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?

What is a laboratory curiosity? I wrote another informal paper to define and explore the concept:

  • 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

From that longer definition we now have some more concrete criteria, such as:

  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

Quantum computing does satisfy some of those criteria, to some extent:

  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

Despite all of the hype, attention, interest, and enthusiasm, quantum computing is not even close to being ready to advance beyond being a mere laboratory curiosity.

Decent progress, but…

Yes, we’re seeing a lot of progress — both hardware and software.

Much research is needed

Much research has been done over the past 25 years, but much more 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

This is mostly related to the need for more research in general, but simply to highlight the point that many areas of quantum computing need to spend a lot more time in the lab before being ready to be considered for application to real-world problems and release to use in the real world.

When Will Quantum Computing Have Its ENIAC Moment?

When can we expect quantum computing to have advanced to a stage comparable to the public unveiling of ENIAC in 1946, when the future has finally arrived and become now, when a quantum computer is finally capable of solving a substantial, nontrivial, real-world computing problem with nontrivial amounts of data rather than being merely yet another promise and mere hint of a future to come, some day, but not real soon?

  • 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

The ENIAC moment for a technology only technically needs a single application, but for a technology which applies to multiple application categories, each category deserves its own ENIAC moment.

  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?

If the ENIAC moment for quantum computing establishes the raw technical feasibility of developing quantum applications which can deliver substantial real-world value, the FORTRAN moment will signify the moment when more widespread use of the technology is practical, and ultimately it is that widespread use which signifies that the technology is no longer a mere laboratory curiosity.

ENIAC was still a laboratory curiosity

As dramatic an advance as ENIAC was in 1946, it never really made it out of the laboratory. It did indeed perform a number of useful calculations for military applications, but it was eclipsed by a rapid succession of superior computer systems, including commercial products over the next five years.

Possibly between the ENIAC and FORTRAN moments

The FORTRAN moment would much more clearly usher in widespread adoption and use of quantum applications, but that’s not the absolute requirement for advancing beyond being a mere laboratory curiosity. Some intermediate stage between the ENIAC moment and the FORTRAN moment might actually be the sweet spot, where leading edge developers are actually able to build and deploy production-scale quantum applications which deliver substantial real-world value even if many would-be developers are still left out in the cold.

The proof point for quantum computing

As mentioned in the earlier paper, What Makes a Technology a Mere Laboratory Curiosity?, there will likely be a very clear turning point when it suddenly becomes crystal clear that the technology has been proven to work in some significant fashion and that it is only a matter of time before the remaining pieces of the puzzle fall into place. This would be the so-called proof point.

A few good applications

One possibility is that instead of immediate widespread commencement of application development from scratch, maybe a lot of organizations wait until a number of relatively generalized or standardized applications have been developed, so that they can copy, mimic, or even directly use quantum applications development by other organizations.

What can’t a quantum computer compute?

For all of the great applications of quantum computing, is there anything it can’t do? Well, quite a bit. Quantum computers are appropriate for any application where quantum parallelism can be exploited, but generally that means no complex logic. You must reduce your computation to the raw physics supported by qubits. Very little of what you can do in a classical programming language, even BASIC, can be readily transformed into a quantum program.

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

As noted in the preceding section and the linked paper, exploitation of quantum computing — quantum parallelism — requires a relatively simple algorithm which performs a relatively simple computation over a very large solution space. Any complex logic is out of the question. Many (most?) of the compute-intensive applications currently running on classical computers have relatively complex logic which cannot be readily transformed into the simple raw physics operations of qubits.

Intellectual property (IP) — boon or bane?

Intellectual property (IP) such as patents can cut both ways. The prospect of proprietary advantage is a fantastic incentive. But open source can be a huge advantage as well. If too much of the key technologies of quantum computing are locked up due to IP protections, innovation and adoption can be stifled or delayed.

Open source is essential

Open source technology, both software and hardware is the antithesis of private intellectual property. Access to source code and designs for algorithms, applications, tools and support software, and hardware can greatly accelerate progress for quantum computing. Researchers and product and application developers can rapidly build on the work of others.

Not yet a candidate for release from the lab

A two-step process is needed before committing to release a technology 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

Despite all of the progress over the past 25 years, quantum computing remains a mere laboratory curiosity. The many technological advances and many small proofs of concept and prototypes simply haven’t reached the critical mass to deliver substantial real-world value for production-scale real-world applications.

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

Despite great progress over the past five years, quantum computing is not even close to being ready for prime-time production-scale applications.

Hedge: Maybe some narrow niche applications

Although I’m confident that quantum computing is not yet ready for general production-scale deployment, it may turn out that there might be some very narrow niche applications for which even present-day (or near future) NISQ computers in the lab are sufficient for those particular niches.

All of this could change with just a few key breakthroughs

Much of what is said in this paper is based on progress to date on quantum computing. Granted, it’s always risky to project the future based on the past, but it’s equally risky to project the future on mere speculation. And it’s definitely risky to project the future based on over-rosy optimism.

Moment of truth — imminent deployment

The ultimate moment people are waiting for is the moment of truth, the moment when a quantum computer is ready for imminent deployment and is about to be switched on or placed online and the intended application can commence. Will it work as hoped and planned, or stumble badly, or sort-of work but in a mediocre manner, or function properly but fail to deliver dramatic quantum advantage?

Actual deployment vs. mere intent

Imminent deployment is certainly a fantastic milestone to achieve, but it is actual deployment which is the true milestone, the true culmination of the development process for a quantum computer and applications. The true moment when the rubber actually does hit the road and it becomes crystal clear whether the technology has indeed advanced beyond being a mere laboratory curiosity.

Evaluation of deployment

Even then, once deployment has occurred, there is still more work to do. Deployment simply means that the quantum computer and applications are available for use. There is still the need to see actual evidence of use by actual customers and users in meaningful and relevant use cases. Does it work as hoped and planned, or stumble badly, or sort-of work but in a mediocre manner? Is the anticipated quantum advantage demonstrated as expected?

Okay, but When?

I’ve covered so many of the issues and obstacles, but the headline question remains unanswered: When? When will quantum computing finally advance from being a mere laboratory curiosity?

  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

It feels premature to suggest a detailed and credible sequence of milestones — let alone dates — for how to advance from today to the moment of no longer being a mere laboratory curiosity, but some possible milestones might include:

  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

How many years may it take to advance to a desired number of qubits? I have my own variation of Moore’s Law which basically says that qubit capacity of quantum computers will double every one to two years. Put simply:

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

Quantum ready

There is this marketing notion of Quantum Ready, that the technology of quantum computing may not be ready today or even in the next few years, but that due to the steep learning curve organizations must endeavor to get started now with education, training, experimentation, and proofs of concept and prototyping so that they will be up to speed and ready to hit the ground running when the required technology finally does become available.

  • 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

Quantum insurance is essentially the same concept as Quantum Ready but putting emphasis on the risk and potential cost and lost revenue and competitive disadvantage from being left behind if quantum computing somehow manages to surge ahead without you noticing or paying attention.

  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

One way or another, people will develop expectations for a new technology. Better to be proactive and set expectations for them. But the challenge is to set expectations in a realistic manner. That’s a huge challenge for quantum computing.

  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

Papers, books, conferences, conventions, trade shows, seminars, online communities, and meetups are all expected for a commercially successful product or service, but although they may be necessary they are not per se sufficient to prove that a technology is no longer a mere laboratory curiosity. This has already been proven true for quantum computing.

  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

Another marketing concept for quantum computers is quantum volume which provides a somewhat vague and general notion of how powerful a quantum computer is, permitting a vague, rough comparison of two or more quantum computers. Unfortunately, although it allows people to make statements such as “quantum computer ABC is twice as powerful as quantum computer XYZ”, it doesn’t provide any specific, actionable information to either the designers of quantum algorithms or the developers or quantum applications. And for the purposes of this paper a quantum volume metric tells us little if anything about how powerful a quantum computer will be needed to finally get quantum computing out of the lab and into delivering production-scale real-world value.

Beyond success of consultants

Although the technology may not be ready, consultants are always ready. Consultants can make money at any stage of development. Being a mere laboratory curiosity may only enhance the need for knowledgeable consultants.

Critical mass of interest, but…

It’s quite clear that there is a substantial groundswell of interest in quantum computing. A literal critical mass of interest has been reached, but… the actual technology just isn’t ready yet. All dressed up but no place to go. That won’t be sustainable for too long. How quickly will technology catch up?

Need a critical mass of technology

There are simply too many technological deficits at present to do anything of practical value with quantum computing today. There simply isn’t a critical mass of technology in place today.

Technological deficits

It’s simply not possible to achieve a critical mass of technology if there are so many technological deficits. As mentioned earlier, some of the technological deficits are:

  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

For much more depth on technological deficits, read this paper:

Not clear what the ideal qubit technology will be

The single biggest technological deficit on the hardware front may be that we don’t yet have a handle on what the ideal qubit technology might be. I’m not so optimistic about current superconducting transmon qubit technology, and even trapped-ion qubits may not be enough to get us to the stage where quantum computing is ready to leave the lab and no longer be a mere laboratory curiosity.

The ideal qubit technology has not been invented yet

My personal gut feeling is that the ideal qubit technology has not been invented yet. I could be wrong, but I’m currently not prepared to bet that what we have today will be sufficient for much more than a mere laboratory curiosity.

Hybrid applications — how best to blend quantum and classical computing

Generally, any application utilizing a quantum computing will be a hybrid application since a quantum computer doesn’t have the capabilities for most application operations, such as I/O, database access, network access, and user interface.

Google — no commercial machine yet

Google has certainly succeeded in producing a quantum computer in the laboratory, but they haven’t managed to bring that technology to market, nor have they announced any commercial plans or a roadmap.

Microsoft and Intel — no machines yet

Both Microsoft and Intel have announced intentions to design and build quantum computers, but so far they haven’t yet produced a machine in a laboratory.

Honeywell — an initial splash, but follow-through needed

Honeywell has announced ambitious intentions and appears to have a small machine in the lab with ambitious plans to rapidly evolve to larger machines, but so far all they have is a machine in a lab, albeit with remote access.

Rigetti — losing steam?

Besides IBM, Rigetti Computing was one of the early leaders getting a sequence of machines up and running (in the lab), but lately their momentum seems to have dwindled. They announced intentions to produce a 128-qubit machine “over the next year”, but that was two years ago, and now their most capable machine has 31 qubits.

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

Before Honeywell’s recent announcement of a trapped-ion quantum computing system, IonQ was the only hardware vendor pursuing this alternative to superconducting transmon qubits. They have some interesting hardware in their labs, but they need to demonstrate some serious follow-through, real soon, lest they begin to lose momentum.

IBM — lots of machines, but still too limited

IBM has been working on quantum computing for over 25 years. They currently have, at last count, 18 quantum computers running in their labs. And they are certainly in the lead, but that’s not saying that much in a sector where everybody is plagued with dramatic technological deficits that need to be overcome. IBM continues to make progress, but their machines are still too limited relative to what might be needed to deliver substantial real-world value for production-scale practical real-world applications.

Other machine vendors

There are some number of stealth vendors and some relatively new smaller vendors of quantum computers who claim or are reported to be working on new and exotic machine architectures, but the bottom line is that their machines are either still laboratory curiosities or not even yet laboratory curiosities.

How many qubits does a production system need?

At this stage we have no visibility as to how many qubits might be needed to achieve the level of performance, capacity, and quantum advantage needed to demonstrate production-scale real-world applications comparable to achieving the ENIAC or FORTRAN moments of classical computing.

  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

This paper generally couches quantum computing as a singular, monolithic entity, but the reality is that it is an umbrella concept with a variety of subsidiary technologies under that overarching umbrella, which may come in three forms:

  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

Even once the raw technological deficits have been identified and addressed, there is a need for a critical mass of algorithms and applications which use those algorithms.

Need a critical mass of algorithmic building blocks

We can’t achieve a critical mass of algorithms and applications without first achieving a critical mass of algorithmic building blocks.

Need a critical mass of design patterns

Even with a critical mass of hardware and algorithmic building blocks, we need a critical mass of design patterns for best practice for assembling the building blocks into sophisticated algorithms.

Need a critical mass of application frameworks

Progress will be slow if every application developer must develop each application from scratch. There will likely be a fair amount of common functions and features in many applications. Much of that common logic can be factored out of each application and transformed into an application framework where each application developer can start by standing on the shoulders of the framework developers and then focus on the unique portions of their own application.

Is NISQ an obstacle?

Reliable qubits and gate operations would certainly help a lot, but are noise, errors, limited coherence, and environmental interference a key technical obstacle holding back quantum computing and preventing it from advancing from being a mere laboratory curiosity? Sure, to at least some degree, but it’s more complicated than that.

Is quantum error correction needed?

It’s unclear whether quantum error correction (QEC) might be required before quantum computing can successfully make the leap from a mere laboratory curiosity to a commercial product delivering production-scale real-world value.

What if quantum error correction is required?

Personally, I suspect that many quantum applications will be able to get by without full-blown quantum error correction (QEC) as qubit and gate fidelity incrementally improve, but I may be wrong. Designers of quantum algorithms and quantum applications and the the companies, laboratories, organizations, and agencies which are committing to utilizing them need to ask themselves this fundamental critical question:

  • What if quantum error correction is required?

Gate fidelity is important

Even if or when quantum error correction or relatively high-fidelity qubits become available, it will be for naught if the hardware and firmware are unable to make similar improvements in gate fidelity.

What algorithm advances are needed?

Beyond better hardware, algorithms hold the key, but algorithms are not free, cheap, or easy. We need:

  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

Right now, people are struggling just to get quantum computers to function at all for anything beyond the most trivial algorithms, let alone tackle meaningful applications. But the goal, the whole purpose for quantum computing is to achieve quantum advantage — performance that is so dramatic that it far exceeds anything that even the fastest classical supercomputers can achieve. Alas, at present, we still have no meaningful examples of quantum algorithms for practical real-world applications which actually outperform classical solutions in a truly dramatic manner.

Need benchmarks for quantum advantage

We aren’t even close to being able to consider what algorithms or applications to consider as benchmarks for concluding that a lab-based quantum computer is finally ready to be released into the real world, other for the types of evaluation and experimentation that we are currently doing.

Quantum advantage is mandatory

The bottom line is that quantum advantage is a mandatory requirement for quantum computing to advance beyond being a mere laboratory curiosity.

There’s no point to quantum computing without quantum advantage

Just to hammer the point home, quantum advantage isn’t just mandatory because it’s nice or beneficial, but because it’s the only reason why it’s worth pursuing quantum computing.

Quantum supremacy

Quantum advantage and quantum supremacy are related and sometimes used as synonyms. The key difference is that while a quantum advantage means exactly that — a quantum solution dramatically outperforms a classical solution to the same problem — an advantage, while quantum supremacy means that quantum computing can offer solutions while classical computing cannot even offer a solution since the computational complexity is too high (exponential) and may take many centuries to complete even if that were possible.

Didn’t Google achieve quantum supremacy?

Yes, technically Google did achieve quantum supremacy, but with a narrow technical niche which at present doesn’t seem to apply to any of the broad categories of applications for which quantum computing is seen as a potential solution. As such, Google’s achievement doesn’t result in quantum computing advancing from being a mere laboratory curiosity. In fact, Google’s achievement itself is the epitome of a laboratory curiosity.

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

It is completely unknown and unpredictable which application category might be the first to achieve quantum advantage for a production-scale application which delivers substantial real-world value.

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

Most of the currently published algorithms use a relatively small number of qubits, rarely more than a dozen and very rarely over twenty. It’s simply not possible to get a dramatic quantum advantage using such a small number of qubits. I just saw a paper which uses 28 qubits — that’s the most I’ve seen to date, but still not that large. We need to start seeing quantum algorithms using well more than 32 qubits on a regular basis before we can even begin expecting to see something resembling quantum advantage.

Quantum advantage today: true random number generation

There is one useful function that a classical Turing machine can’t compute, even theoretically: true random number generation. By definition, Turing machines calculate deterministic results — 2 plus 2 is always 4. Programmers must resort to a variety of clever contortions to even approximate random number generation, at best producing so-called pseudo-random numbers.

Need for higher performance quantum simulators

It’s fairly easy to simulate a small quantum computer, but once the number of qubits gets larger it gets increasingly harder — exponentially harder — since the number of quantum states rises exponentially with the number of qubits — 2^n. Even a mere 20 qubits require 2²⁰ or a million quantum states. 32 qubits would require 2³² or four billion quantum states. 40 qubits would require 2⁴⁰ or a trillion quantum states. That’s approaching the practical limits for today’s quantum simulators. 64 qubits would require 2⁶⁴ or millions of trillions of quantum states, far beyond current systems. And that’s just getting started for getting to the number of qubits needed for practical applications, whether that’s 72, 96, 128, 256, 512, 1024, or even more.

Need for a new model for design of scalable algorithms

One of the things that is desperately needed is a much more robust model for quantum algorithms which would in fact allow an algorithm to be tested and simulated on a smaller number of qubits with the knowledge and expectation that the algorithm can be reliably scaled up to a significantly higher number of qubits. For example, test and simulate with eight to 32 qubits with an expectation of scaling up to 64, 128, 256, or even 1024 or more qubits without running into scaling issues on real hardware. Today this is not practical or even theoretically feasible with current hardware or algorithm technology.

Need to move beyond the lunatic fringe of early adopters

Every technology needs early adopters, but the earliest adopters commonly won’t be representative of the main audience for the technology. Commonly the earliest adopters are really merely the lunatic fringe, the elite individuals and elite organizations which are able to accept and work with a new technology in its crudest and least-developed form, well before it is ready to be used and exploited fully by mere-mortal users.

How scalable is your quantum algorithm or application?

I have my doubts about the scalability of most current quantum algorithms and quantum applications. Doubt is putting it charitably. It’s clear to me that virtually none of the current algorithms will scale reasonably from their current state to hundreds or thousands of qubits.

  • How scalable is your quantum algorithm or application?

Do we need a universal quantum computer?

It would be great to have a true universal quantum computer — which combines all of the features of a classical computer with all of the features of a quantum computer with no latency or delay between classical and quantum operations, but I believe that is more of a long-term aspirational goal rather than a requirement for the simpler goal of advancing quantum computing beyond being a mere laboratory curiosity.

Quantum computer as a coprocessor

At present, a quantum computer is simply a coprocessor for a classical computer — classical code prepares a quantum circuit, input data, and parameters for a quantum algorithm, hands the prepared circuit off to an attached or remote quantum processor for execution, and then post-processes the results — measured qubits — from the quantum algorithm using more classical code.

Tools and support software are essential

Software tools and support software are critical for effective use of quantum computing.

Need for Principles of Operation documentation and specifications

Current documentation of current quantum computers is mediocre and spotty at best. It simply illustrates the degree to which current systems are still stuck in the lab and haven’t undergone a full and complete product development engineering process.

Need for detailed personas, use cases, access patterns

The quantum computing sector needs a rich, comprehensive, complete, clear, concise, and detailed elaboration of personas, use cases and 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?

Quantum computing is definitely in the news a lot these days and a lot of companies are talking a lot about it, but what are companies actually doing with quantum computing — other than the vendors who are developing and providing access to machines in their laboratories? For the most part they are getting ready for quantum computing:

  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?

A traditional approach to problems with a combinatorial explosion of possible solutions is to take a statistical approach such as Monte Carlo simulation (MCS). The results may not be optimal, but with careful attention to heuristics and a little patience it is not uncommon to get results which are good enough for the immediate need for many 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

The discovery or development of a quantum algorithm tends to require such out of the box thinking that the process might uncover or suggest an alternative approach to a classical implementation which performs much better than a more direct classical solution.

What about D-Wave Systems?

D-Wave Systems has had a succession of commercial quantum computing products. Shouldn’t they count as commercial quantum computers? Possibly. Probably. Maybe.

  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?

Money is always an issue, but is it the gating factor at this stage of quantum computing? No and yes — I don’t think money alone is the reason quantum computing is still a mere laboratory curiosity, but a lot more money focused on the right areas could indeed make a difference, even if not within the next few years.

  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?

As just mentioned, no, availability of venture capital is not a gating factor at this stage of development of the quantum computing sector. Much more research is needed. Once research for hardware and algorithms has advanced through some indeterminate number of additional milestones, only then will it be appropriate for venture capital to pay attention to quantum computing.

Limited talent pool

Progress at any stage of research, development, and uptake of a new technology can be severely constrained by a limited talent pool of technical staff needed to work on and utilize the new technology, especially for very advanced technologies such as quantum computing which require the expertise of elite scientific and technical disciplines.

  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

My advice to enterprises seeking talent in quantum computing for the purpose of investigating applications is to repurpose a small fraction of your existing in-house technical talent on a part-time basis. Unless you’re a very large organization with very deep pockets or a niche tech firm focused on quantum computing, it just won’t make sense to attempt to build a full quantum-only technical team. Instead, select a few of your best technical staff, send them to training, assign them to do some reading, give them time and resources to experiment with and evaluate the technology — on a part-time basis.

Obsession over Grover search algorithm even though not exponential advantage

As noted earlier, we don’t have a reasonable and robust collection of basic algorithms or algorithmic building blocks to serve as the foundation for development of quantum applications. One of the early algorithms developed by researchers was the Grover search algorithm. It’s somewhat interesting but it’s not a great example to base the foundation of quantum computing since it provides only a quadratic speedup, not the exponential speedup which is supposed to be the hallmark of quantum computing.

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

In theory, or at least as claimed, Shor’s algorithm should be able to factor very large semiprime numbers such as 4096-bit public encryption keys. But it’s hardware requirements are so extreme and there are so many questions about its feasibility that it is not a credible example of quantum computing either today, the next few years, five years, or even longer. Nonetheless, even recent academic papers continue to cite Shor’s algorithm as if it were practical and in fact as if it already had been implemented in full, which it has not.

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

Seriously, so many people are talking as if quantum computing will be able to solve many hard problems which classical computers are unable to address. In fact, the current presumption seems to be that any problem which can’t be solved by a classical computer can be solved using a quantum computer.

  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

I’m a big fan of being methodical and disciplined, but sometimes plain old simple human cleverness or intuition can achieve results which far surpass the most methodical and disciplined efforts of even diligent professionals.

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

How much might a modern-day Rip Van Winkle miss if he drank too much fermented quantum Kool-Aid and fell asleep for the next two (or three or five) years? I’d say not much.

Will two or three years be enough? Very unlikely

I honestly don’t see any promising path to advancing quantum computing beyond being a mere laboratory curiosity over the next two years.

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

Some people are aggressive optimists and forecasting that quantum computing will be ready for practical applications in three to five years. I don’t see it.

Five years? Outside possibility, but still unlikely

I can imagine some pathways to dramatic advances in quantum computing over the next five years, but once again the technology has a very long way to go, so I see quantum computing still as a laboratory curiosity in five years.

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

Maybe another two years will do the trick. It’s very possible, but I still rate it as not a slam dunk.

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

Ten years feels like a much more credible time frame for finally seeing at least a few production-scale practical real-world applications which actually deliver substantial real-world value, signifying that quantum computing has finally advanced from being a mere laboratory curiosity.

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

I’d be very surprised if quantum computing wasn’t a mainstream technology in fifteen years, but… you never know.

Twenty years? If not by then, maybe never?

Personally, I’d be expecting a universal quantum computer in twenty years which fully merges classical and quantum computing, as well as photonic computing and room temperature operation. Actually, I’d hope for and expect a universal quantum computer sooner, like 12–15 years.

Prospect of a quantum winter?

The notion of a technology going through a winter, such as an AI winter, could become a very real concern for quantum computing. What defines a winter for a technology?

  • 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)

You don’t have to accept my word on the need for a lot more research and that commercially-viability of quantum computing is not imminent. I’ve extracted a long list of key phases from a description which the National Science Foundation (NSF) just recently posted on their website. Their rhetoric is somewhat confusing and conflicting, sending mixed messages, buoyant with hope, promises, and anticipation, but, to their credit, loaded with many caveats which match a lot of my own sentiments.

  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

As noted in the underlying What Makes a Technology a Mere Laboratory Curiosity? paper, there will always be ethical considerations for any technology which is going to be introduced into the real world. Quantum computing is not immune from such considerations. Ethical considerations should of course be considered before transitioning quantum computing from being a mere laboratory curiosity to being accessible in the real world.

Regulatory considerations

Generally, any technology advancing out of the laboratory will need to be evaluated with respect to government regulatory requirements, if any apply. The point here is that it may not be possible for a technology to advance from being a mere laboratory curiosity until it successfully complies with all relevant regulations and gains any required regulatory approval.

Conclusions

Personally, I really do see quantum computing as still being a mere laboratory curiosity.

  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.

--

--

Freelance Consultant

Love podcasts or audiobooks? Learn on the go with our new app.

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store