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Desmond
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Desmond
@DesFrontierTech
Tracking IonQ where quantum, AI, and runtime become behavior.
Katılım Aralık 2020
70 Takip Edilen7.9K Takipçiler

Gene sees it.
Revenue is growing fast.
Customers are committing early, the stack is coming together end to end, and the company has time to build it.
$IONQ
seekingalpha.com/article/488613…
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@genejchan You do a great job tying the numbers to how the business is actually forming.
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@DesFrontierTech 🎯
$IONQ is severely undervalued now for how fast its revenue is growing
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Been using the word infrastructure a lot. This Vanderbilt forum shows what it looks like. IonQ speaking across security, networking, energy, and policy. This is the buildout. $IONQ
vanderbilt.edu/government-com…
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Here it is:
$IONQ will do $500M in 2026 (pardon my previous typo), over a billion in 2027, and reach profitability by 2029
My latest article earned "Editor's Pick" on SeekingAlpha
Enjoy!
seekingalpha.com/article/488613…

Gene@genejchan
Just submitted a new $IONQ article to SA, pending editorial review prior to publishing My analysis shows that IonQ will reach $300M revenue this year, over a billion by 2027, and profitability by 2029 Details to follow in my upcoming article - stay tuned!
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Good instinct. The catch is decoherence is more than timing. It's when the quantum state is lost. Once that happens, the information is gone. Software can't save it by passing it to another machine. What engineers actually do is use error correction, move the state carefully between systems, and design hardware that keeps it stable long enough to keep going. You can plan around decoherence, but you can't avoid it with software. This is a long way of saying you don't outrun decoherence with software, you manage it with architecture.
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@DesFrontierTech I've been wondering, Desmond, if there is a way to calculate decoherence time, and have a software application that passes the calculation from one machine to the next before they go incoherent. Like a software relay so decoherence never happens.
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Customers don't care about gate speed.
They're thinking about how long it takes to get an answer they can trust.
“Time to solution” means how long it takes to go from a question to a usable answer.
And that depends on how the system behaves when you actually use it, not just how fast it looks.
Do you get there in a few runs, or do you have to keep trying?
Do the results hold up, or do you keep fighting the noise?
Customers are looking for clear answers to those questions because that's what determines cost and whether it delivers when you need it.
$IONQ
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🚨 $IONQ
Hyundai and Kia just patented something with IonQ.
Not about "quantum advantage."
About how machines make decisions.
Sensors capture the world.
Classical systems process the data.
Quantum is inserted into the decision step.
Autonomy runs into the same constraint.
Noisy data and real-time decisions.
This is where systems fail.
Improve that layer and everything downstream improves including reliability and safety.
Advantage doesn't come from bigger models.
It comes from how decisions get made inside the system.
drive.google.com/file/d/1lj3vr3…

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@DesFrontierTech The energizer bunny rabbit better watch his back! Niccolo is coming for his job!
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Niccolo de Masi spoke at the the Council on Competitiveness today.
Science, government, and enterprise leadership in the same room.
compete.org
$IONQ


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Jensen Huang said the problem no longer fits in one machine.
AI now runs across thousands of systems.
Coordination becomes the constraint.
IonQ building in that layer.
$IONQ
youtu.be/vif8NQcjVf0?si…

YouTube
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Ran portfolio optimization on S&P 500 data.
As the system scaled, results improved without changing the workflow.
Better inputs, better outcomes.
$IONQ
ionq.com/blog/quantum-c…
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