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Joe Fitzsimons

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@jfitzsimons

Physicist. Founder and CEO @horizon_quantum. Former @quantumlah PI and associate professor. Slowly drifting south-east (Dublin-Oxford-Singapore).
Singapore Joined July 2009
584 Following 8.9K Followers 6.2K Posts

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Joe Fitzsimons Verified account @jfitzsimons
Delighted to be able to share that we expect to begin trading on Nasdaq tomorrow as $HQ. After 7 years of work, it’s great to finally reach the starting line.

$DMYY

horizonquantum.com/resources/news…
www.horizonquantum.com Horizon Quantum Closes Its Business Combination with dMY Squared Horizon Quantum closes its business combination with dMY Squared, and its shares will begin trading on the Nasdaq.
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Joe Fitzsimons Verified account @jfitzsimons
We’ve had some nice results multiplexing characterisation experiments. Our latest blog post digs into our team’s work on ZZ and control crosstalk characterization protocols that scale logarithmically in the number of qubits in a processor.
Horizon Quantum @horizon_quantum 1d
Maximizing hardware performance is key to reaching broad quantum advantage, making efficient, scalable characterization increasingly important. Our proprietary multiplexed protocol can enable rapid characterization of high-qubit-count systems. Learn more 👇horizonquantum.com/resources/blog…
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Joe Fitzsimons Verified account @jfitzsimons
Rob was a visiting student in my research group and my brother is his co-founder, so you can take what I say with a pinch of salt, but LLMs that are 10x faster and 100x cheaper with the output guaranteed to be in exactly the form you need seem a pretty compelling proposition.
Robert Pisarczyk Verified account @R_Pisarczyk Oct 27
Very excited to finally share what we've been building!

Today, we are launching Blatent Struct, our first language model. 🚀

One thing that's always bothered us about building with LLMs is how much engineering you need around the model. Getting an answer is one thing. Making sure it follows the right decision path, returns everything your application needs and doesn't break your business rules is another.

We wanted to change that.

Struct brings programmability to language models.

You can define complex, cascading decisions, conditional dependencies and exactly what each outcome must satisfy. You can also enforce business rules and permissions during inference.

And rather than stitching together multiple model calls, Struct can handle the whole decision tree in a single run.

We also spent a lot of time making sure this doesn't come at the expense of performance.

In our benchmarks, Struct achieves comparable accuracy to Claude Sonnet 5.5, at 11× the speed and over 100× lower estimated cost.

It's also over 2× faster than Jev, with higher average accuracy across 20 benchmarks. And on source-grounded extraction, it outperforms Cloudflare Clairic (87.5% vs. 81.7% across six benchmarks), at 62% lower estimated cost.

For me, the exciting part is what this enables: everything from document processing and customer support to agents that can make complex decisions while respecting the rules you've set.

This is just our first model, and there's a lot more to come.

I'd love for you to give it a try and tell us what you think. We're especially keen to hear what works, what doesn't and what you'd like us to build next.

🚀 Get started for free (up to 100m input tokens are on us) : blatent.ai
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