Future Topics for My Writing on Quantum Computing

325 min readOct 8, 2019

This informal paper lists many of the topics I may be writing on related to quantum computing in the coming months and years, and even decades. And on quantum information science in general or indeed anything quantum, including quantum mechanics.

Minor caveat: My primary intention for this informal paper is for my own personal benefit, to simply capture, organize, maintain, and review my list of potential writing topics for quantum computing for my own review. Hopefully others will find my list of topics enlightening as well, but my only real goal is to facilitate my own writing. That said, I did try to put in a reasonable effort to make the list readable by others as well.

Update #2 — May 16, 2021:

Added a leading section My current active topics which is a short list of the topics which I am either currently working on or hope to begin working on soon. It’s always a horse race as to which of these topics makes it to completion first. Sometimes I get bogged down in a topic and then switch to another topic where I feel like I can make more rapid progress — or has more interest for me.

Update #1 — May 6, 2021:

The original version of this list, posted in October 2019, had 308 topics — they are mostly intact as before, but many additional topics have been added, bringing the total to over 1,000 topics. Some of the existing topic titles or notes may have been edited as well, but generally the existing topics are the same.

I spent over two months poring through hundreds of pages of my quantum-related notes, culling out topics that I had thought about over the past four years.
In addition, there is now a Top 25 topics list as well as a Runner-Up topics list (about 75 topics) which precede the full list. Those two lists highlight topics which feel more likely to get earlier attention in my thinking and writing. The full list (“Full list of topics”) includes all of the top topics.
There is also a third extra section, Additional runner-up topics, with close to 100 topics. I know it’s extra clutter since they’re already on the main list, but I just couldn’t bring myself to refrain from highlighting them as higher priority than merely being on the main list. I’m hoping to winnow this list down over time. Although every time I think I have my lists under control they just seem to grow even more!
In truth, all 200 or so topics on the three Top Topic lists are equally fair game when I’m choosing a next topic to write about.
The Top Topic lists will be fluid as priorities and interests change, while the full list will remain mostly static (other than occasional content updates), with new topics added at the end chronologically.
Looking for the new arrivals on the list, my latest thinking? Jump to the end of the main list (full list — “Full list of topics”) to find the most recent additions to the list. The last 10, 20, 50, or 100 topics on the list will give you a sense of my most recent thinking. Although I also sometimes update the notes for older topics on the list, but not very frequently.

My ultimate vision for all of this

My ultimate vision for this list of topics on quantum computing is not that I succeed at writing about many, most, or all of these topics, but rather that the underlying quantum computing sector, its underlying science and technology, and its community and ecosystem advance to the stage of true practical quantum computing where the simple truth is that my writing on any or all of these topics is simply no longer needed or even relevant — that visions are supplanted by actual reality.

All that I am really doing is filling in the myriad gaps in the underlying quantum computing sector, its underlying science and technology, and its community and ecosystem. Once all of these gaps have been adequately filled by actual and adequate advances, me and my work will no longer be needed. It’s that simple! If only that were true! Maybe, some day! They do say that hope springs eternal.

Preface

I have no idea in what order I’ll tackle my remaining writing ideas. In fact, I commonly come up with other topics and write about them first.

Readers should feel free to comment on what they would find most helpful in the near term. And suggest additional topics as well. And also cite existing online writing which might seem to at least partially satisfy any of the listed topics.

There is no particular order to this list, just as they came to mind and as I collected them from old notes of the past four years.

Currently I am doing a fair amount more reading and research than writing, particularly on the quantum mechanics and physics side of the house, but I hope to get back to writing more purely about quantum computing within a few months. Or not — I may take a longer break, three months, six months, a year, or maybe even 18 months to give the field time to catch up with my writing.

Many of my proposed writing topics in fact require significant reading and research before I’ll be prepared to write in any detail about the topic. In fact, in some cases it may be more appropriate for someone else to write on some of these topics. Either way, the presence of a topic on my list indicates a strong interest on my part that the topic be written about, whether by me or someone else.

I have already written fairly extensively on many of these topics, but my real goal is to take it to the next level, the next level of depth. There’s too much superficial hype, useless jargon, and extraneous detail floating around; my goal is to dig down to real, actual ground truth. And to limit myself to plain language to the maximal extent possible — minimal Greek, symbols, and math.

In a lot of cases I don’t yet know enough to write with any true sense of authority. I view research, reading, and writing as one integrated super-activity. If I find that I have gaps in knowledge when writing, that motivates me to do more reading and research to endeavor to fill those gaps. Writing is a good test of how complete and deep your knowledge really is.

Ultimately, I think I’d like to see 50 to 100 “What is…?” papers to cover the breadth of key concepts of quantum computing. That might be suited to be a full book, but I’m more interested in having easily digested chunks of free online material than a physical book.

There is another, shorter, list at the end of topics that I have no intention of writing about.

This is a living list and will grow on an almost-daily basis.

My interests — capabilities, limitations, and issues

My writing about quantum computing revolves around my interests in quantum computing — I’m a technologist, not a researcher or application developer, so I am most interested in the capabilities of quantum computing, its limitations, and any and all issues related to quantum computing. And quantum information science as well, and quantum mechanics as well, to some degree. I’m less interested in hands-on issues, specific applications, deep theory, or hard-core math.

I actually wrote an informal paper to discuss my interests:

My Interests in Quantum Computing: Its Capabilities, Limitations, and Issues

That should give a sense of why the topics listed in this paper were included.

Simple text only

Since I am a text-only guy, I won’t be publishing pictures or photos, diagrams, charts, tables, artwork, or complex mathematical formulas or anything involving lots of Greek letters or mathematical symbols. Just plain-language text and bullet points.

That also means I won’t be writing detailed papers on topics which require complex math, lots of Greek letters, symbols, etc.

Blog posts?

Some of the topics may in fact be suitable for relatively simple blog posts, but generally I will endeavor to cover topics to a depth and level of detail far beyond what makes sense for a typical blog post. I rarely write less than ten to thirty pages on a given topic, and sometimes fify to a hundred or more pages — hardly appropriate for a blog post. I’ll leave normal blogging (a few paragraphs, a page or two) to others.

FAQ?

Many of the topics are couched as questions and at least superficially seem appropriate for an FAQ, but generally my intention is to cover topics to a level of depth and detail far beyond a typical FAQ entry. A brief summary for some topics may indeed be appropriate for an FAQ — and some day I may in fact do a true FAQ, but that’s not my intention at this stage, and not my intention for questions listed here as topics for writing.

Long form writing on Medium

All of my writing is posted on Medium, a perfect medium for long-form writing (as opposed to blogging.)

Topics for others as well

Although all of the topics I list here are my own interests, others are certainly free to research and write about them as well — with or without my permission.

Quantum information science in general a well

My writing interests are not limited to quantum computing proper, but also over all of quantum information science, including quantum communication, quantum networking, quantum metrology, and quantum sensing. And quantum mechanics and quantum effects as well. Anything related to quantum.

For a general overview of quantum information science, check out:

What Is Quantum Information Science?

My existing writing

First, here’s a full list of my existing writing related to quantum computing:

List of My Papers on Quantum Computing

My list of questions

I’ve also compiled a separate list of questions that I have about quantum computing. Some of those questions also show up as topics here in this paper. Generally a question will become a topic here if an answer to the question is likely to be much more extensive than a relatively brief paragraph. And even if any of the questions are not listed here, they are still also fair game for future writing topics for me — or someone else. That separate list of questions can be found here:

Questions About Quantum Computing

Ripe to write?

Generally, topics will sit on my list indefinitely until they become ripe to write, meaning that a critical mass of my own interest, my own knowledge, and relevance to the community has been achieved. Generally, all three of those factors have to coalesce and converge before I’ll begin to tackle a topic.

Sure, I may make preliminary notes or start any number of topics, but those preliminary efforts don’t always bear fruit.

Everything I write is informal

Some of my interests are worthy of formal treatment in a formal paper, but formality is not my thing. I’ll leave formal academic papers to academics and researchers. I try to focus on the content that I feel is important, and not worry about the formal structure of an academic paper, formal citations, peer review, etc.

My goal is simply to identify key ideas and concepts and express them as simply, concisely, clearly, and completely as I can muster.

I’m especially interested in nuances and finer details, which I feel are woefully under-covered in the quantum computing sector, even in lofty, peer-reviewed academic papers. That’s what motivates a lot of my questions.

New arrivals — most recent additions to the list

Looking for the new arrivals on the list, my latest thinking? Jump to the end of the main list (full list — “Full list of topics”) to find the most recent additions to the list. The list is maintained in strict chronological order.

The last 10, 20, 50, or 100 topics on the main list will give you a sense of my most recent thinking.

Topics not under consideration

My intention is to remain focused on capabilities, ideas, limitations, issues, principles, and theory, so here are the topics that I have no intention of writing about:

Hands-on tutorials.
Hands-on details of particular quantum computers.
Hands-on details of particular algorithms. With some rare exceptions.
Hands-on details of particular quantum circuits. With some rare exceptions.
Hands-on details of particular class libraries and APIs.
Hands-on details of use of particular programming languages.
Common, popular, and favorite circuit patterns and quantum programming pearls. I’ll discuss the need, but stay away from the details.
Details of specific algorithmic building blocks. I’ll discuss the need, but stay away from the details. With some rare exceptions.
Physical details of particular quantum computers. Including specifications, timing, and performance.
Details of particular unitary transforms or particular quantum logic gates. With some rare exceptions.
Details of support software.
“How to…” In general. Some rare exceptions.
Funding and grant announcements. Unless something especially noteworthy.
Announcements of partnerships and deals.
Press releases. Unless something especially noteworthy.
Company news. Unless something especially noteworthy.
Marketing.
Market studies and projections.
Competitive comparisons of vendors.
Benchmarking of particular quantum computers, or benchmarking comparisons of various quantum computers.

Book(s)?

Every once in a while I contemplate writing a book about quantum computing. Some of my “informal” papers are literally book-length. And there are probably small collections of related papers which conceivably could be of value if published as books.

Some topic areas that might make sense for me as books:

Introduction to quantum computing.
What Is Quantum Computing?
Introduction to quantum information science and quantum effects.
The Greatest Challenges for Quantum Computing Are Hardware and Algorithms.
Quantum computing for managers and executives.

So far, reason has prevailed and I have refrained from attempting to turn any of my writing about quantum computing into one or more books.

Still, the thought persists in the darker recesses at the back of my mind.

One of these days… but not any day soon… hopefully.

My last hurrah?

I’ve already done a ton of writing about quantum computing. I’m well ahead of the state of the art, so it might be beneficial for me to hit pause and hold off on too much additional writing until research and product development catches up to at least some degree. But I will continue writing until I feel that I don’t have anything else that I feel an urgent need to say.

My current expectation is that I may write another five to twenty informal papers before pausing. That’s not a precise or specific goal, just a rough expectation. And very subject to change.

Even if I do pause overall, I may still find occasion to write something, just not as frequently.

My current active topics

This is a short list of the topics which I am either currently working on or hope to begin working on soon. It’s always a horse race as to which of these topics makes it to completion first. Sometimes I get bogged down in a topic and then switch to another topic where I feel like I can make more rapid progress — or has more interest for me. This list will be updated fairly frequently, particularly as I complete one topic and begin working on the next.

Topics may be on my Top 25 list but not this list if my interest is high but the topic is not quite ripe for writing. For example, they may require significant reading, research, or thinking first.

Only the topic titles are given here. Additional notes can be found in the top, runner-up, and full lists.

[Note: My writing is on pause right now and for the indefinite future. This list remains as an indication of my future writing if and when I might resume writing.]

Probability plus statistics equals approximate determinism.
What are you waiting for in quantum computing?
Adventures in quantum computing toy land — can we ever escape; can we checkout but never leave?
Does the lack of fine granularity of phase doom Shor’s algorithm for cracking large encryption keys?
What is quantum parallelism?
Quantum parallelism is the everything of quantum computing.
What can you do with a quantum computer today?
The solution to hype is more research, and lots of it.
The seven main quantum application categories.
Developing quantum computing intuition.
Where Are All of the 32-qubit, 28-qubit, and Even 24-qubit Quantum Algorithms?
Lessons from Theranos and Elizabeth Holmes: Is quantum computing trying too hard to “fake it until you make it” and projecting an unfaltering confidence and exaggerating a possibly unfulfillable promise that the technology will change the world?
Soliciting nominations for best quantum algorithm.
What specific quantum hardware advances does your quantum algorithm need to take you to the next level of functional capability?
Quantum algorithm designers and quantum application developers are the top two personas for quantum computing.
How can average non-elite quantum application developers best gain access to quantum parallelism and achieve significant quantum advantage?
Intellectual property (IP) — is a proprietary interest helpful or harmful?
What is quantum expertise?
Model for stages of adoption for a new technology.
What will quantum computing be when it grows up?
No point to quantum computing until and unless dramatic quantum advantage is achieved.
Will quantum computing be effectively useless until quantum phase estimation and quantum Fourier transform are practical at production-scale?
Fine granularity of phase and probability amplitude is the #1 technical obstacle to achieving quantum advantage.
Are quantum Fourier transform (QFT) and quantum phase estimation (QPE) required to achieve quantum advantage?
Are quantum Fourier transform (QFT) and quantum phase estimation (QPE) the Holy Grail of quantum computing for achieving quantum parallelism?
What might the initial commercialization stage of quantum computing — C1.0 — look like?
Reality of quantum computing now and in the near-term.
What should people be expecting to see in quantum computers in five years?
What might you do with 1,000 qubits?
What are quantum computers good for?
Quantum computing needs a massive reset.
Can a NISQ quantum computer ever achieve dramatic quantum advantage?
Need to focus on near-perfect qubits.
Why NISQ starts at 50 qubits.
NISQ is a misnomer.
No, most current quantum computers are technically not true NISQ devices.
What do senior executives need to know about quantum computing?
What corporate managers and executives need to know about quantum computing.
Cautions for enterprise managers and executives when contemplating quantum computing investments.
Thoughts on probability, stochastic processes, statistical aggregation, and determinism.
Is quantum computing a fake concept?
Generative coding of quantum circuits.
Most projects should stick with designing and running quantum algorithms on classical quantum simulators and stay away from actual NISQ hardware, for the indefinite future.
Thoughts on quantum networking.
How do you talk about quantum to lay people?
How do you talk about quantum computing to lay people?
Forms and levels of quantum logic packaging.
Prospect for a Q-com boom and eventual bust analogous to the infamous dot-com boom and bust — followed by an even bigger but more durable boom.
My quantum swan song.

Runner-up topics

These topics are just as important and interesting as the top choices, but didn’t quite make the cut. In some cases the topic might be sufficiently appealing, but I may not yet have enough information to do it justice.

Prospect for a Q-com boom and eventual bust analogous to the infamous dot-com boom and bust — followed by an even bigger but more durable boom. Many of today’s high-flyers will soar for a while — maybe even for two to three years, but many will then crash and burn in a Quantum Winter. A second wave combination of the few survivors and a heartier cohort of newcomers will follow the bust. Q-com = Quantum Commercial businesses.
Are quantum Fourier transform (QFT) and quantum phase estimation (QPE) required to achieve quantum advantage? At least for quantum computational chemistry? Implies that fine granularity of phase is required.
Are quantum Fourier transform (QFT) and quantum phase estimation (QPE) the Holy Grail of quantum computing for achieving quantum parallelism? If not, what methods or techniques are superior and more powerful and more general? Are there more specialized superior methods or techniques which apply to niche cases?
Thoughts on probability, stochastic processes, statistical aggregation, and determinism.
What does the field really need right now, for the coming year? Deliver promised qubit increases. Improve coherence time.
What application is likely to reach dramatic quantum advantage first? Or at least which application category? I’m personally rooting for quantum computational chemistry. Business process optimization would seem to be a slam dunk. What factors will make the choice? Could ability to tolerate an approximate solution be a key factor?
What application is likely to reach The ENIAC Moment first? This could be the same as What application is likely to reach dramatic quantum advantage first?, but a true dramatic quantum advantage is not required provided that enough of a partial quantum advantage is achieved that really impresses people.
Need for multiple ENIAC Moments, one for each major application category. The needs of the various application categories are too different for a solution in one to automatically translate into a solution in any of the others. But maybe the others follow in fairly rapid succession from the first one, which blazes the trail.
What might you do with 512 qubits?
What might you do with 256 qubits?
What might you do with 128 qubits?
Need for 32 to 64-qubit algorithms. Smaller algorithms don’t really demonstrate the true potential of quantum computing — dramatic quantum advantage. 32–40 qubits can be classically simulated, so focus there first, with emphasis on scaling from 32 to 64 qubits.
How soon will algorithms hit a hard wall without quantum error correction? Algorithms using more than 28 or 32 or 36 or 40 qubits? For usable, practical algorithms addressing real-world business problems, not specialized computer science experiments.
Wanted: True Nobel-class geniuses to propel quantum computing more than a few quantum leaps forward. Mere incremental engineering just won’t cut it to finally make quantum computing usable and useful at production scale for real-world problems. Breathtaking advances are needed, but incremental advances are simply not enough. Quantum computing will just be dead air until then.
Would moonshot projects or Manhattan Project-style of projects really help quantum computing make a quantum leap to its full potential? Timing is everything — doing either Project Apollo or the Manhattan Project even a mere five years earlier would have been an absolute disaster — a critical mass of the critical technologies, coupled with a critical mass of resources and a critical mass of commitment are essential. Quantum computing is still basically stumbling around in the dark. Besides, look how well classical computing developed, without a moonshot project. But also look at how the long stream of critical developments occurred over an extended period of time.
Can quantum variational methods ever achieve quantum advantage? Sure, variational methods are a great way of performing computational chemistry calculations on NISQ devices, but not with any apparent let alone dramatic quantum advantage. Too fragmented. Each invocation is too limited. Need to use 50 or more qubits in a single Hadamard transform computation to achieve quantum advantage.
Are variational methods a deadend? Inability to achieve dramatic quantum advantage?
Importance of focusing on near-perfect qubits. Especially as a near-term research target. Full-blown quantum error correction (QEC) is too far out. Near-perfect qubits are needed for QEC anyway. NISQ won’t get to quantum advantage. Range of possible nines — generally, 3 to 6. Nines and fractional nines.
NPISQ vs. NISQ devices. NPISQ = Near-Perfect Intermediate-Scale Quantum. Not quite as error-free as full quantum error correction, but getting close, and far less noisy than NISQ. May be good enough for many applications. And doesn’t require the sheer volume of qubits that full QEC requires. Full QEC (FTISQ = Fault-Tolerant Intermediate-Scale Quantum) may still be needed for some applications, but NPISQ may be good enough for many.
Noisy, near-perfect, and fault-tolerant quantum computers. Fault-tolerance requires dramatically more qubits (redundancy). Noisy is too noisy for most applications. Near-perfect still has some minor amount of noise and isn’t as absolutely perfect as fault-tolerant QEC, but may be good enough for many applications. Prefixes: N, NP, FT.
Small scale, intermediate scale, and large scale quantum computers. Intermediate-scale quantum computers, as in NISQ, have 50 to a few hundred qubits. Small scale would be under 50. Large scale would be more than a few hundred, thousands, or even millions. Prefixes: SS, IS, LS. Combine with prefixes for noisiness (N, NP, FT): NSSQ, NISQ, NLSQ, NPSSQ, NPISQ, NPLSQ, FTSSQ, FTISQ, FTLSQ.
Importance of focusing classical quantum simulators on near-perfect qubits. Although the definition and specification of qubit fidelity for near-perfect qubits will vary over time, it would be very beneficial to configure classical quantum simulators to precisely match the qubit fidelity of real near-perfect qubits so that simulation runs will closely match the results of execution on a real quantum computer constructed of near-perfect qubits meeting those qubit fidelity specifications. In addition to precisely matching existing machines, qubit specifications could be tuned to match proposed qubits to see how they are likely to perform even before the machine is actually built. A wide range of possible near-perfect qubit specifications could be simulated to get a sense of expected results even before real machines are actually built. Specify number of nines for qubit fidelity. Focus algorithm designers and applications developers on how many nines of qubit fidelity they are working with. Focusing on both NISQ and perfect qubits is an unproductive distraction. Granted, initial focus will be on very-low nines (1 to 2–90% to 99%), but explicit focus on nines is a more productive mindset.
We desperately need near-perfect qubits to accelerate algorithm development. Noisy qubits are an incredible burden and impediment to designing quantum algorithms and developing quantum applications. Logical qubits based on full, automatic, and transparent quantum error correction will solve a plethora of problems, but won’t be available for quite a few years, so the intermediate steppingstone of near-perfect qubits will enable a fairly wide range of algorithms and applications. Near-perfect qubits are needed to accelerate algorithm design, application development, and deployment of quantum computing solutions.
Might an application-specific or domain-specific quantum computer lead to the greatest breakthrough in quantum computing? We do have D-Wave, but it is algorithm-specific without general-purpose capabilities. Classical computing leaped over analog computing with the capability of a general-purpose Turing machine.
Where is quantum computing today? Outside chance of something useful in 2 yrs or 2–3 years? Greater chance of something useful in 5 years or 4–5 years? Nothing really useful today? Unlikely in next 6 months? Still rather unlikely in 12 months? Or even 18 months? Is two years still a little too far out to be trying to accurately predict? Will Honeywell and IonQ break out, stall, or stumble? Toy apps — 5 to 17 qubits, no real apps with over 20–24 qubits, other than Google quantum supremacy example? Unclear how many qubits are usable with quantum simulators — very small numbers, 20, 40 (only in theory)? Connectivity is still a limiting factor — transmon — hard limitation, trapped ion — promises any to any, but not yet a reality for a significant number of qubits?
No, most current quantum computers are not NISQ devices. Technically, intermediate-scale (the IS in NISQ) means at least 50 qubits, up to a few hundred qubits. As such, quantum computers with 5 to 32 qubits, the bulk of most current quantum computers don’t technically qualify as true NISQ devices. That said, most people consider these smaller quantum computers to be NISQ devices anyway. That’s life in a young sector where hype is rampant.
People are jumping the gun and prematurely acting as if quantum computing technology was near to being ready for deployment in the next few years. A belief in deployment of quantum computing in the next two or three years is clearly not currently justified. Even deployment in three to five years is currently not technically justified. Five to seven years might be a practical timeframe, but that’s really merely speculation at this juncture.
Enhancements to What Are Quantum Effects and How Do They Enable Quantum Information Science? The quantum hypothesis — in general. Historical role in transitioning from the classical world to the quantum world. Momentum, angular momentum, orbital momentum. Add Helicity (spin, angular momentum), Chirality (handedness), polarization, and detail on creation and annihilation operators (primarily to facilitate quantum mechanical modeling of many-particle systems.) Harmonics? Localization vs. spooky action at a distance? Pure state, mixed state, density matrix, density operator. Wave packets. Nuclear magnetic resonance (NMR). Some early efforts in quantum computing relied on NMR. Nuclear electric resonance. Gravity — nominally lies in the realm of General Relativity rather than quantum mechanics, but… who knows for sure. Can a quantum computer be used to accurately simulate or model gravity and gravitational effects, including effects on time and space? Clearly quantum metrology relates to gravity and gravity waves in some way. Is friction a quantum effect? Avalanche and threshold effects as quantum effects? Correlated quantum matter and quantum materials. Mention hidden variables. Mention Hilbert space. Nonlocality, Bell’s theorem, nonlocal correlations. Contextuality — Kochen–Specker (KS) theorem, also known as the Bell–Kochen–Specker theorem. Add Penrose microtubules and consciousness. Quantum logic clock. AMO physics — atomic, molecular, and optical — matter-matter and light-matter interactions at the scale of no more than a relatively few atoms. Are phase transitions a quantum effect — macroscopic in aggregate, but specific effect at the atomic or molecular level, and could they be used for quantum information science? Quantum correlations. CHSH inequality.
Toolkits, libraries, frameworks, and packaged solutions for quantum computing applications. Relative value. Relative benefits. Limitations. Toolkits help and give you some, but limited, leverage: Little in the way of deep, dramatic abstractions, Significant knowledge of under the hood is required, Work across multiple or even all domains, Intimate knowledge of probabilistic and statistical solutions rather than deterministic solutions. Frameworks gives you significant leverage: Many deep and meaningful abstractions, But still a lot of gaps and connections that user must fill in, Some, but limited, knowledge of under the hood may be required, Degree and depth of knowledge will vary from framework to framework and domain to domain, May work across at least some domains, but may be domain-specific, Some degree of knowledge of probabilistic and statistical solutions rather than deterministic solutions. Packaged solutions: It’s all there, User gets to work exclusively in terms of higher-order abstractions, User can do some degree of configuration and customization, but all in terms of higher-order abstractions, No knowledge of under the hood required, Usually domain-specific, although there may be some niche horizontal solutions, Deterministic solutions, or maybe some degree of statistical solutions, if that is appropriate for the application domain, otherwise strictly deterministic, even if that is accomplished using statistical methods. Tools vs. toolkit — standalone tools as apps vs. modules linked from libraries? Libraries separate, or simply the implementation mechanism for toolkits and frameworks? Under the hood: Math, Physics, Greek symbols, Arcane jargon.
Quantum hell. Waiting for… Hardware, Algorithm metaphors, and Applications. Needed and critical: genius-level innovation.
How do you talk about quantum to lay people? So many different audiences. So many different interests. So many different levels of sophistication. Granularity of the universe. Qubits behave communally — enormous parallelism. Chemistry is fundamentally quantum. Molecules are quantum. Much greater sensitivity. Classical mechanics is an approximation — it doesn’t capture everything. Quantum mechanics has been around for a long time. A lot of uncertainty in and about quantum technology.
Which will come next: more qubits or higher quality qubits? Either way, we need near-perfect qubits, even for quantum error correction. Quality would help a lot. Not clear that algorithms are capable of utilizing more qubits yet, especially if better connectivity is needed. Will ion traps reach commercialization soon or have a slower slog towards a higher number of qubits?
What’s wrong with quantum volume? Limitations. Low utility. Lack of specificity.
Alternatives to quantum volume for benchmarking. Separate grade for each capability. Number of qubits. Nines of qubit fidelity for gate execution and measurement. Coherence time. Connectivity — any to any vs. SWAP network fidelity? Gate execution time as well (ion traps supposedly much slower.)
The essential concepts in quantum computing. Not all of the technical details, math, physics, and nuances, but what really matters. Quantum parallelism. Interference. Phase. Circuit repetitions (shots) for statistical significance. Probabilistic. Little data with a big solution space and little output. Also see Little Data With a Big Solution Space — the Sweet Spot for Quantum Computing.
NISQ era. Does it have any real practical consequences, or is it simply an inconsequential steppingstone to get to a proverbial post-NISQ era where there actually are dramatic practical consequences? AFAICT, NISQ alone won’t enable production-scale practical applications with dramatic quantum advantage.
Add the recent (2019) Preskill comments from Quanta to my quantum advantage paper. Preskill in Quanta: Why I Called It ‘Quantum Supremacy’ — https://www.quantamagazine.org/john-preskill-explains-quantum-supremacy-20191002/
Quantum parallelism as the central transform for quantum algorithms.
Update What Is Quantum Algorithmic Breakout and When Will It Be Achieved? Large number of measurement shots needed — can force use of sub-optimal algorithms in a bid to dramatically reduce the number of measurements/shots needed. Is the ultimate rule for shot count polynomial or exponential? More emphasis on advanced, powerful simulators. Or is the current section enough? Maybe mention AtoS, hardware accelerators. More massive memory.
Fractional parallelism. Unable to do full desired parallel computation in one step — must break it into pieces with optimization or other processing between the pieces. Blocks for D-Wave. Variational methods. Machine learning?
Status of two-year path to initial success. When did I first say I saw a 2-year path to a real, meaningful application of quantum computing? Later I codified it as The ENIAC Moment. Now, how much longer is it likely to take? Is it slipping a year every year?
Still In the 1930’s of classical computing. Current quantum computers are not even comparable to what we could do with classical computers in the early 1940’s. For a timeline of historical classical computers, see Timeline of Early Classical Computers.
What happens during the gap between the ENIAC moment and the FORTRAN moment? The Lunatic Fringe reigns supreme with raw machine language serving a limited, elite audience and market. Packaged quantum solutions requiring only configuration but no algorithm coding can enable solutions to problems for a wider audience. A number of mini-stages before the full FORTRAN moment is achieved — partial FORTRAN moments, such as a limited number of logical qubits. Some niche applications may be able to verge on The FORTRAN Moment even before many or most applications are enabled for the full sense of The FORTRAN Moment.
Is a quantum computer merely a quantum calculator? Is a quantum computer really a computer or simply a calculator? The coprocessor model. Merely a single block of code rather than a full program or application, with no control structures, data structures, nested function calls, rich data types, I/O, database access, or network access. Separate paper for quantum coprocessor? Hybrid computation — quantum subroutine, quantum computer doesn’t perform a full computation, only a portion, a “calculation”, such as the variational quantum eigensolver (VQE) method. What are the key differentiators between a computer and a calculator? Such as, both perform basic operations, logic — conditional execution, loops, iteration, nested functions, with parameters, input and output during processing, persistent storage — shared between runs of the same or different programs. Full Turing machine? What defines what that is?
Qubit is a hybrid combination of a storage element and a processing element. And information as well. Can storage and processing be separated, either completely or temporally?
Quantum computing for dummies. The easy stuff that everybody needs and wants to know. But none of the deep stuff, no math, no Greek, no physics. Plenty of these out there, but I know I can do it better and more simply — and more correctly.
My focus for quantum computing. The technology and how it can be applied, but no specific applications per se. 2–5 year timeframe. Not so interested in the next year. Likely not the second year either. Not as interested in 10+ years, other than eventual destination and universal quantum computer — hybrid with classical. 5–10 years less an interest, but mabe 7 if delays for technology which should have happened in 5 years. But… likelihood of dramatic progress in 2–5 years is a coin flip, so expectations for 2–5 years may not transpire until 5–10 years.
Classical computers are based on quantum mechanics and quantum effects too. Yes, they are. Quantum effects within transistors — and in wires as well. All classical bits and gates are ultimately derived solely from quantum effects. Somehow, quantum effects can be converted into non-quantum macroscopic effects — through the magic of statistical aggregation. Probability plus statistical aggregation equals approximate but practical determinism. Contrast with absolute determinism. Or does an exponential limit plus quantum threshold guarantee determinism, short of hardware failure? Every classical computer is also a quantum computer, in some sense. Simply collapses all of the quantum states. But still depend on quantum state transitions between the collapses. Technically, it may be more correct to say that classical computing is based on quantum effects rather than on the logical operations of the Bloch sphere and unitary matrices on qubits. It would be fascinating to explore the boundary between the quantum and non-quantum worlds, possibly even producing a hybrid, mixed model of computation.
Roadmap for molecular modeling of chemistry. Define a wide range of molecules, from simplest to most complex, as the milestones on the roadmap. Such as salt, caffeine, sugar, gasoline, etc. Estimate qubits, circuit depth, and coherence needed for each milestone.
When will an application be appropriate for a quantum computer? Solving a substantial, production-scale, real-world problem using a quantum computer.
Levels of use. Related to personas and use cases, but a simpler formulation. 1) Direct use of qubits by algorithm designers. 2) Application developers use high-level libraries which generate quantum circuits and convert qubit results to application results. 3) STEM managers and executives who have teams to design, implement, deploy, and operate quantum-based solutions. 4) Non-STEM business and operational managers and executives who rely on the results of quantum-based applications. 5) Executives of organizations which rely on quantum-based computations, such as drug discovery, material design, business process optimization, etc. — better or more efficient operations, better or more efficient financial results. 6) Customers and users who use the products and services of organizations which rely on quantum computations.
Not ready for Quantum Ready. Because quantum is not ready for us. Not just a need for much more advanced hardware, but algorithms and programming models as well. Algorithmic building blocks — richer set of levels of abstraction. Standardized primitive operations. Programming language. Standardized APIs. Design patterns. Clearly explained examples — fully commented.
What computational tasks are possible using quantum computing? Not applications or application problems per se, but specific types of computations.
How many more qubits are needed before we can do anything significant with a quantum computer? Maybe that’s The ENIAC Moment, or maybe something short of the full ENIAC moment.
Deep dive on phase estimation. What makes it work? How does phase actually get captured? Why isn’t this a primitive operation of the firmware? How many 9’s of qubit fidelity are needed to capture each bit of phase? Need solid, real-world examples.
Is quantum computing on the verge of a breakout, or a breakdown into a quantum computing winter?
Beware of quantum algorithms which don’t discuss and fully characterize scalability. Discuss, demonstrate, and even prove scalability, if possible. Fine to run on a small real quantum computer, but should accurately simulate on larger classical quantum simulators (up to 40 to 45 qubits.) Simulation should use a realistic noise model comparable to expected real machines in a target timeframe (one year, two years, five years, seven years, ten years.) Identify and discuss any limiting factors, such as phase granularity.
Scaling of algorithms and applications won’t be easy, automatic, and free. Too many odd factors and tight limits. Someday it may get easier, more automatic, and relatively cheap, but not anytime soon. For the foreseeable future it will be tedious, difficult, and outright problematic. Quantum Fourier transform, quantum phase estimation, and anything dependent on fine granularity of phase will be especially problematic.
When will we get to see the emperor’s clothes? The tailors have made many wildly extravagant promises, and we’re all presuming that the reality will be even more extravagant than the promises, but… here we are, waiting patiently (okay, very impatiently!) for the emperor and his super-extravagant clothes to make their appearance. We won’t be disappointed… will we? Seriously, how much of the hype should be treated as fact, or even understatement of fact?
What is the truth about quantum computing? Beyond the hype. What are the strongest statements we can make which are in fact grounded in fact?
When will quantum computing start to become interesting? 128 vs 256 vs. 1K qubits? 100 vs. 1K vs. 10K gates? 100 vs. 250 vs. 500 vs. 1K circuit depth? (coherence) What algorithms, besides Shor’s? Only when production-scale is reached? Short of production scale, but sufficient to demonstrate that production-scale is close to being in reach? Maybe it’s The ENIAC Moment, by definition?
What can a quantum computer actually do? Problems. Applications. Functions. Mathematical functions. Computable functions. Tasks. Logic gates. Absolutely, and relative to what a classical computer can accomplish.
When is quantum computing projected to be able to do anything interesting or practical? Maybe 2–3 years. Maybe 5 years. Maybe 7 years. Maybe 10 years. Maybe 15 years.When might The ENIAC Moment arrive?
Is a Quantum Winter coming? Based on what criteria? What would it look like? How might it end and transition to a Quantum Spring and Quantum Summer?
Quantum computing is (currently) far too complicated for all but the most elite of professionals. To do anything useful, that is. When will that change? Maybe, or in theory, The FORTRAN Moment.
What criteria must an application meet to be appropriate for a quantum computer? Probabilistic results are okay. Approximate results are okay. The application problem must be reducible to a problem in physics. Can’t require complex logic with conditionals, looping, functions with parameters, or rich data types. Must utilize quantum parallelism with a register of substantially more than fifty qubits under a Hadamard transform to achieve dramatic quantum advantage. Only dramatic quantum advantage justifies the use of a quantum computer.
Need for a lone physicist with a single great idea. Achieving The ENIAC Moment for quantum computing might be less about some big corporate push, and more about a solitary elite scientist or engineer identifying a kernel of a simple but great idea and pushing forward to a breakthrough implementation that dramatically blows away everyone and everything else. Possibly even developing new hardware, new tools, and a new programming model focused on achieving great results for that single great idea. Minimal resources, but maximal impact. Even if that one implementation is specialized for that one idea, it could provide the foundation for generalizing to a much broader class of problems. Are there any young Feynmans or Oppenheimers out there poised for the challenge?
The Mantra: Quantum computing must deliver substantial and dramatic enterprise value far beyond what classical computing can deliver. In the form of quantum advantage — dramatic quantum advantage.
The siren song of quantum computing: The lure and promise of quantum parallelism. Making the promise a reality.
Quantum variational methods considered harmful. Variational methods are useful in general in physics, and seem to work modestly well on NISQ devices, but they don’t appear to offer any prospect of achieving dramatic quantum advantage. They are a mediocre substitute for true quantum parallelism on a scale capable of achieving dramatic quantum advantage.
How best to frame quantum computing. Especially for different audiences or personas. Will evolve over time as well as the technology, use cases, and access patterns evolve.
What is the largest semiprime number which a quantum computer can reasonably be expected to factor within the next 2–3 years? Or are the precision and fidelity requirements for quantum Fourier transform still too great for near-term machines? Can we achieve enough nines of qubit fidelity for near-perfect qubits, or are even more nines or full quantum error correction required?
What do senior executives need to know about quantum computing? Right now, just that it is a research area — fund much more research before expecting that it can be deployed to solve real-world problems. Less about the raw technology and what’s under the hood, and more about applications, benefits, and limitations.
What mindset is needed to excel at quantum computing? How to analyze real-world problems. How to couch problems in quantum terms. How to architect and synthesize quantum solution approaches. How to design quantum algorithms. How to develop applications which use quantum algorithms.
What are quantum computers good for? What problems are they most applicable to? Key benefits — besides raw performance, solving problems previously thought unsolvable using classical computers.

This is a live list and could be updated on any day.

Additional runner-up topics

I know it’s a lot of extra clutter since these topics are also in the full list, but all of them feel important enough to warrant my attention sooner rather than later. I just couldn’t bring myself to remove them. I’m hoping to winnow this list down over time. Although every time I think I have my lists under control they just seem to grow even more!

Integrating quantum computing with real-time quantum sensing. I’m not sure exactly what this would really look like, but the potential is… awesome. Need some sort of real-time loop — snapshot quantum sensed data, process, optionally output classically, rinse and repeat. Actual quantum processing would have to be re-thought from the ground up — not limited by or based on classical real-time processing. A quantum version of a CCD image sensor would be an obvious use case.
How can we automatically validate scalability of a quantum algorithm?
What is the secret sauce (or sauces) of quantum computing? What really makes quantum parallelism tick… and roar? How is quantum advantage actually achieved?
Spoiled by the richness of classical computing, Turing machine, data type abstractions.
Quantum algorithm design, quantum application development, and quantum application deployment. Overall architectural and methodology model for quantum computation. Core quantum algorithms. Hybrid classical code to bridge the gap between application data and quantum circuits. Classical code for applications which use those quantum algorithms. Real-world deployment of those applications.
What are pure states and mixed states? And density matrix and density operator. Do they relate to superposition, entanglement, or both? What’s the point? Why should we care? What are they good for? How do we use them? FWIW, I rarely see mention of them. What are the proper terms to use when the probability amplitudes of the basis states of a single, unentangled qubit are 0.0 and 1.0 vs. neither 0.0 nor 1.0 — pure and mixed states, or some other terms? See Introduction to Quantum Information Science Lecture Notes by Scott Aaronson.
Bespoke algorithms vs. reusable algorithms. Tradeoff between algorithms custom-designed and tailored for particular applications, and general-purpose algorithms usable across many applications. Advantages and disadvantages to both. Bespoke algorithms can be more efficient, run faster, and use less resources, but require dramatically more intellectual effort, and be much more difficult to maintain. General-purpose algorithms can be slower and use more resources, but much easier to understand and use. Generalities, but many exceptions.
Challenges of modeling complex systems for quantum computing. Other than physics and chemistry which are naturally modeled by quantum mechanics. Telling people to simply model their problem as a physics problem is too simplistic and not so helpful for non-physicists and non-chemists.
GIGI — Garbage In, Garbage Out — How will your quantum algorithm or application behave if the input data is problematic? Detecting errors. Rules for good, clean data. Issues with quantum circuits. Validate data before generating a quantum circuit.
What does it mean to be quantum? In general, but simplified. See also: What Are Quantum Effects and How Do They Enable Quantum Information Science?
Necessity is the mother of invention: Let application requirements drive the design of the quantum computer. The designs of all of the early classical computers were driven by the intense needs of particular high-value applications. Bell Labs, Harvard, Zuse, Atanasoff, Colossus, ENIAC, EDVAC, EDSAC, SEAC, Whirlwind, MANIAC, SAGE, et al. But somehow there was still an intense interest in building a fairly general purpose machine.
My pet peeves about quantum computing. So many! Where to start?!
What are the most problematic aspects of quantum computing today? Which are preventing production-scale applications which deliver substantial business value not achievable using classical computers. Capacity and fidelity are the first two.
Debunking quantum hype. Where to start! Never-ending! Not much left?
What might a BASIC language for quantum computing look like? What types of problems can be solved very easily?
No need to start learning about quantum computing until 40-qubit algorithms are the commonplace norm. Scalability is one of the more important and urgent early lessons — it’s okay to code and test an 8 to 12-qubit algorithm, but you must not presume that it works until you scale it up to 32 to 48 qubits. And this is just the starting point — quantum computing won’t be in full swing until 64 to 80-qubit algorithms are the commonplace norm. IOW, dramatic quantum advantage is the norm.
What’s common across all quantum computing application categories? Opportunities for sharing and reuse vs. need for unique and specialized approaches.
Essence of quantum computing. In one sentence, one paragraph, one page, two pages, four pages, ten pages, twenty pages, fifty pages, and 100 pages. What gets added at each stage, and what gets left out to get to the previous stage.
The great seduction of quantum computing. How can we reclaim our dignity?
Ideal qubit technology has not yet been discovered or invented. Ideal as in good enough to achieve dramatic quantum advantage. Still in the early days. May be another 5–10 years before we see qubit technology which will enable dramatic quantum advantage for a wide range of applications.
Never underestimate the boundless cleverness which can propel classical computing to great new levels of capability. Clever heuristics and intuitive leaps can surmount even extremely daunting obstacles.
Proof points for quantum computing to achieve widespread adoption. Milestones to quantum advantage and beyond. The ENIAC Moment and The FORTRAN Moment would be two. Various numbers of qubits would be others — 72, 80, 96, 128, 192, 256, 512, 1024, etc. Various nines of qubit fidelity would be others — two nines, three nines, four nines, five nines, six nines, etc. Full quantum error correction would be another. Various circuit depths of coherence would be others — 10, 20, 50, 75, 100, 150, 250, 500, 1,000, etc. Fractions of full, dramatic quantum advantage would be others — 10%, 25%, 50%, 75%, 90%, etc., although different application categories might have different requirements.
What is the key to quantum parallelism and quantum advantage?
Things I like to say about quantum computing. Wisdom. Fundamental general knowledge. Like, the ideal qubit technology has not yet been invented. Summarize the field. Simplify the field. Characterize the limits of the field. Cut through all of the hype.
How much can a quantum computer compute without recursion? Seems like a rather severe limitation.
How do you do anything useful on a quantum computer — like what? Tends to be too opaque and inscrutable. Need some direct, plain language description.
The power of randomness in a deterministic world. Using randomness, probability, and statistical aggregation to approximate determinism. Many real events have a random character, so coping with randomness is a good first step. Being overly-reliant and overly-dependent on absolute determinism can cause problems when so many random factors are in play. Randomness can average out to an approximate determinism.
My function as doing due diligence for quantum computing.
Quantum algorithms need to be provably scalable. Generally scalable via parameterized dynamic generation of quantum circuits. Test on both real machines and simulators for smaller sizes, simulator-only for medium sizes (32 to 50 qubits), and reliance on proof of scalability for largest sizes, beyond 50 or so qubits.
Am I on the verge of becoming an apostate for quantum computing? Yes, I’m getting more gloomy about the short-term prospects for quantum computing, but I still haven’t given up on the longer-term prospects, yet.
The truths you need to know about quantum computing.
Bitter truths about quantum computing. Tough to accept.
Why is quantum computing such a big deal? Quantum parallelism provides an exponential speedup which gives a dramatic quantum advantage over classical computing.
How to Get Started in Quantum Computing? To do what? What target persona? What target use case? Preparation for what? Role-specific — producers — research and vendors, consumers. Application architects. Application developers. Algorithm designers. Business managers with business operations problems to solve. STEM managers with STEM problems to solve. Clearly document your intentions — what problems are you trying to solve, specific use cases.
When should senior managers and senior executives expect that quantum computing will be capable of delivering dramatic top-line revenue and bottom-line profit — 3, 5, 7, 10, 12, 15, or 20 years? Tough question. Significant investment will be needed well in advance of production-scale deployment, but business managers need to focus on delivering dramatic business value using mature technology, not availability of risky early versions of technology.
The ugly truth about NISQ computers — great for testing but not for production. Small scale is a possibility, but not production scale.
What is a post-NISQ quantum computer? What would it look like and what can it do that a NISQ quantum computer can’t do?
My concerns about quantum computing at this stage. Lack of robust application examples. Lack of great algorithmic building blocks.
I’m not ready to give up on quantum computing yet, but… Not even close to being useful. Not even just over any reasonable horizon. Serious issues even on a much longer timeline.
Quantum computing: All dressed up and no place to go. Maybe great (or at least decent) algorithms that can be simulated, up to a degree, but no hardware to achieve true quantum advantage, for now. “Maybe next year [or the year after that or…].”
Stuck/mired in the quantum swamp. A superposition of futures — simultaneously very bright and very discouraging! Great potential for various niches, but not value or irrelevant for many others.
What will it take to get to the ENIAC moment?
When can we expect practical, production-scale real-world applications with a dramatic quantum advantage? 5–7 years? Two years after The ENIAC Moment — the first production-scale application.
A quantum computer as a function, subroutine, or coprocessor. Think of a quantum computer as a function, subroutine, or coprocessor.
Can quantum computing ever expand beyond being a mere coprocessor for narrow computations? Complex logic, rich data types, complex data structures, Big Data, functions, classes/objects, I/O, databases — all while under quantum parallelism.
Future of quantum computer/processor as a feature of a universal computer rather than as a separate computer. Coprocessor vs. just additional instructions. Special data types vs. an attribute of existing data types.
Should I take a break from quantum computing, maybe even a protracted Rip Van Winkle sleep? How long? Maybe two years, or maybe just six months, or maybe even sleep for 2–4 years, or 5–7 years, or even 15–25 years and wake up when quantum computing really is universal and finally ready for deployment at production scale, or a year before The ENIAC Moment. Criteria for resumption, threshold. Let the actual technology catch up with at least a fraction of the hype — and my own expectations. What to monitor while I wait — news, papers, conferences, books. Definitely monitor progress, but how to do that most effectively. Maybe not a break so much as shifting gear or slowing down or part time so I can focus on other things as well.
Quantum computing as a Mount Everest problem. Relatively easy to climb up the lower portions of the slope, but lack of oxygen on the upper reaches of the slope can just crush your soul and spirit and bank account or at least the patience of your management and backers.
Clarify the terminology: quantum computer, quantum computer system, quantum computing, quantum computation, quantum processor, quantum processing unit, QPU, quantum device, NISQ device. Where are the boundaries between pure quantum and non-quantum hardware?
Venture capital opportunities in quantum computing. Really only a research play right now. “Quantum Ready” is more of a fiction than a reality. More than a few years from commercial or even laboratory availability of a quantum computer capable of delivering dramatic quantum advantage for production-scale real-world problems.
Quantum volume — how does it work, what does it mean? Deeper dive. List all of the factors. Explain in plain language. Can it be reverse engineered? What does it really mean for application developers? Does it really not work for >50 qubits?
A few general comments on quantum volume.
How to decode quantum volume. How to get square size. How wide and long a non-square rectangle is supported. Sense of overall fidelity. Sense of connectivity. Anything else? Or is a single number a one-way hash which hides rather than enhances value? Or is it only useful for comparing machines, relative to each other, and not comparing rectangle size, maximum circuit length, or connectivity?
Companies looking to deliver data science, machine learning, AI, drug discovery, material design, and business optimization over the next 2–3 years should not be looking to quantum solutions in that timeframe. Experimentation, prototyping, and leading-edge research, yes, but not production-ready solutions.
The fantasy of quantum computing. Top 10 fantasies — coming soon — within 2 years, reliable qubits, coherent qubits, high-capacity simulator, portability of algorithms and applications between vendors (hardware platforms), high-level programming model and language, transition from hyper-hype to real reality.
The problem and opportunity of quantum networking. No-cloning theorem — not simply copying information from A to B. Entanglement — transmitting shared information, establishing a portal (of sorts.)
Breakthroughs vs. incremental progress. Will quantum error correction be enough? Are more dramatic hardware breakthroughs needed? Is a dramatic algorithmic breakthrough needed? Need a much higher level programming model, with logical operations. What is needed for The ENIAC Moment, or The FORTRAN Moment?
What’s my most valuable contribution to quantum computing at this stage? Fighting hype. Simplifying jargon. Uncovering nuance. Uncovering issues. Understanding — and explaining — limits and limitations.
Essential issues for quantum computing. Probabilistic and statistical rather than deterministic. Noise and errors using NISQ vs. eventual QEC fault-tolerant quantum computing. Reduction of problems from application terms and traditional math and computer science to raw physics. Need for elite staff. Classical computing still has plenty of gas and runway. Classical Boolean logic and classical mathematical algebraic expressions don’t translate well (at all) into quantum code. Individual qubits vs. numbers.
Need a roadmap for quantum computing for what is needed, not simply what can be done. Needs to be more about problem solutions than the raw technology.
Should quantum applications even need to be aware of qubits, even logical qubits, or should higher-level abstractions be much more appropriate for an application-level quantum programming model? Classical programming doesn’t require knowledge of bits: Integers, real numbers, floating point, Booleans — logical true and false — binary, but not necessarily implemented as a single bit, text, strings, characters, character codes, structures, objects, arrays, trees, maps, graphs, media — audio, video, structured data, semi-structured data.
What is quantum networking? Doesn’t exist yet, but hypothetically.
Quantum area networks (QAN). Goal is modular quantum computing systems. Limit to qubits in a micro-area (1 mm or 1 cm?). Need ability to “shuttle” quantum state across relatively short distances, a few mm, a cm, maybe a few cm, or maybe a few feet, or even 10–20 feet to enable two or more modular quantum systems to be combined into a single quantum computer. Maybe some way to daisy chain QPUs to have even a large data center complex as a single quantum computer system. Distinguish “moving” entirety of a qubit quantum state vs. enabling two-qubit gates for entanglement between two adjacent modules.
Is Shor’s algorithm the worst-case limit for quantum algorithms, or just the starting point for serius, heavy-duty quantum algorithms?
What’s the largest single quantum computation we can imagine at this time? If we could build the largest quantum computer we wanted to solve the largest and hardest computation problems we could possibly imagine, what computations or problems might they be?
Google quantum supremacy one year on. Why didn’t this feat open the floodgate for practical applications or even one additional example of quantum supremacy? Preskill: What’s Next After Quantum Supremacy? http://theory.caltech.edu/~preskill/talks/Preskill-Q2B-2019.pdf
Risk of intellectual property (IP) causing a quantum dark age. Accenture patents? Put emphasis on open source.
Hello World program for quantum computing. No clue what it should really look like. A single X or H gate might suffice, but my inclination is that it should be the simplest program which shows a practical example of quantum parallelism with results that most people can understand. Something functional, such as 2, 3, and 4-qubit QFT. Something that most people can relate to and say “Wow, that’s cool!” Should attempt to show quantum advantage, but that could take 50 qubits or more. Maybe a graduated collection of Hello World programs is best.
What’s wrong with the traditional quantum example algorithms. Not solving real problems. Too artificial. Too inscrutable. Grover provides only a quadratic speedup and really isn’t appropriate for “databases” per se. The examples, or their narrative, is unclear about how quantum parallelism really works.
Need a better collection of example algorithms. Solve problems people can relate to. Solve real-world problems. Demonstrate quantum advantage.
My five (or so) key stumbling blocks in quantum computing. How are multi-qubit product states represented physically — for n qubits, 2^n quantum states. Granularity of phase and probability amplitude.
Top priorities for quantum computing research. Better qubit hardware. Qubit fidelity — coherence, gate operations, measurements. More qubit hardware technologies. Better environmental isolation and shielding. Logical qubits, error correction. Larger numbers of qubits. Modular quantum processor designs for very large numbers of qubits. Algorithms capable of scaling to quantum advantage. Tools to analyze algorithms for scalability issues. Tools for testing and debugging algorithms. Better classical quantum simulators — performance, capacity, accuracy, configurability.
Some day all computers will be quantum computers. Quantum-level logic could enable much smaller and much more efficient classical logic. Use logical qubit technology to construct classical bits. Simultaneously use desire for smaller and more efficient classical bits to drive down size and performance of physical qubits and logical qubits.
What practical, production-scale real-world problems can quantum computing solve — and deliver dramatic quantum advantage and dramatic real business value? And be clearly beyond what classical computing can deliver.
Quantum phase estimation (QPE) and quantum Fourier transform (QFT) as discrete (parameterized) firmware operations. Permit algorithm designers and application developers to use QPE and QFT as simple, atomic black boxes rather than a blizzard of gates. Optimize any SWAP networks needed for connectivity within the firmware and hardware.
Quantum computing as it exists today and in the near future is a dead end. No hint of The ENIAC Moment. Quantum advantage is not possible without quantum error correction or very near-perfect qubits. Next two years. For the foreseeable future? Hardware — Not enough qubits, Weak fidelity, Limited connectivity. Algorithms — No robust programming model, No robust set of algorithmic building blocks, No easy paths from problem statements to quantum solutions, No robust set of introductory example programs, No clear path to quantum advantage. Not suitable for production-scale real-world problems.
How precisely can a probability amplitude or probability or phase be estimated? Can 0.0 or 1.0 or even 0.50 be precisely achieved? Or is there always a tiny epsilon of uncertainty down at the Planck level — 0.0 plus epsilon, 1.0 minus epsilon, 0.50 +/- epsilon? Same or different epsilon for probability amplitude and probability? Separate epsilons for the real and imaginary parts of a probability amplitude? Epsilon as a Planck probability? How tiny? Also for minimum difference between two probability amplitudes. Even for 0.50, how close can two probabilities be? Has anyone else written about this?
Applying quantum effects to general AI and simulation of the human brain/mind. Probability and statistical aggregation. Data-driven processing, not program-driven. And sensor-driven as well — quantum image sensors.
Is quantum computing needed to achieve human-level artificial intelligence? Maybe not quantum computers as currently envisioned, but some level of computing using quantum effects. Turing u-machine. Quantum operations — cannot be computed classically.
Is quantum computing real? Capable of solving production-scale real-world problems and delivering substantial real-world value?
Is there any material or substance that can’t be used as a qubit? A grain of salt, sugar, or sand? A drop of water? A small ball bearing, BB pellet, or lead shot? A snippet of copper wire? A capacitor? In theory, anything could be used to build a quantum computer (or qubit) — everything is based on the same fundamental quantum mechanics. Isolation, control, coherence, and measurement are the gating factors. Since the natural state of the world is quantum, “Almost anything becomes a quantum computer if you shine the right kind of light on it.” — MIT physicist Seth Lloyd
Quantum Internet. What exactly is it? Is it simply a special instance or application of quantum networking, or a distinct concept. Regular Internet using quantum links? Quantum channels? Quantum hubs — store and forward?
Short piece on achieving quantum parallelism using Hadamard transform (gate).
Where is the value in quantum machine learning if there is no support for Big Data?
Grover’s algorithm considered harmful. Or at least less than helpful. Not really relevant to traditional Big Data databases. Only a quadratic speedup — not an exponential speedup. What would be a better example? What would be a realistic app to use Grover? How many qubits? What phase granularity requirement?
Citation of Shor’s algorithm considered harmful. There are no practical implementations of the original algorithm, as written, so there is no point to citing it as an example of a practical quantum algorithm — and this is not likely to change any time soon. It’s very misleading to speak of Shor’s algorithm for factoring large semiprime numbers as having been “demonstrated” or implying that it is feasible or practical for relatively large numbers. It adds no real value to any published paper to cite Shor’s algorithm. At best, it’s a mere distraction. At worst, it detracts attention from more worthy near-term pursuits.
Quantum computing is stuck in Toy Land. Currently only suitable for relatively trivial toy-like algorithms and applications. Not practical for real-world production-scale problems.
What are the quantum volume requirements for an algorithm or application? Applying quantum volume to algorithms and applications. Need automated analysis. Especially tricky for dynamically-generated algorithms and applications, such as for a Python program which generates and executes quantum circuits.
How to map your application problem solution to a problem in physics. Need for application frameworks and domain-specific mappings so elite pioneers can do all the heavy lifting to map the problem area to physics problems so that their followers can then more directly exploit (reuse) those mappings without necessarily even understanding what’s going on under the hood.
Tasks to pursue to get deeper into quantum computing. Read a lot more of the papers coming out. Read more of the older papers, the foundational material. Deeper understanding of how qubits are actually implemented. Deeper understanding of quantum mechanics. Deeper understanding of quantum chemistry. Deeper understanding of Shor’s algorithm, and others — look at the derivative algorithms, read Miller’s ERH paper. Study number theory. Quantum error correction. Quantum communication. Post-quantum cryptography. Fill in as many of the TBDs in my glossary as possible. Consider a quantum Rip Van Winkle nap to wake up when quantum computers finally are mainstream with widespread production-scale applications very common — how many years should I set my alarm clock for?
Might Fortran make a comeback for quantum computing? Purely speculative, but based on scientific computing being a primary focus for quantum computing. My notion of The FORTRAN Moment was merely using the original FORTRAN programming language as a metaphor for a transition to mass adoption by non-elite professionals as occurred in the 1950’s for classical computing when the original FORTRAN was introduced, but if scientists flock to quantum computing, many of them are already and still using Fortran. Note: FORTRAN (all caps) became Fortran (capitalized) beginning with the Fortran 90 standard. Many existing scientific applications may have been developed using the FORTRAN 77 standard, or even earlier versions of FORTRAN (FORTRAN IV, FORTRAN 66.)
Model for scalability of algorithms. General approach and need for discussion of scalability. Demonstrating algorithms on smaller machines, with details for how the algorithm would scale to larger machines. Demonstrating algorithms on classical quantum simulators with noise models that match actual or projected real machines.
Potential for quantum sensors and quantum effectors. Super-fine sensing and robotics. Integration of quantum sensing and quantum computing.
Preliminary thoughts on an ontology for quantum computing. And overall quantum information science as well. My glossary of terms for quantum computing might be a good starting point for terms and concepts. The goal for this particular topic is not the full ontology, but more of a framework or outline for the full ontology. Criteria. Goals. In theory an ontology should be machine readable, as well as templates and automated processes for producing human-readable versions of the machine-readable ontology. Not clear who or what would use the ontology, although it should be a definitive map of the landscape of quantum computing.
Fundamental organizing principles and key insights for quantum computing. What really makes quantum computing tick. What is quantum parallelism really all about. How does product state for entangled qubits really work its magic? Not all the gory low-level technical details, but the key abstractions and functional elements of quantum computing. See also: What Are Quantum Effects and How Do They Enable Quantum Information Science?
Key questions about quantum advantage to ask when reviewing algorithms, applications, papers, projects, and products. Mostly just making sure the matter is discussed fully and thoroughly, and in specific detail. How scalable is it as well. See section in What Is Dramatic Quantum Advantage?