Quantum Computing

Analog

AI

Quantum Computing

Briefest origin story: Quantum Computing as a concept follows from an 80's era collective conjecture, developing into a dedicated mission upon the publishing of Shor's algorithm in 1994. I am assuming most people are familiar.

So, 32 years later...

WHERE IT STANDS

There are a handful of companies providing platforms for quantum computing and they've produced devices that have many qubits.

Physical qubits are given in the table but relative to computation topics like code-breaking, logical qubits are what matter. Multiple physical qubits are required to achieve a single logical qubit, with the exact number varying considerably from platform to platform.

So, how many logical qubits are needed? For breaking encryption, the task so prominently mentioned 30 years ago, here are the estimates:

By the way, ECC-256 is the encryption used in the bitcoin protocol.

Alright, so the next thing we need to know is how many logical qubits are being achieved.

That top entry with 'Error Detection Only' sort of doesn't count because error correction is needed for the computation being considered. So for now, 28 is the max, and my reading leads me to believe that this was labwork.

Bitcoin would seem to be safe for awhile.

So what can be done with the logical qubits available? Well, nothing to prefer it over the old fashioned silicon.

Consensus is that 100 logical qubits are need to definitively outperform the alternatives. This will usher in the era of "scientific quantum advantage", a time when quantum computing certainly outperforms supercomputers at several tasks of scientific interest. And it is at this point that all of this effort will be proven worthwhile.

Here's the roadmap to 100 logical qubits:

Wasn't going to include those two myths at the bottom, but that kind of smack talk is part of QC.

There are also claims that, by the end of 2026, algorithmic cleverness will allow for some "quantum advantage" applications even with currently achievable logical qubit numbers.

Now appreciate this fact:

The first logical qubit was achieved in 2024.

As with other new technologies and nascent sectors, most of the news comes from the companies and research teams themselves, so some skepticism is warranted.

On a personal note:

I remember the interest generated when Shor published. Before that I had heard nothing of QC and hadn't even taken Quantum Mechanics (or even Mechanics) yet. The most anticipated application, then as now, was secure communication, or quantum encryption. There was a guy I knew who was particularly enthusiastic about confidentiality and most of what I heard about the topic, I heard from him, the kind of person that learned German and Russian for no reason. Conversations often went to how quantum computing would break standard encryption wide open. "Give it 10 years...", always another 10 years.

Analog Computers

A new niche for an old approach. New architectures for analog computers in theory can lower the energy consumed by the LLM computation, by factors in the range 100-1000.

A good Curious Droid video covering some of the past and present of analog computers:

There are a few approaches relevant to AI out there:

The CIM (Compute In Memory) approach using memristors is relatively simple and looks the most promising in terms of immediate applicability in the AI sector. As with Quantum Computing, most news in this sector comes from the companies themselves.

There were a couple of Veritasium videos some years ago about a company called Mythic using CIM. Cool stuff:

Then, later that year (2022), Mythic announced they had run out of money.

They managed to get $13 million to keep a pulse, and a few months ago received $125 million in funding.

I got a list of companies in the field from an AI prompt and everyone I looked at was a startup with hardly any revenue, their biggest announcements being their latest funding rounds. In fact, it looked like their reported revenue may have been a modest return on the money they're holding, but I don't know much about all that. I'm not really sure at this point who has products in the field.

Not CIM, but something about this idea I really like:

Anyway, Microsoft Research is funding a photonics-based endeavor and IBM is doing something as well, so there are $$$ in the mix.

As with Quantum Computing, the theoretical gains are irresistable motivation to anyone selling computation.

So from what I've seen, the main drawback with the CIM method is a worst case error of ~5%. There is some claim of error detection though, which would allow a calculation to be discarded in the extreme case and another attempt made. But there's always going to be some error in an analog system.

A lot of us have used analog computers without really putting such a fine point on it. An analog PID controller, for instance, is an analog computer. What about a digital PID controller? Well, then you are using a digital computer to emulate an analog computer.

Aside:

I worked with an old engineer some time ago, and he had dabbled in analog computers as a younger man. Where he had worked, there were still vacuum tubes in the older analog computers, or 'analyzers' as he called them. I always thought that was cool.

The comments in that Curious Droid video have a lot of old guys getting misty-eyed over their bygone control systems. They had some great analog methods for data analysis, too.

AI

--2025

2025 was the last year of the period of time covering AI's infancy and adolescence, basing my opinion mostly on the commercial availability of AI and the degree of utilization. A nice tidy number, too.

There were holdouts all through 2024 (couldn’t blame them), but the quality of the models exceeded some threshold in 2025. The most reluctant were brought onboard by Claude, which in that period produced output that was subtly better than the competition. It reminded me of the difference between my IPad and my Android tablets.

2026--

There is widespread adoption and almost universal awareness of the technology, even in the least developed areas.

Some say we have entered a time of acceleration.

Some say we have already reached a plateau.

At some point this year (2026), I started to get that queezy 'uncanny valley' feeling reading Claude text. Strange, I've never gotten that from text before, only from images and video. And I don't get that feeling from the latest generation of AI-generated visuals.

Where's the fake part?

After the words are made and the words have been lined up, the thinker is no longer required.

A Positivist View

Holding everything else constant, if AI simply attenuates the impact of human error and negligence then a lot of value is added to human society, and real money is saved on wasted time, remediation and insurance.

The best way to get any share in this is to use AI for one's own benefit as aggresively as everyone else.