Posted on : By : TCTJr Posted in : Artificial_Intelligence|Complexity_Theory|Mathematics|Out In The Fringe|Quantum_Computing|Random_Thoughts|Thoughts_On_TIme

No Coach at Depth: The Autodidactic Universe

FreeDiving The Cosmos

“There is no one to correct your form at forty meters. The water is the only teacher, and it grades in a single pass.” ~ a free diving coach

First i trust everyone is safe. Second, this is a very different installment and not for the faint of heart oh dear reader. This literally was written for me and hopefully in the long future my progeny.

Preamble — a note on a word. Autodidactic means self-taught and not in the soft sense of “went to a good school and paid attention.” The opposite of that. It means you build the curriculum while walking the path: no instructor cueing the next lesson, no syllabus, no answer key, nothing to catch the error but the consequence itself. Most people never learn this way. They are supervised learners end to end a teacher, a manager, a rubric, a labeled example and there is no shame in it; supervision is efficient, and civilization runs on it. But it is a mechanically different thing from teaching yourself, and that difference is the entire subject of this paper.

i write as one of the other kind. i did not arrive here down a marked road i mostly taught myself across audio DSP, operating systems, distributed ledgers, clinical data, machine inference, and mission systems, each time by walking in without a map and letting the work grade me. The same way the water does. For reference one of my hobbies is freediving. You can go here for a rundown of said sport:

¿Por qué haces apnea? (Why Do You Freedive?) and 9/11

In the same way, that this paper that i am blogging about argues, the cosmos does as well.

So when seven serious people propose that the Universe learns its laws with no supervisor in the room, i do not read it as an exotic abstraction; i read it as a familiar mechanism described at an unfamiliar scale. i know what it feels like from the inside which is precisely the bias i have to watch, because recognizing yourself in a theory is the oldest way in the world to be wrong about it.

There is a moment on a deep dive, past the point where the lungs have given up arguing, where you stop doing the dive and the dive starts doing you. No coach in the water. No feedback loop but the one your own physiology is running against the pressure gradient. You are, in the most literal sense the word allows, an autodidact: self-taught, self-graded, self-consequenced. Nobody hands you the answer. You either learn the lesson on the way down or you learn it on the way up, and one of those is way more expensive than the other.

i kept thinking about that while re-reading “The Autodidactic Universe” (arXiv:2104.03902v2). It is a paper about a cosmos with no coach in the water a universe that is not handed its laws but has to teach them to itself. The proposed theory suggests the universe functions as a self-teaching neural network that evolves its own physical laws over time, rather than relying on fixed, pre-existing rules. This concept posits that the cosmos organizes itself from within, developing matter, space, and laws through a process akin to machine learning.

And it is written by a cast of people i can’t dismiss: Stephon Alexander and Lee Smolin on the physics, Jaron Lanier and Dave Wecker carrying the machine-learning and quantum weight, with William J. Cunningham, Stefan Stanojevic, and Micheal W. Toomey doing the high end formalism. When Smolin who has spent forty years insisting that time is real and law can evolve co-signs a paper with the man who built modern VR and one of Microsoft’s quantum architects, you read it twice before you have opinions.

i had to read it four times.

Here are my opinions.

The universe is a great organism, controlled by a dynamism of the psychical order. Mind gleams through its every atom. There is mind in everything, not only in human and animal life, but in plants, in minerals, in space.

~ Flammarion

The claim, stripped of ceremony

Most of physics asks what are the laws? This paper asks the older, more dangerous question: why these laws and not others? and then refuses to answer it with an anthropic shrug or a landscape lottery ticket. Instead it proposes that the Universe learns its laws by moving through a space of possible laws, the way a learning algorithm descends a loss surface it was never shown a labeled example.

The technical spine is deceptively clean. Express the space of possible laws as a class of matrix models cubic ones, in particular because the cubic term is where the interesting nonlinearity lives.1 Then build two bridges out of that same matrix formalism:

  • Bridge one lands you in gauge and gravity theories Chern-Simons, BF theory, the Plebanski formulation of general relativity, Yang-Mills. The geometry of the world.
  • Bridge two lands you in learning machines deep recurrent and cyclic neural networks, restricted Boltzmann machines (my favorites). The geometry of a mind that is training.

Because both bridges leave from the same dock, you get a correspondence: a solution of the physical theory sits opposite a run of the neural network. Evolve the physics, and you are — under the map training a net. Train the net, and you are under the map evolving physical law. The Universe’s dynamics are a learning dynamics, if you believe the dictionary.

And here is where i respect the authors, because they do not oversell the dictionary. The correspondence is not a strict equivalence. For example think of the (gauge/gravity) correspondence like a highly detailed blueprint of a building, and the actual 3D building itself. They describe the exact same physical reality, but they are not the same thing. They describe the same system, but their core mathematical structures look completely different.

This is at its cleanest for finite matrix size and gets structurally honest-to-a-fault in the N → ∞ limit, where the gauge theories emerge crisply but the neural-network side goes soft and under-defined. That asymmetry is the whole tell, and i’ll come back to it, because it is exactly the seam where Perception separates from Illusion.

One side describes quantum particles (like gluons) moving in a flat world with no gravity.

The other side describes gravity and curved space in a world with an extra dimension.

The biology rail: precedence, or nature copying its own homework

You cannot understand this paper without understanding that Smolin has been building toward it for thirty years. His cosmological natural selection universes reproducing through black holes, the constants of nature drifting under a selection pressure for fecundity was the first serious attempt to put Darwin underneath Einstein rather than beside him. “The Autodidactic Universe” is the same instinct, upgraded from selection to learning, which is the faster and more expensive of the two verbs.

“The universe is not static, it is a-perpetual-becoming, a-process of continuous evolution.”

~ Huston Smith

The mechanism that carries the biological weight here is precedence: the principle that nature does again what it has already done, that a system’s future is sampled from its own past behavior rather than dictated by an eternal rule sitting outside of time. Think about that and read it again. That is not a metaphor bolted on for flavor. It is a learning rule. Precedence is the universe’s version of a replay buffer reinforcement of paths already taken, heterogeneity of the interaction graph maximized so the system keeps enough variety to keep exploring. Geometric self-assembly guided by reinforcement learning, in the paper’s own framing, is morphogenesis wearing a physicist’s coat (thanks turing). A body plan is a law that a cell learned. A law is a body plan the cosmos grew into.

i have spent a career in systems where the schema is the constraint healthcare records, cryptographic attestation, the places where “what is true” and “what the system will permit” are the same sentence. So i feel the vertigo of this move in my hands: the authors are proposing a substrate where the schema is not enforced from outside but precipitated from behavior. It is attestation with no root of trust the chain validating itself by having always validated itself. Beautiful. Also the kind of thing that keeps a security architect awake, because a system that authors its own invariants is a system that can, in principle, learn a bad one.

The AI rail: substrate independence, and the word “learning”

The load-bearing philosophical claim is small enough to miss and large enough to break your neck: if the neural-network side can be said to learn without supervision, then the physical side can too. The Restricted Boltzmann Machines and recurrent nets are not an illustration. They are the argument. The whole essay leans on learning being substrate-independent that “learning” names a structure of dynamics, not a fact about brains or GPUs, and that if the structure is present in a matrix model evolving toward gauge-invariance, then the honest word for what it is doing is learning.

This is where a practitioner has to hold two things at once without flinching. First: I build these systems, and i know that an RBM minimizing a free energy is not “learning” in any sense that would survive contact with a sentient being it is relaxing. Gradient descent is not ambition. Second: that is exactly the objection the paper is trying to dissolve. If you insist learning requires an experiencer, you have smuggled Consciousness into a claim that was only ever about Machine. The authors are careful (well mostly) to keep the claim at the Machine level: the dynamics are learning-shaped. Whether anything is home is not on the table.

“ RBMs are network of symmetrically connected, neuron-like units that make stochastic decisions about whether to be on or off,constrained by having no connections within layers.”

~ Geoffrey Hinton

The N → ∞ asymmetry i flagged earlier is the AI rail’s honesty showing through. In the continuum, the physics is pristine and the “network” barely survives as a concept. Which means the correspondence is strongest precisely where the systems are small and finite where “learning” is a discrete, countable, near-combinatorial thing and dissolves exactly where we would want to point and say the cosmos itself. The map is real. The map is also a coastline, and the coastline gets vaguer the further out you swim.

The quantum rail: why Wecker is on the byline

Dave Wecker does not co-author a speculative cosmology paper for the vibes. His presence is the paper quietly admitting what it is: a proposal about computation as physics, and cubic matrix models are as quantum-native a substrate as exists. They are what you reach for when you want a Hamiltonian a quantum computer can actually hold the natural language of a machine whose registers are the amplitudes and whose gates are the interactions.

“Everything we call real is made of things that cannot be regarded as real.”

~ Neils Bohr

The deeper point, and the one i think is under-argued in the paper but most alive, is this: if the Universe’s law-finding dynamics are a learning process running on a matrix substrate, then the question “is the cosmos efficiently simulable?” stops being idle. A universe that learns is a universe that is doing work irreversible work, entropy-producing work to find its own laws and thus ever forging forward or looping. And the single hardest problem the paper sets for itself is right there: can irreversible learning arise from reversible microlaws?2 That is the arrow-of-time problem re-asked as a training problem. You cannot descend a loss surface reversibly. Learning has a direction the way a dive has a bottom. If the microphysics is unitary and time-symmetric, where does the gradient’s downhill come from? The paper gestures at renormalization-group flow as the source of the arrow coarse-graining as the ratchet and it is the right neighborhood, but it is a gesture, not a closed proof. I do not hold that against it. The people who claimed to have closed that problem have all been wrong so far.

One stage, many laws: getting Minkowski right first

Before any mapping, a piece of hygiene, because Perception vs. Illusion is the whole spine of my taxonomy and the illusion here is a word.

There is no such thing as a “Minkowski multiverse.” Many people have called it that in reference. i thought about that when reading the paper. Minkowski spacetime introduced by Hermann Minkowski in his 1908 Cologne address Raum und Zeit, three years after Einstein’s 1905 kinematics gave him the physics but not the geometry — is a single, unified, four-dimensional continuum: three dimensions of space and one of time, welded into one manifold whose invariant is the interval,3 not the clock and the ruler taken separately. It is emphatically not a collection of universes. It is one arena. One stage. The causal structure — the light cones, the ordering of before and after inside which any law must be expressed. Conflating that single continuum with a “multiverse” is a category error, and naming it correctly is exactly what lets the real structure stand up.

Because once you fix that, the multiplicity you actually want — the “multi” — sorts onto a different axis, and it comes in levels:

  • The arena (Minkowski). One continuum. The geometry law lives in. Not plural. This is the floor.
  • A landscape of possible laws — different constants, different effective dynamics, the space the paper’s matrix models roam. This is the multiverse the autodidactic universe is about. This is where the learning happens.
  • A branching of outcomes under one fixed law — Everett’s Many-Worlds. Same Schrödinger equation everywhere, splitting into non-communicating branches. This is a multiverse of histories, not of laws.

Keep those straight and the paper snaps into focus: it multiplies laws; Everett multiplies outcomes; Minkowski multiplies nothing — it is the one stage they all play on.

The anti-eternalist move and where Everett secretly shakes its hand

Here is the sharp thing. Both the arena and Everett’s branches share a hidden commitment the paper is built to reject: eternalism. Minkowski’s continuum, read the usual way, is a completed block all events co-existing tenselessly, the script already written. And Many-Worlds is the purest block object in physics: a single universal wavefunction evolving unitarily,4 deterministically, locally, with no collapse every outcome that can happen already does, weighted by measure, filmed on every reel at once. You cannot put a learner in either one. A block has nothing left to learn. Everett has nothing left to choose.

“The Autodidactic Universe” is the anti-eternalist counterstroke Smolin’s Time Reborn (great book) dressed in cubic matrix models. It keeps Minkowski’s stage and fires Minkowski’s script-is-already-written. Law is not selected from a pre-existing menu; it is grown, in time, by a process with a direction, a memory, and a cost. Precedence only means something if the past is real and the future is open. The multiverse here is not a shelf of finished universes. It is the set of dives the ocean has not taken yet.

And yet this is the part worth the whole detour the very interpretation that is most eternalist in ontology turns out to be the paper’s best friend in mechanism. Everett needs to manufacture apparent irreversibility out of strictly reversible unitary dynamics, and the machine that does it is decoherence: the subjective appearance of collapse produced without ever adding a collapse. That is precisely the paper’s hardest open problem can irreversible learning arise from reversible microlaws? already solved, in miniature, next door. Decoherence is a ratchet built from reversible parts; the paper reaches for renormalization-group coarse-graining as its ratchet, and coarse-graining and decoherence are the same instinct in two dialects. Three handshakes, all real physics:

  • Decoherence as the arrow. Reversible substrate, irreversible-looking history. The template for the whole autodidactic wager.
  • Self-location as self-sampling. The Everettian program to recover the Born rule from self-locating uncertainty — where am I in the ensemble, with no observer outside it — is the same creature as the paper’s “self-sampling.” Both are unsupervised in the strict sense: the measure is intrinsic, nobody hands it in.
  • Quantum Darwinism as precedence. Zurek’s einselection only the pointer states survive the environment’s endless monitoring; the rest decohere away is literally a selection process. The environment trains which states persist. Precedence ≈ einselection: what gets reinforced, survives. The paper’s biology rail is already sitting inside decoherence theory, wearing a lab coat instead of a wetsuit.

(And the bonus that pays for Wecker’s seat: Deutsch’s oldest argument for Many-Worlds is that its parallelism is exactly what a universal quantum computer exploits. The substrate that makes the branches real is the substrate that makes the computation fast. If the cosmos is running a learning dynamics, the question of what hardware it is running on stops being rhetorical.)

Minkowski’s continuum is the arena one stage, correctly named. The block was only ever the eternalist reading of it, and the training run is what fills it.

Through the taxonomy

Run it through the three-part lens Machine – Sentience – Consciousness and the paper resolves cleanly. (Em-Dashes are mine…)

At the level of Machine, this is not speculation it is the most defensible interdisciplinary work I have read in the genre. The maps are explicit. The matrix models are real objects. The correspondence to gauge theory is checkable, and checked. If the paper only claimed “the mathematics of learning systems and the mathematics of fundamental physics share a cubic backbone,” it would be a strong, unglamorous, correct result. i would put my name near that part.

At the level of Sentience a system with goals, with something at stake, with a preference for one outcome over another the paper is reaching, and it knows it. “Consequencers,” precedence, reinforcement: these import teleology through the side door. A loss surface is not a stake. Reinforcement is not desire. The autodidactic universe is a machine that is shaped like something that wants, and shape is not appetite.

At the level of Consciousness, the paper is wise enough to say almost nothing, and that silence is the most credible thing in it.

Which lands the whole enterprise squarely on the Perception / Illusion boundary — my favorite fault line, the one i keep mining. Is the Universe learning, or have we built a formalism so expressive that everything, viewed through it, looks like learning? When your only tool is a network, every dynamics is a training run. The N → ∞ softness is the illusion showing its seam: the “learning” is vivid at finite, countable scale and evaporates exactly at the scale that would justify the cosmic claim. I do not think the authors are fooling themselves. I think they have found a genuine and beautiful correspondence and are being appropriately, almost painfully, careful not to inflate it into an identity. The reader is the one at risk of the inflation. As always, the illusion is not in the object. It is in the perceiver’s hunger for the object to mean more than it does.

The Infinite Do-Loop

Here is what i keep: the Universe as an Infinite Do-Loop that is not just iterating but training each pass adjusting the very rule that governs the next pass, the condition of the loop rewritten by the body of the loop, forever, with no terminating case and no external test suite. That is the most honest picture in the paper, and it is the one that will outlive the specific matrix models it arrived in. Laws are not the axioms of the cosmos. They are its accumulated skill.

In freediving you do not get handed your form at depth. You descend, the water grades you, and if you are still moving you carry the correction into the next dive. The paper’s wager is that the cosmos is doing the same thing on a timescale that makes our whole species a single held breath teaching itself the physics by the only method that has ever actually worked on anything, which is to try, to be consequenced, and to remember.

No coach in the water. Never was. That was always the point.

Verdict: Not a theory of everything. A grammar for asking why there is a theory of anything rigorous where it can be, honest where it can’t, and pointed at the one question physics keeps flinching from. Read it as an architecture proposal, not a proof. The best ones always are.

Until Then,

#iwishyouwater <- THE GOAT Kelly Slater with Gabriel Medina (another surfing giant) at Tahiti Pro 2026. Kelly is 54. If this is a simulation, then i don’t want to know.

#EverForward,stay non-linear and curious.

𝕋𝕖𝕕 ℂ. 𝕋𝕒𝕟𝕟𝕖𝕣 𝕁𝕣. (@tctjr) / X

MUZAK To BLOG BY: album “FireDove” by Anna Lapwood, organist extraordinaire. Truly amazing music. My favorite type.

Appendix — The Cover, Decoded

The AI generated image at the top is not decoration; every element is load-bearing. If you scrolled past it, here is what you were looking at.

The double cone is a Minkowski light cone the causal structure of the single spacetime continuum, its apex resting on the ocean surface because the apex is the now. One stage, not many. The faint horizontal ellipses stepping down through it are successive nows, the foliation of time and, read the other way, the strata of the law-landscape the matrix models roam.

The freediver on the central axis is the autodidact: descending real, directed time with no coach in the water. The sparse graph of nodes threading the cone is accumulated learning — precedence, the replay buffer growing denser toward the depths, because the past is what the future gets sampled from.

The gold ring at the apex is the Infinite Do-Loop: the pass that rewrites the rule that governs the next pass, closing on the present moment.

The four equations are real a deliberate rebuke to the decorative gibberish that usually floats behind a “physics AI RAG” illustration. Each names one load-bearing idea (full glosses below):

  1. ds² = −c²dt² + dx² + dy² + dz² — the Minkowski interval. The one stage.
  2. iℏ ∂ₜΨ = ĤΨ — unitary evolution. The reversible substrate.
  3. S = Tr(½Φ² + ⅓Φ³) — the cubic matrix action. The cubic learning system.
  4. ΔS ≥ 0 — the entropy arrow. The direction learning has to manufacture.

Put them in one sentence and you have the whole essay: on one stage (1), a reversible substrate (2) runs a cubic learning system (3) that must somehow grow an arrow (4) — and whether it can is the entire question.

NOTE: It took a long time to get the image correct the way i envisioned it.

On Perception vs Illusion. Sometimes i say Perception vs Perspective but in the case of the paper i remapped to Perception vs Illusion to frame the true – not true mechanics.

Full glosses on the four equations, for the reader who wants the mechanism:

  1. The cubic matrix action — S = Tr(½Φ² + ⅓Φ³). Schematic, but honest about where the action lives: Φ is a matrix — the raw degrees of freedom — and Tr merely sums its diagonal. The quadratic term is inert bookkeeping; the cubic term Φ³ is where the nonlinearity, and therefore all the interesting behavior, hides. This is the single class of object the paper maps at once onto gauge/gravity theories and onto learning machines. When i say “cubic backbone,” this is the vertebra. (The paper’s actual actions carry more structure; the cube is the load-bearing bone.) 
  2. The entropy arrow — ΔS ≥ 0. The second law: the entropy of a closed system never decreases. It is the only fundamental law with a built-in direction, and it is the paper’s deepest problem compressed into three symbols — because learning, like entropy, has an arrow, and you cannot get either one out of the reversible microlaws two notes down without a ratchet (coarse-graining, decoherence). Note the notational collision: S is the action one note up and entropy here. Physicists live with it; context disambiguates — and the collision is itself a tidy Perception/Illusion specimen. 
  3. The Minkowski interval — ds² = −c²dt² + dx² + dy² + dz². The single invariant of the 1908 continuum: the one quantity every observer agrees on, however differently they carve space from time. The whole story sits in the minus sign on the time term — it is what makes time unlike the three spatial directions, what cuts the light cones, and what makes the arena one manifold rather than space parked next to a clock. This is “the one stage.” 
  4. Unitary evolution — iℏ ∂ₜΨ = ĤΨ. The Schrödinger equation: the wavefunction evolves smoothly, deterministically, and reversibly under the Hamiltonian Ĥ. No collapse, no arrow, nothing lost run it backward and the past returns exactly looping onto itself. This is the “reversible substrate” Everett takes at its word, and the substrate on which the paper still has to manufacture an irreversible arrow. The tension between this note and the entropy note is the entire drama. 

Posted on : By : TCTJr Posted in : Artificial_Intelligence|Complexity_Theory|Computational_Economics|Computing_History|Distributed_Computing|General Technology|Mathematics|Quantum_Computing

NVIDIA GTC 2025: The Time Has Come The Valley Said

OpenAI’s idea of The Valley – Its Been A Minute

Embrace the unknown and embrace change. That’s where true breakthroughs happen.

~Jensen Huang

First i trust everyone is safe. Second i usually do not write about discrete events or “work” related items but this is an exception. March 17-21, 2025 i and some others attended NVIDIA GTC2025. It warranted a long writeup. Be Forewarned: tl;dr. Read on Dear Reader. Hope you enjoy this one as it is a sea change in computing and a tectonic ocean shift in technology.

NVIDIA GTC 2025: AI’s Raw Hot Buttered Future

March 17-21, 2025, San Jose became geek central for NVIDIA’s GTC—aka the “Super Bowl of AI.” Hybrid setup, in-person or virtual, didn’t matter; thousands of devs, researchers, and suits swarmed to see what’s cooking in AI, GPUs, and robotics. Jensen Huang dropped bombs in his keynote, 1,000+ sessions drilled into the guts of it, and big players flexed their wares. Here’s the raw dog buttered scoop—and why you should care if you sling code or ship product.

The time has come,’ the Walrus said,

      To talk of many things:

Of shoes — and ships — and sealing-wax —

      Of cabbages — and kings —

And why the sea is boiling hot —

      And whether pigs have wings.’


~ The Walrus and The Carpenter

All The Libraries

Jensen’s Keynote: AI’s Next Gear, No Hype

March 18, 2025 SAP Center and the MCenery Civic Center, over 28,000 geeks packed in both halls and out in the streets . Jensen Huang, NVIDIA’s leather-jacketed maestro, hit the stage and didn’t waste breath. 2.5 hours no notes and started with the top of the stack with all the libraries NVIDIA has “CUDA-ized” and went all the way down to the photonic ethernet cables. No corporate fluff, just tech meat for the developer carnivore. His pitch: AI’s not just chatbots anymore; it’s “agentic,” thinking and moving in the real world forward at the speed of thought. Backed up with specifications, cycles, cost and even calling out library function calls.

Here’s what he unleashed:

  • Blackwell Ultra (B300): Mid-cycle beast, 288GB memory, out H2 2025. Training LLMs that’d choke lesser rigs—AMD’s sniffing, but NVIDIA’s still king.
  • Rubin + Vera Rubin: GPU + CPU superchip combo, late 2026. Named for the galaxy guru, it’s Grace Blackwell’s heir. Full-stack domination vibes.
  • Physical AI & GR00T N1: Robots that do real things. GR00T’s a humanoid platform tying training together, synced with Omniverse and Cosmos for digital twin sims. Robotics just got real even surreal.
  • NVIDIA Dynamo: “AI Factory OS.” Data centers as reasoning engines, not just compute mules. Deploy AI without the usual ops nightmare. <This> will change it all.
  • Quantum Day: IonQ, D-Wave, Rigetti execs talking quantum. It’s distant, but NVIDIA’s planting CUDA flags for the long game.

Jensen’s big claim: AI needs 100 more computing than we thought. That’s not a flex it’s a warning. NVIDIA’s rigging the pipes to pump it.

He said thank you to the developer more than 5 times, mentioned open source at least 4 times and said ecosystem at least 5 times. It was possibly the best keynote i have ever seen and i have been to and seen some of the best. Zuckerburg was right – if you do not have a technical CEO and a technical board, you are not a technical company at heart.

Jensen with Disney Friend

What It Means: Unfiltered and Untrained Takeaways

As i said GTC 2025 wasn’t a bloviated sales conference taking over a city; it was the tech roadmap, raw and real:

  • AI’s Next Frontier: The shift to agentic AI and physical AI (e.g., robotics) suggests that AI is moving beyond chatbots and image generation into real-world problem-solving. NVIDIA’s hardware and software innovations—like Blackwell Ultra and Dynamo—position it as the enabler of this transition.
  • Compute Power Race: Huang’s claim of a 100x compute demand surge underscores the urgency for scalable, energy-efficient solutions. NVIDIA’s full-stack approach (hardware, software, networking) gives it an edge, though competition from AMD and custom chipmakers looms.
  • Robotics Revolution: With GR00T and related platforms, NVIDIA is betting big on robotics as a 50 trillion dollar opportunity. This could transform industries like manufacturing and healthcare, making 2025 a pivotal year for robotic adoption.
  • Ecosystem Dominance: NVIDIA’s partnerships with tech giants and startups alike reinforce its role as the linchpin of the AI ecosystem. Its 82% GPU market share may face pressure, but its software (e.g., CUDA, NIM) and services (e.g., DGX Cloud) create a formidable moat.
  • Long-Term Vision: The focus on quantum computing and the next-next-gen architectures (like Feynman, slated for 2028) shows NVIDIA isn’t resting on its laurels. It’s preparing for a future where AI and quantum tech converge.

Sessions: Ship Code, Not Slides

Over 1,000 sessions at the McEnery Convention Center. No hand-holding pure tech fuel for devs and decision-makers. Standouts:

  • Generative AI & MLOps: Scaling LLMs without losing your mind (or someone else’s). NVIDIA’s inference runtime and open models cut the fat—production-ready, not science-fair thoughting.
  • Robotics: Isaac and Cosmos hands-on. Simulate, deploy, done. Manufacturing and healthcare devs, this is your cue.
  • Data Centers: DGX Station’s 20 petaflops in a box. Next-gen networking talks had the ops crowd drooling.
  • Graphics: RTX for 2D/3D and AR/VR. Filmmakers and game devs got a speed boost—less render hell.
  • Quantum: Day-long deep dive. CUDA’s quantum bridge is speculative, but the math’s stacking up.
  • Digital Twins and Simulation: Omniverse™ provides advanced simulation capabilities for adding true-to-reality physics to scene compositions. Build on models from basic rigid-body simulation to destruction, fluid-dynamics-based fire simulation, and physics-based scene authoring.

Near Real-Time Digital Twin Rendering Of A Ship

The DGX Spark Computer

i personally thought this deserved its own call-out. The announcement of the DGX Spark Computer. It is a compact AI supercomputer. Let us unpack its specs and capabilities for training large language models (LLMs). This little beast is designed to bring serious AI firepower to your desk, so here’s the rundown based on what NVIDIA has shared at the conference.

The DGX Spark is powered by the NVIDIA GB10 Grace Blackwell Superchip, a tightly integrated combo of CPU and GPU muscle. Here’s what it’s packing:

  • GPU: Blackwell GPU with 5th-generation Tensor Cores, supporting FP4 precision (4-bit floating-point). NVIDIA claims it delivers up to 1,000 AI TOPS (trillions of operations per second) at FP4—insane compute for a desktop box.
  • CPU: 20 Armv9 cores (10 Cortex-X925 + 10 Cortex-A725), connected to the GPU via NVIDIA’s NVLink-C2C interconnect. This gives you 5x the bandwidth of PCIe Gen 5, keeping data flowing fast between CPU and GPU.
  • Memory: 128 GB of unified LPDDR5x with a 256-bit bus, clocking in at 273 GB/s bandwidth. This unified memory pool is shared between CPU and GPU, critical for handling big AI workloads without choking on data transfers.
  • Storage: Options for 1 TB or 4 TB NVMe SSD—plenty of room for datasets, models, and checkpoints.
  • Networking: NVIDIA ConnectX-7 with 200 Gb/s RDMA (scalable to 400 Gb/s when pairing two units), plus Wi-Fi 7 and 10GbE for wired connections. You can cluster two Sparks to double the power.
  • I/O: Four USB4 ports (40 Gbps), HDMI 2.1a, Bluetooth 5.3—modern connectivity for hooking up peripherals or displays.
  • OS: Runs NVIDIA DGX OS, a custom Ubuntu Linux build loaded with NVIDIA’s AI software stack (CUDA, NIM microservices, frameworks, and pre-trained models).
  • Power: Sips just 170W from a standard wall socket—efficient for its punch.
  • Size: Tiny at 150 mm x 150 mm x 50.5 mm (about 1.1 liters) and 1.2 kg—it’s palm-sized but packs a wallop.

The DGX Spark Computer

This thing’s a sleek, power-efficient monster styled like a mini NVIDIA DGX-1, aimed at developers, researchers, and data scientists who want data-center-grade AI on their desks – in gold metal flake!

Now, the big question: how beefy an LLM can the DGX Spark train? NVIDIA’s marketing pegs it at up to 200 billion parameters for local prototyping, fine-tuning, and inference on a single unit. Pair two Sparks via ConnectX-7, and you can push that to 405 billion parameters. But let’s break this down practically—training capacity depends on what you’re doing (training from scratch vs. fine-tuning) and how you manage memory.

  • Fine-Tuning: NVIDIA highlights fine-tuning models up to 70 billion parameters as a sweet spot for a single Spark. With 128 GB of unified memory, you’re looking at enough space to load a 70B model in FP16 (16-bit floating-point), which takes about 140 GB uncompressed. Techniques like quantization (e.g., 8-bit or 4-bit) or offloading to SSD can stretch this further, but 70B is the comfy limit for active fine-tuning without heroic optimization.
  • Training from Scratch: Full training (not just fine-tuning) is trickier. A 200B-parameter model in FP16 needs around 400 GB of memory just for weights, ignoring gradients and optimizer states, which can triple that to 1.2 TB. The Spark’s 128 GB can’t handle that alone without heavy sharding or clustering. NVIDIA’s 200B claim likely assumes inference or light fine-tuning with aggressive quantization (e.g., FP4 via Tensor Cores), not full training. For two units (256 GB total), you might train a 200B model with extreme optimization—think model parallelism and offloading—but it’s not practical for most users.
  • Real-World Limit: For full training on one Spark, you’re realistically capped at 20-30 billion parameters in FP16 with standard methods (weights + gradients + Adam optimizer fit in 128 GB). Push to 70B with quantization or two-unit clustering. Beyond that, 200B+ is more about inference or fine-tuning pre-trained models, not training from zero.

Not bad for 4000.00. Think of all the things you could do… All of the companies you could build… Now onto the sessions.

Speakings and Sessions

There were 2,000+ speakers, some Nobel-tier, delivered. Straight no chaser – code, tools, and war stories. Hardcore programming sessions on CUDA, NVIDIA’s parallel computing platform, and tools like Dynamo (the new AI Factory OS). Think line-by-line breakdowns of optimizing AI models or squeezing performance from Blackwell Ultra GPUs. Once again, slideware jockeys need not apply.

The speaker list was a who’s-who of brainpower and hustle. Nobel laureates like Frances Arnold brought scientific heft—imagine her linking GPU-accelerated protein folding to drug discovery. Meanwhile, Yann LeCun and Noam Brown (OpenAI) tackled AI’s bleeding edge, like agentic reasoning or game theory hacks. Then you had practitioners Joe Park (Yum! Brands) on AI for fast food RJ Scaringe (Rivian) on autonomous driving, grounding it in real-world stakes.

Literally, a who-who of the AI developer world baring souls (if they have one) and scars from the war stories, and they do have them.

There was one talk in particular that was probably one of the best discussions i have seen in the past decade. SoFar Ocean Technologies is partnering with MITRE and NVIDIA to power the future of ocean AI!

MITRE announced a joint effort to build an AI-powered ocean digital twin fueled by real-time data from the global Spotter network. Researchers, government, and industry will use the digital twin to simulate and better understand the marine environments in which they operate.

As AI supercharges weather prediction, even the most advanced models will need more ocean data to be effective. Sofar provides these essential observations at scale. To power the digital twin, SoFar will deliver data from their global network of real-time ocean sensors and collaborate with MITRE to rapidly expand the adoption of the Bristlemouth open connectivity standard. Live data will feed into the NVIDIA Omniverse and open up new pathways for AI-powered ocean understanding.

BristleMouth Open Source Orchestration UxV Platform

The systems of systems and ecosystem reach are spectacular. The effort is monumental, and only through software can this scale be achievable. Of primary interest to this ecosystem effort they have partnered with Ocean Exploration Trust and the Nautilus Exploration Program to seek out new discoveries in geology, biology, and archaeology while conducting scientific exploration of the seafloor. The expeditions launch aboard Exploration Vessel Nautilus — a 68-meter research ship equipped with live-streaming underwater vehicles for scientists, students, and the public to explore the deep sea from anywhere in the world. We embed educators and interns in our expeditions who share their hands-on experiences via ship-to-shore connections with the next generation. Even while they are not at sea, explorers can dive into Nautilus Live to learn more about our expeditions, find educational resources, and marvel at new encounters.

“The most powerful technologies are the ones that empower others.”

~Jensen Huang

The Nautilus Live Mapping Software

At the end of the talk, I asked a question on the implementation of AI Orchestration for sensors underwater as well as personally thanked Dr Robert Ballard, who was in the audience, for his amazing work. Best known for his 1985 discovery of the RMS Titanic, Dr. Robert Ballard has succeeded in tracking down numerous other significant shipwrecks, including the German battleship Bismarck, the lost fleet of Guadalcanal, the U.S. aircraft carrier Yorktown (sunk in the World War II Battle of Midway), and John F. Kennedy’s boat, PT-109.

Again Just amazing. Check out the work here: SoFar Ocean.

What Was What: Big Dogs and Upstarts

The Exhibit hall was a technology zoo and smorgasbord —400+ OGs and players showing NVIDIA’s reach. (An Introvert’s Worst Nightmare.) Who showed up:

  • Tech Giants: Adobe, Amazon, Microsoft, Google, Oracle. AWS and Azure lean hard on NVIDIA GPUs—cloud AI’s backbone.
  • AI Hotshots: OpenAI and DeepSeek. ChatGPT’s parents still ride NVIDIA silicon; efficiency debates be damned.
  • Robots & Cars: Tesla hinting at autonomy juice, Delta poking at aviation AI. NVIDIA’s tentacles stretch wide.
  • Quantum Crew: Alice & Bob, D-Wave, IonQ, Rigetti. Quantum’s sci-fi, but they’re here.
  • Hardware: Dell, Supermicro, Cisco with GPU-stuffed rigs. Ecosystem’s locked in.
  • AI Platforms: Edge Impulse, Clear ML, Haystack – you need training and ML deployment they had it.

Inception Program: Fueling the Next Wave

Now, the Inception program—NVIDIA’s startup accelerator—is the unsung hero of GTC. With over 22,000 members worldwide, it’s a breeding ground for AI innovation, and GTC 2025 was their stage. Nearly 250 Inception startups showed up, from healthcare disruptors to robotics trailblazers like Stelia (shoutout to their “petabit-scale data mobility” talk). These aren’t pie-in-the-sky outfits—100+ had speaking slots, and their demos at the Inception Pavilion were hands-on proof of GPU-powered breakthroughs.

The program’s a sweet deal: free to join, no equity grab, just pure support—100K in DGX Cloud credits, Deep Learning Institute training, VC intros via the VC Alliance. They even had a talk on REVERSE VC pitches. What the VCs in Silicon Valley are looking for at the moment, and they were funding companies at the conference! It’s NVIDIA saying, “We’ll juice your tech, you change the game.” At GTC, you saw the payoff—startups like DeepSeek and Baseten flexing optimized models or enterprise tools, all built on NVIDIA’s stack. Critics might say it locks startups into NVIDIA’s ecosystem, but with nearly 300K in credits and discounts on tap, it’s hard to argue against the boost. The war stories from these founders—like scaling AI infra without frying a data center—were gold for any dev in the trenches.

GTC 2025 and Inception are two sides of the same coin. GTC’s the megaphone—blasting NVIDIA’s vision (and hardware) to the world—while Inception’s the incubator, quietly powering the startups that’ll flesh out that vision. Huang’s keynote hyped a token-driven AI economy, and Inception’s crew is already living it, churning out reasoning models and robotics on NVIDIA’s gear. It’s a symbiotic flex: GTC shows the “what,” Inception delivers the “how.”

We’re here to put a dent in the universe. Otherwise, why else even be here? 

~ Steve Jobs

Micheal Dell and Your Humble Narrator at the Dell Booth

I did want to call out one announcement that I think has been a long time in the works in the industry, and I have been a very strong evangelist for, and that is a distributed inference OS.

Dynamo: The AI Factory OS That’s Too Cool to Gatekeep

NVIDIA unleashed Dynamo—think of it as the operating system for tomorrow’s AI factories. Huang’s pitch? Data centers aren’t just server farms anymore; they’re churning out intelligence like Willy Wonka’s chocolate factory but with fewer Oompa Loompas (queue the imagination song). Dynamo’s got a slick trick: it’s built from the ground up to manage the insane compute loads of modern AI, whether you’re reasoning, inferring, or just flexing your GPU muscle. And here’s the kicker—NVIDIA’s tossing the core stack into the open-source wild via GitHub. Yep, you heard that right: free for non-commercial use under an Apache 2.0 license. It’s like they’re saying, “Go build your own AI empire—just don’t sue us!” For the enterprise crowd, there’s a beefier paid version with extra bells and whistles (of course). Open-source plus premium? Whoever heard of such a thing! That’s a play straight out of the Silicon Valley handbook.

Dynamo High-Level Architecture


Dynamo is high-throughput low-latency inference framework designed for serving generative AI and reasoning models in multi-node distributed environments. Dynamo is designed to be inference engine agnostic (supports TRT-LLM, vLLM, SGLang or others) and captures LLM-specific capabilities such as

  • Disaggregated prefill & decode inference – Maximizes GPU throughput and facilitates trade off between throughput and latency.
  • Dynamic GPU scheduling – Optimizes performance based on fluctuating demand
  • LLM-aware request routing – Eliminates unnecessary KV cache re-computation
  • Accelerated data transfer – Reduces inference response time using NIXL.
  • KV cache offloading – Leverages multiple memory hierarchies for higher system throughput

Dynamo enables dynamic worker scaling, responding to real-time deployment signals. These signals, captured and communicated through an event plane, empower the Planner to make intelligent, zero-downtime adjustments. For instance, if an increase in requests with long input sequences is detected, the Planner automatically scales up prefill workers to meet the heightened demand.

Beyond efficient event communication, data transfer across multi-node deployments is crucial at scale. To address this, Dynamo utilizes NIXL, a technology designed to expedite transfers through reduced synchronization and intelligent batching. This acceleration is particularly vital for disaggregated serving, ensuring minimal latency when prefill workers pass KV cache data to decode workers.

Dynamo prioritizes seamless integration. Its modular design allows it to work harmoniously with your existing infrastructure and preferred open-source components. To achieve optimal performance and extensibility, Dynamo leverages the strengths of both Rust and Python. Critical performance-sensitive modules are built with Rust for speed, memory safety, and robust concurrency. Meanwhile, Python is employed for its flexibility, enabling rapid prototyping and effortless customization.

Oh yeah, and for all the naysayers over the years, it uses Nats.io as the messaging bus. Here is the Github. Get your fork on, but please contribute back – ya hear?

Tokenized Reasoning Economy

Along with this Dynamo announcement, NVidia has created an economy around tokenized reasoning models, in a monetary sense. This is huge. Let me break this down.

Now, why call this an economy? In a monetary sense, NVIDIA’s creating a system where compute power (delivered via its GPUs) and tokens (the output of reasoning models) act like resources and currency in a marketplace. Here’s how it works:

  • Compute as the Factory: NVIDIA’s GPUs—think Blackwell Ultra or Hopper—are the engines that power these reasoning models. The more compute you throw at a problem (more GPUs, more time), the more tokens you can generate, and the smarter the AI’s answers get. It’s like a factory producing goods, but the goods here are tokens representing intelligence.
  • Tokens as Currency: In the AI world, tokens aren’t just data—they’re value. Companies running AI services (like chatbots or analytics tools) often charge based on tokens processed—say, (X) dollars per million tokens. NVIDIA’s optimizing this with tools like Dynamo, which boosts token output while cutting costs, essentially making the “token economy” more efficient. More tokens per dollar = more profit for businesses using NVIDIA’s tech. Tokens Per Second will be the new metric.
  • Supply and Demand: Demand for reasoning AI is skyrocketing—enterprises, developers, and even robotics firms want smarter systems. NVIDIA supplies the hardware (GPUs) and software (like Dynamo and NIM microservices) to meet that demand. The more efficient their tech, the more customers flock to them, driving sales of GPUs and services like DGX Cloud.
  • Revenue Flywheel: Here’s the monetary kicker—NVIDIA’s raking in billions ($39.3B in a single quarter, per GTC 2025 buzz) because every industry needs this tech. They sell GPUs to data centers, cloud providers, and enterprises, who then use them to generate tokens and charge end users. NVIDIA reinvests that cash into better chips and software, keeping the cycle spinning.

NVIDIA’s “tokenized reasoning model economy” is about turning AI intelligence into a scalable, profitable commodity—where tokens are the product, GPUs are the means of production, and the tech industry is the market. The Developers power the Flywheel. Makes the mid-90s look like Bush League sports ball.

Tori MCcaffrey Technical Product Manager Extraordinaire and Your Humble Narrator

All that is really missing is a good artificial intelligence to control the whole process. And that is the trick, isnt it? These types of blue-sky discussions always assume certain advances for a sucessful implmentation. Unfortunately, A.I. is the bottleneck in this case. We’re close with replication and manufacturing processes and we could probably build sufficiently effective ion drives if we had the budget. But we lack a way to provide enought intelligence for the probe to handle all the situations it could face.

~ Eduard Guijpers from the Convention Panel -Designing a Von Nueman Probe

Daily and Lecun – Fireside

Lecun FireSide Chat

Yann LeCun, Turing Award badass and Meta’s AI Chief Scientist brain, sat down for a fireside chat with Bill Daily, Chief Scientist at NVIDIA that cut through the AI hype. No fluffy TED Talk (or me talking) vibes here just hot takes from a guy who’s been torching (get it?) neural net limits since the ‘80s. With Jensen Huang’s “agentic AI” bomb still echoing from the keynote, LeCun brought the dev crowd at the McEnery Civic Center a dose of real talk on where deep learning’s headed.

LeCun didn’t mince words: generative AI’s cool, but it’s a stepping stone. The future’s in systems that reason, not just parrot think less ChatGPT, and more “machines that actually get real work done.” He riffed on NVIDIA’s Blackwell Ultra and GR00T robotics push, nodding to the computing muscle needed for his vision. “You want AI that plans and acts? You’re burning 100x more flops than today,” he said, echoing Jensen’s compute hunger warning. No surprise—he’s been preaching energy-efficient architectures forever.

The discussion further dug into LeCun’s latest obsession: self-supervised learning on steroids. He’s betting it’ll crack real-world perception for robots and autonomous rigs stuff NVIDIA’s Cosmos and Isaac platforms are already juicing. “Supervised learning’s dead-end for scale,” he jabbed. “Data’s the bottleneck, not flops.” There were several nods from the devs in the Civic Center. He also said we would be managing hundreds of agents in the future, vertically trained – horizontally chained so to speak.

No slides once again, just LeCun riffing extempore, per NVIDIA’s style. He dodged the Meta AI roadmap but teased “open science” wins—likely a jab at closed-shop rivals. For devs, it was a call to arms: ditch the hype, build smarter, lean on NVIDIA’s stack. With Quantum Day buzzing next door, he left us with a zinger: “Quantum’s cute, but deep nets will out-think it first.”

GTC’s “Super Bowl of AI” rep held. LeCun proved why he’s still the godfather—unfiltered, technical, and ready to break the next ceiling and pragmatic.

Jay Sales, Engineering Executive Rockstar and Your Humble Narrator

Bottom Line

GTC2025 wasn’t just a conference. GTC 2025 was NVIDIA flipping the table: AI’s industrial now, not academic. Jensen’s vision, the sessions’ grit, and the hall’s buzz screamed one thing—build or get buried. For devs, it’s a CUDA goldmine. For suits, it’s strategy. For the industry, it’s NVIDIA steering the ship—full speed into an AI agentic and robotic future. With San Jose’s dust settling, the code’s just starting to run. Big fish and small fry are all feeding on bright green chips. 5 devs can now do the output of 50. Building stuff so others can build is Our developer mantra. Always has been, always will be – Gabba Gabba Hey One Of Us, One of Us!

Huang’s overarching message was clear: AI is evolving beyond generative models into “agentic AI”—systems that can reason, plan, and act autonomously. This shift demands exponentially more compute power (100x more than previously predicted, he noted), cementing NVIDIA’s role as the backbone of this transformation.

Despite challenges—early Blackwell overheating issues, U.S. export controls, and a 13% stock dip in 2025. Whatevs. NVIDIA’s record-breaking 39.3 billion dollar revenue quarter in February proves its resilience. GTC 2025 reaffirmed that NVIDIA isn’t just riding the AI wave; it’s creating it.

One last thought: a colleague was walking with me around the conference and inquired to me how did this feel and what i thought. Context: i was in The Valley from 1992-2001 and then had a company headquartered out there from 2011-2018. i thought for a moment, looked around, and said, “This feels like 90’s on steroids, which was the heyday of embedded programming and what i think was then the height of some of the most performant code in the valley.” i still remember when at Apple the Nvidia chip was chosen over ATI’s graphics chip. NVIDIA’s stock was something like 2.65 / share. i still remember when at Microsoft the NVIDIA chip was chosen for the XBox. NVIDIA the 33 year old start-up that analyst are talking of the demise. Just like music critics – right? As i drove up and down 101 and 280 i saw all of the new buildings and names – i realized – The Valley Is Back.

until then,

#iwishyouwater <- Mark Healy Solo Outer Reef Memo

@tctjr

Muzak To Blog By: Grotus, stylized as G̈r̈oẗus̈, was an industrial rock band from San Francisco, active from 1989 to 1996. Their unique sound incorporated sampled ethnic instruments, two drummers, and two bassists, and featured angry but humorous lyrics. NIN, Mr Bungle, Faith No More and Jello Biafra championed the band. Not for the faint of heart. Nevertheless great stuff.

Note: Rumor has it the Rivian SUV does in fact, go 0-60 in 2.6 seconds with really nice seats. Also thanks to Karen and Paul for the tea and sympathy steak supper in Palo Alto, Miss ya’ll!

Only In The Valley

Posted on : By : TCTJr Posted in : Quantum_Computing

Hello Multi-Worlds With IBM Q

“If you are not completely confused by quantum mechanics, you do not understand it.”
~ John Wheeler

A chandelier or computing device?

Introduction

i wanted to take advantage of the #socialdistancing to catch up on personal blog writing. One of the areas that i have been meaning to start is my sojourn into the area of Quantum Computing specifically with IBM Q framework Qiskit (pronounced KIZ-KIT). Qiskit is an open-source quantum computing software development framework for leveraging today’s quantum processors in research, education, and business. Having read many of the latest texts (which i will add at the end of the blog) as well as initially implementing some initial Hello_World python scripts i decided to put it away due to the fact it made Alice In Wonderland’s Rabbit hole look tame. I did, however, go through some of the initial IBM Learnings and received the following:

Quantum
I am Bonafide

So given that i decided to fully re-engage and start the process the first steps as with any language or framework is to create the proverbial “Hello_World”. However, before we get into the code lets address what is in the Qiskit coding framework.

The following components are within the Qiskit framework: Terra, Aer, Aqua, and Ignis:

  • Terra: Within Terra is a set of tools for composing quantum programs at the level of circuits and pulses, optimizing them for the constraints of a particular physical quantum processor, and managing the batched execution of experiments on remote-access backends.
    • User Inputs (Circuits, and Schedules), Quantum Circuit, Pulse Schedule
    • Transpilers and optimization passes
    • Providers: Aer, IBM Quantum, and Third Party
    • Visualization and Quantum Information Tools (Histogram, State, Unitary, Entanglement)
  • Aer : It contains optimized C++ simulator backends for executing circuits compiled in Qiskit Terra and tools for constructing highly configurable noise models for performing realistic noisy simulations of the errors that occur during execution on real devices.
    • Noise Simulation (QasmSimulator Only)
    • Backends ( QasmSimulator, StatevectorSimulator, UnitarySimulator)
    • Jobs and Results: Counts, Memory, Statevector, Unitary, Snapshots
  • Aqua: Libraries of cross-domain quantum algorithms upon which applications for near-term quantum computing can be built. Aqua is designed to be extensible and employs a pluggable framework where quantum algorithms can easily be added.
    • Qiskit Aqua Translators ( Chemistry, AI, Optimization, Finance )
    • Quantum Algorithms ( QPE, Grover, HHL, QSVM, VQE, QAOA, etc… )
    • Qiskit Terra ( Compile Circuits)
    • Providers: Aer, IBM Quantium and Third Party
  • Ignis: A framework for understanding and mitigating noise in quantum circuits and systems. The experiments provided in Ignis are grouped into the topics of characterization, verification and mitigation.
    • Experiments: List of Quantum Circuits and Pulse Schedules
    • Qiskit Terra: Compile Circuits or Schedules
    • Providers: Qiskit Aer, IBM Quantum, Third Party
    • Fitters / Filters: Fit to a Model/Plot Results, Filter Noise

As one can see the components are cross-referenced across the entirety of the framework and provide the quantum developer a rich set of tools, algorithms, and methods for code creation.

Putting Your Toe In The First Quantum World

This section covers very basic quantum theory. There are several great textbooks on this subject and i will list some at the end of the blog with brief reviews. Suffice to say you cannot be scared or shy away from “greek letters or strange symbols”. To fully appreciate what is happening you need “the maths”. That said let us first define a qubit. Classical Computers operate on ( 0 ) or ( 1 ). Complete binary operations due to the nature of a diode or gate. Quantum Computers operate on quBits for Quantum Bits. These are represented by surrounding a name by ” | ” and ” > “. Thus a Qubit “named” “1” can be written as | 1\rangle. This notation is known as Dirac’s bra-ket notation. Specifically from a mathematical standpoint and this is why the above uses the label “named” it is represented by a two-dimensional vector space over complex numbers \mathbb{C}^2. This means that a Qubit takes two complex numbers to fully describe it. Okay so think about that… It takes two numbers to describe the state. Already strange huh? The computational (or standard) basis corresponds to the two levels |0\rangle and |1\rangle, which corresponds to the following vectors:

    \[\begin{split}|0\rangle = \begin{pmatrix}1\\ 0 \end{pmatrix}~~~~|1\rangle=\begin{pmatrix}0\\1\end{pmatrix}\end{split}\]

So remember that the state is described by two complex numbers. Well, the qubit does not always have to be in either |0\rangle or |1\rangle ; it can be in an arbitrary quantum state, denoted |\psi\rangle, which can be any superposition (|\psi\rangle\ = \alpha|0\rangle + \beta|1\rangle of the basis vectors. The superposition quantities \alpha and (\beta\) are complex numbers; together they obey |\alpha|^2 + |\beta| = 1 . Interesting things happen when quantum systems are measured, or observed. Quantum measurement is described by the Born rule. In particular, if a qubit in some state |\psi\rangle, is measured in the standard basis, the result 0 is obtained with probability |\alpha|^2, and the result 1 is obtained with the complementary probability |\beta|^2. Interestingly, a quantum measurement takes any superposition state of the qubit, and projects it to either the state |0\rangle or the state |1\rangle, with a probability determined from the parameters of the superposition. Whew! What i found really cool was that all of the linear algebra is the same. Here is another really cool thing: To actually create the environment the amazing scientists at IBM In the IBM Quantum Lab keep the temperature cold (15 milliKelvin in a dilution refrigerator) that there is no ambient noise or heat to excite the superconducting qubit. It is beyond the scope of why this is needed but suffices to say it involves making a superconductor, and that is when a material conducts electricity without encountering any resistance, thus without losing any energy. Ok, let’s climb out of Alice’s Rabbit Hole and get to some practical code.

Setting Up The Environment

So we are assuming the reader is familiar with setting up a python virtual environment and able to either pip install or utilize a package manager like anaconda for installing the respective libraries. The complete installation process can be found here: Installing QisKit. For completeness, i will duplicate the cogent items in the following sections. i’ll also be posting a Juypyter Notebook to github.

The simplest way to use environments is by using the conda command, included with Anaconda. A Conda environment allows you to specify a specific version of Python and set of libraries. Open a terminal window in the directory where you want to work.

Create a minimal environment with only Python installed in it.

conda create -n name_of_my_env python=3 
source activate name_of_your_env

Next, install the Qiskit package, which includes Terra, Aer, Ignis, and Aqua. ( in this writeup i will only focus on the very basics. i will get to the others in later posts! )

pip install qiskit

NOTE: Starting with Qiskit 0.13.0 pip 19 or newer is needed to install qiskit-aer from precompiled binary on Linux. If you do not have pip 19 installed you can run pip install -U pip to upgrade it. Without pip 19 or newer this command will attempt to install qiskit-aer from sdist (source distribution) which will try to compile aer locally under the covers.

If the packages installed correctly, you can run conda list to see the active packages in your virtual environment.

There are some optional packages i suggest installing for really cool circuits visualizations and like that work in conjunction with matplotlib. You can install these optional dependencies by with the following command:

pip install qiskit-terra[visualization]

To check if everything is running hop into the python prompt and type:

import Qiskit

Getting an IBM Q account and API Key

Next, you will need to register for an IBM Q account. Click this link -> Register For IBM Q Account

Here is link just in case:

https://quantum-computing.ibm.com/

IBM Q allows you to interface directly with IBM’s remote quantum hardware and quantum simulation devices. You can execute code locally on a quantum simulator however getting access to the hardware and understanding how noise affects the circuits and measurements are crucial in understanding quantum algorithm development. As with any remote system you need to lock it to an API Key. When you login you will see the following:

Generate the API token and then click on Copy API Token to copy your API Token and place into into your Jupyter Notebook. I recommend using JupyterLab Credential Store for these types of tokens and login credentials. We will come back to using the API Key so dont misplace it!

So i am assuming you made it this far and have your venv activated and your Jupyter Lab / Notebook up and running.

Check your installation by performing the following. It should print out the latest version. Also run the following commands to store your API token locally for later use in a configuration file called qiskitrc. Replace MY_API_TOKEN with the API token value that you stored in your text editor or Jupyter Notebook. Note this method saves the credentials and token to disc. It is a matter of taste you can choose in session usage as well. These are some standard imports. 

%matplotlib inline
import numpy as np
from qiskit import * 
from qiskit import IBMQ
from qiskit.tools.visualization import plot_histogram
qiskit.__version__
qiskit.__qiskit_version__

IBMQ.save_account('MY_API_TOKEN') # THIS IS YOUR API KEY FROM EARLIER!

[1]: 0.12.0

i appear to be up to date.

Next you want to make sure you are up to date on the latest versioning of the platform. Since November 2019 (and with version 0.4 of this qiskit-ibmq-provider package), the IBM Quantum Provider only supports the new IBM Quantum Experience, dropping support for the legacy Quantum Experience and Qconsole accounts. The new IBM Quantum Experience is also referred to as v2, whereas the legacy one and Qconsole as v1.

IBMQ.update_account()

Depending on your credentials you will either get a listing of updating credentials or that you are up to date.

IBM Q has various backends to run your code upon. The default is a full-fledged simulator that is invoked locally which is very convenient. The next invocation method is via direct quantum computing hardware access. i must say it is astounding that one can access via open-source quantum computing resources.

By default, all IBM Quantum Experience accounts have access to the same, open project (hub: ibm-q, group: open, project: main). For convenience, the IBMQ.load_account() and IBMQ.enable_account() methods will return a provider for that project. If you have access to other projects, you can use:

provider_2 = IBMQ.get_provider(hub='MY_HUB', group='MY_GROUP', project='MY_PROJECT')

i used the following to check out the available backends that are available. Note: The name is just a name – not the location of the hardware:

provider = IBMQ.get_provider(group='open')
provider.backends()
[10:] [<IBMQSimulator('ibmq_qasm_simulator') from IBMQ(hub='ibm-q', group='open', project='main')>,
 <IBMQBackend('ibmqx2') from IBMQ(hub='ibm-q', group='open', project='main')>,
 <IBMQBackend('ibmq_16_melbourne') from IBMQ(hub='ibm-q', group='open', project='main')>,
 <IBMQBackend('ibmq_vigo') from IBMQ(hub='ibm-q', group='open', project='main')>,
 <IBMQBackend('ibmq_ourense') from IBMQ(hub='ibm-q', group='open', project='main')>,
 <IBMQBackend('ibmq_london') from IBMQ(hub='ibm-q', group='open', project='main')>,
 <IBMQBackend('ibmq_burlington') from IBMQ(hub='ibm-q', group='open', project='main')>,
 <IBMQBackend('ibmq_essex') from IBMQ(hub='ibm-q', group='open', project='main')>,
 <IBMQBackend('ibmq_armonk') from IBMQ(hub='ibm-q', group='open', project='main')>]

Running Your First Circuits

There are several ways to run your first circuits. There is online access via in place Jupyter Notebooks as well as a visual circuit designer called IBM Circuit Composer which you can access via your IBM Q account. i will be describing steps using python code and direct Qiskit usage due to flexibility, transparency, and granularity over the environment. This will set it to the 'ibmq_qasm_simulator' 

my_provider = IBMQ.get_provider()
my_provider.backends()
my_provider.get_backend('ibmq_qasm_simulator')

So some terminology registers are used to create circuits. Circuits act upon registers. Now lets actually look at some code that generates some registers as well as a quantum circuit:

Here we a script that starts off with an input of 2 quantum “0” bits There is no action before it outputs a classical equivalent of bits:

So if you run this you will get the output:

Total count for 00 and 11 are: {'00': 517, '11': 483}

Here is what is happening:

  • QuantumCircuit.h(0): A Hadamard gate 𝐻on qubit 0, which puts it into a superposition state.
  • QuantumCircuit.cx(0, 1): A controlled-Not operation (𝐶𝑋) on control qubit 0 and target qubit 1, putting the qubits in an entangled state.
  • QuantumCircuit.measure([0,1], [0,1]): if you pass the entire quantum and classical registers to measure, the ith qubit’s measurement result will be stored in the ith classical bit.
Your First Quantum Circuit

So this is an ASCII printout. i was really impressed when i found out this tidbit. You can also pass in “mpl” for matplotlib or “latex” for full on latex beautification!

circuit.draw("mpl") and circuit.draw("latex")
Matplotlib representation of the circuit

NOTE: The latex and latex_source drawers need pylatexenc installed. Run "pip install pylatexenc" before using the latex or latex_source drawers. Professor Donald Knuth will be pleased.

#Plot a histogram
plot_histogram(counts)
Histogram showing the probability of results

The observed probabilities 𝑃𝑟(00) and 𝑃𝑟(11) are computed by taking the respective counts and dividing by the total number of shots.

Next Steps

So this is just a small step into the world of quantum programming. Below i have included several resources for study. If you are interested in pursuing this area i do urge you to take your time. i hope this at least gives you a perspective and provides a vehicle for entry. i personally feel completely humbled every time i start to read or re-read something in this area. Quantum computing is going to change the way view our world. i for one will be going deeper in this area as far as i am intellectually capable of taking the process.

NOTE: This the title of this blog refers to the theory of Minowski Multi-Worlds with a pun on Hello_World. The many-worlds interpretation implies that there is a very large—perhaps infinite number of universes. It is one of many multiverse hypotheses in physics and philosophy. MWI views time as a many-branched tree, wherein every possible quantum outcome is realized.

Resources

IBM Q User Guides All of the official IBM Q User Guides – very comprehensive.

IBM Q Wikipedia – A good readers digest of the history of IBM Q

The IBM Quantum Experience – the entry and dashboard experience

IBM Q online book – an amazing interactive experience covers everything from physics, linear algebra to code.

Mastering Quantum Computing with IBMQX – a great practical well-written book on how to get your hands coding on IBM Q

Dancing with Qubits – Written by Dr Bob Sutor of IBM a wonderful text on the mathematics and processes of quantum computing

Practical Quantum for Developers – a multi-disciplinary book that covers all aspects of coding for quantum from python, apis, cryptography, and even game theory.

Quantum Computing – A Gentle Introduction – this book covers the fundamentals of quantum computing in a very pragmatic fashion and focuses on the mathematical aspects.

Quantum Algorithms via Linear Algebra – the title is the content. ready set Linear Algebra – its the same stuff only quantum!

Quantum Computing for Computer Scientists – very close to the Gentle Introduction text however it covers the theory in-depth and also goes over several different types of algorithms.

Minowski Multi Worlds – The many-worlds interpretation implies that there is a very large perhaps infinite number of universes. It is one of many multiverse hypotheses in physics and philosophy. MWI views time as a many-branched tree, wherein every possible quantum outcome is realized. This is intended to resolve some paradoxes of quantum theory, such as the EPR paradox and Schrödinger’s cat since every possible outcome of a quantum event exists in its own universe. If you ask i’ll say the cat is dead.

Until then,

#IWishYouWater

tctjr