Dr. Ernest Blatchley
speaker
323 appearances
2 recordings
2 series
first heard Jun 2026
last heard 29 Jun
Dr. Ernest Blatchley’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 2 in all, peaking in Jun 2026 with 2.
Appearances
At Sigildry, we're looking across all the different quantum modalities and hardware types and architecting computer systems to meet the requirements of AI based on the maturing path that all these different hardware modalities are on.
And that allows us to build systems that are specifically tailored to AI and that we believe are going to be able to meet the requirements of bringing quantum into the AI data center at scale.
Yeah, we've been able to do that, largely speaking, and you can do simulations of something, computer system or a jet or anything, and varying levels of physical fidelity and detail.
The simulation we've been able to do so far indicate that we expect a level of, you know, several orders of magnitude potential speed up for key training tasks, right?
So this is not a factor of two or a factor of five increase that we're targeting with quantum acceleration inside the data center.
it's several orders of magnitude you know when when all the pieces come together um but that simulation you talked about is a really really important and powerful part of designing a computer system you can't simulate all the all the logic of a quantum computer because that would require a quantum computer itself sure kind of by definition but you can do load profiling you can do uh you can do traces you can understand how that's gonna you know uh
be distributed across classical and quantum hardware and also simulate all the networking transactions in between.
And so that's the kind of simulation driven design approach we're taking.
It's really interesting.
I think the answer is both.
So our starting point is we're looking at ways that you can insert quantum algorithms and quantum computing capability into the existing paradigm, the existing workflow for training and deploying very large models, frontier models at scale.
And
That means you're looking for an insertion point from quantum algorithm where the data in the data out allow you to then take a step that would take maybe, you know, a day or two classically and compress that down to hours or minutes and do that throughout the workflow.
The challenge is that
quantum computing provides an exponential uh you know the possibility for exponential speed up with the right algorithm but it also has this issue with data in and data out so it's classical data in which is can't be exponential in size and classical data out and so the less you do that that translation between the quantum part and the classical part it's going to end up working better so asymptotically where we're where we're heading is more quantum native models
Models that are designed in the first place to leverage a quantum computing capability tightly integrated with your classical infrastructure.
But where you're probably not going to see is fully quantum-based models that don't include a substantial amount of classical compute as well.
So this isn't going to replace all the AMD or NVIDIA infrastructure in the data center.
It's going to augment it.
And our business model and our focus and our product strategy is to build a quantum accelerated AI server that sits next to the pod.
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