dunk, but you never know

Twenty years? If not by then, maybe never?

Prospect of a quantum winter?

  • A technology winter is the period of disappointment, disillusionment, and loss of momentum which follows a period of intense hype and frenzy of frothy activity as grandiose promises fail to materialize in a fairly prompt manner. The winter is marked by dramatically lower activity, slower progress, and reduced funding for projects. The winter will persist until something changes, typically one or more key technological breakthroughs, emergence of enabling technologies, or a change in mindset which then initiates a renewed technology spring. The winter could last for years or even decades. A technology could go through any number of these cycles of euphoria and despair.

Mixed messages from the National Science Foundation (NSF)

  1. NSF is focused on research. That’s a very good thing.
  2. If NSF is involved, you better believe that there is a significant level of research required before commercial viability can be achieved.
  1. You’re going to live in a quantum future. Sooner than we may once have imagined
  2. But just how distant is this future?
  3. The shift to a quantum world won’t happen overnight.
  4. Today, we are on the cusp of a similar revolution…
  5. We can expect innovative applications of quantum principles to emerge at an accelerated pace
  6. over the next few decades.
  7. Making the quantum future a reality is a goal that researchers around the globe have long been working toward.
  8. Quantum is still an emerging area of science
  9. building technologies that harness its potential will require extensive, fundamental research to better understand the principles that drive it.
  10. The U.S. also needs a significantly larger, quantum-educated science and engineering workforce ready to develop, operate and maintain the quantum technologies of the future.
  11. For decades, the U.S. National Science Foundation has led strategic investments in foundational research and development that have jumpstarted the quantum revolution.
  12. NSF is working to address key scientific and technological challenges that must be overcome to unleash its full potential.
  13. At NSF, we’re working to bring you into that quantum future — faster.
  14. The quantum future grows nearer
  15. While still in the early phases of development
  16. One day, they will do more than simply function as faster and better computers
  17. QIS researchers have ambitious goals; and at every step of the way, they’re encountering new challenges that require resources and radical thinking to address.
  18. QIS has the potential to fundamentally revolutionize society, but only after some overarching challenges are addressed.
  19. Quantum computers are in development, but getting them to the point of commercial viability requires making them more reliable.
  20. a quantum computer has to be reliable to truly reach its potential.
  21. It needs quantum networks. And every component that goes into those networks faces scientific questions just as difficult as those that face quantum computers.
  22. NSF is tackling some of the big questions through its new Quantum Leap Challenge Institutes working to make sure that we’re ready to use quantum computers once they become more viable.
  23. We are on the cusp of a new quantum revolution
  24. NSF has been funding quantum research and education since the 1980s
  25. There are many more obstacles that we know about between us the quantum future — and even more we don’t and will encounter along the way.
  26. But by identifying these roadblocks and giving researchers the resources they need to remove them, NSF is accelerating the quantum revolution.

Ethical considerations

Regulatory considerations

Conclusions

  1. This year? No way.
  2. Next year? Ditto.
  3. 2–3 years? Very unlikely.
  4. 5 years? Outside possibility, but high risk.
  5. 7 years? Maybe. If researchers finally get their acts together.
  6. 10 years? One would hope! On the verge of being a zombie technology.
  7. 20 years? If not by then, maybe never.

What’s next?

  1. Waiting for the next dramatic breakthrough. Will it be sufficient to finally break out from being a mere laboratory curiosity? How many more dramatic breakthroughs will be required?
  2. Watching the endless stream of incremental progress. Like watching grass grow or paint dry. Rarely seems to amount to a significant breakthrough.
  3. Watching the flow of money, resources, and attention to basic research.
  4. Watching the progress on the algorithm front.
  5. Waiting for the ENIAC moment. A first substantial real app — with quantum advantage.
  6. Waiting for the FORTRAN moment. Making it easy to develop real apps — with quantum advantage.
  7. Waiting for evidence of quantum advantage.
  8. Waiting for evidence of true quantum supremacy for a real-world application with a non-trivial amount of data.
  9. Wondering which application and application category will be the first to finally make it clear that quantum computing is no longer a mere laboratory curiosity.
  10. Grow tired of people insisting that quantum computing is no longer a mere laboratory curiosity when it clearly is.

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Freelance Consultant

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Jack Krupansky

Jack Krupansky

Freelance Consultant

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