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Every quantum state in the universe is a vector - not in 3D space, but in a complex space with potentially infinite dimensions where the sum of squared coefficients always equals exactly 1. That constraint is the same math LLMs use to normalize probability distributions over tokens. A wavefunction...

18,166 görüntüleme • 1 ay önce •via X (Twitter)

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$IONQ The U.S. National Science Foundation just launched Project Triad: "the first-ever system where quantum sensing, networking and computing work together in concert," built to turn quantum into real-world applications across safety, healthcare, energy and defense. Now look at the elements. Sensing, computing, networking, the exact three domains NSF wants to unite. IonQ Federal's Rick Muller laid out all three at World Quantum Day, months earlier: Sensing: PNT clocks roughly 1000× more accurate than GPS (Vector Atomic), plus space (Capella) and optical comms (Skyloom). Networking: a contract in DARPA's Heterogeneous Architectures for Quantum (HARQ) program, plus QKD and entanglement distribution. Computing: nearing fault-tolerant operation in DARPA's Quantum Benchmarking Initiative (QBI), verified by DARPA. So it's not one program. IonQ is stacking two DARPA programs (HARQ and QBI) on top of the Vector Atomic, Capella and Skyloom acquisitions. His words: "IonQ can provide an engine to drive federal quantum integration that no other companies can." Now put the two diagrams side by side. NSF's illustration of an "integrated quantum system" and IonQ's timing architecture are the same picture: networked satellites, ground stations, sensors and a quantum computer, tied together by quantum links. NSF hasn't named a company. But it's building a national program around the architecture IonQ already has, down to the diagram. Sound familiar? 🔗 #IonQ #Quantum #ProjectTriad

TechInnovation

17,144 görüntüleme • 2 ay önce

In 2006, Netflix offered $1,000,000 to anyone who could improve their recommendation algorithm by 10%. Over 2,000 teams competed for three years. The team that won did not use more data. They used fewer dimensions. They found a basis - a small set of independent vectors that captured everything important about 100,000,000 movie ratings. The lead mathematician on the winning team: $2,800,000 a year. A machine learning engineer at Spotify building the same kind of system: $245,000 a year. This is MIT 18.06, Lecture 9 - Gilbert Strang. Free on YouTube. Most people think independence is obvious. Two vectors pointing in different directions. Then the definition. Independence means no combination of your vectors gives the zero vector - except the trivial one where all the coefficients are zero. That's it. That's the whole definition. But watch what it unlocks. Watch the moment Strang puts three vectors in a two-dimensional plane. He doesn't even tell you which three vectors. He just draws them. And immediately says: dependent. No question. No calculation. Why? Because three vectors in two-dimensional space means more columns than rows. More unknowns than equations. That always forces a free variable. A free variable always gives a non-zero solution to Ax = 0. And that non-zero solution is a combination of the columns that produces zero. Dependence. "Three vectors in the plane have to be dependent. That's the key fact." Then the basis. A basis is vectors that are independent and span the space. Not too few, not too many. Just right. The pivot columns of any matrix form a basis for the column space. Every other basis you can think of will have exactly the same number of vectors. Then the dimension. All bases for the same space have the same number of vectors. That number is the dimension. The rank of a matrix is the dimension of its column space. The number of free variables is the dimension of the null space. And rank plus null space dimension equals the total number of columns. "I don't take the dimension of A. I take the dimension of the column space of A. If you use those words right, it shows you've got the idea right." A data scientist at Netflix building recommendation engines: $230,000 a year. A quantitative researcher at Two Sigma finding independent factors in financial markets: $350,000 a year. A computer vision engineer at Apple using low-dimensional representations for face recognition: $260,000 a year. They all needed to know how many dimensions were really there. bookmark this and watch later - after this lecture every dataset you look at will feel like a matrix waiting to be reduced to its basis.

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