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. (tables AI sourced)

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:

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.

On a personal note:

I remember the interest generated when Shor published. 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. In those days, if two or three people were interested in a topic, they might contrive to meet in person, off the clock even, so that they could talk about it. 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 tech. 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:

https://www.youtube.com/watch?v=e2Mtt2rb654&t=10s

There are a few approaches 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 was a Veritasium video some years ago about a company called Mythic using CIM. Cool stuff:

https://www.youtube.com/watch?v=GVsUOuSjvcg

https://www.youtube.com/watch?v=IgF3OX8nT0w

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

They managed to get $13 million to stay alive, and a few months ago received $125 million in funding, so more videos maybe.

AI gave me a list of companies in the field. Everyone I looked at was a startup with hardly any revenue and their biggest announcements were their latest funding rounds. 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.

On a personal note:

I worked with an old engineer some time ago, and he had dabbled in analog computers as a younger man. Where he 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 produces output that is subtly better than the competition. It reminds me of the difference between my IPad and the Android tablets I have.

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.

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.