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The AI-Quantum Convergence: How Information Compression Powers the Future

Peter H. Diamandis · 1:21:37 runtime

AI and quantum, though seemingly different, share a core commonality: both compress vast amounts of data into manageable information. This convergence enables Large Quantitative Models (LQMs) that leverage quantum equations on GPUs to revolutionize drug discovery, materials science, and energy without waiting for fault-tolerant quantum computers.

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Setting the Stage: Jack Hidary and Sandbox AQ

Decades of Research, Limited Progress

Alzheimer's, Parkinson's, dementia, and cancer have seen decades of research with few effective treatments, highlighting the urgent need for new approaches.

Instead of the world of large language models, we've now entered the world, Peter, of large quantitative models, LQMs.
$500M
Funding raised by Sandbox AQ in a single round, with Eric Schmidt as chairman.

Jack Hidary's Journey

1999
Founded Vista Research.
2016
Founded quantum group at Alphabet, working with Sergey Brin and Astro Teller.
2022
Spun out Sandbox AQ, became CEO, raised $500M with Eric Schmidt as chairman.
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Why AI and Quantum? The Nexus of Information Compression

Grand Challenges in Medicine and Energy

Despite decades of research, Alzheimer’s, Parkinson’s, pancreatic cancer, and glioblastoma remain untreatable. Energy transition progress is halting. These massive challenges demand new tools.

40 years
Alzheimer's research with no effective treatment
Steve Jobs died of pancreatic cancer, billions in the bank account, nothing to do.

The AI–Quantum Nexus

Both AI and quantum technology model the world by compressing enormous datasets into manageable, actionable insights, enabling predictions and outputs to tackle grand challenges.

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Large Language Models: Strengths and Limitations

86–100 billion
Neurons in the human brain, loosely inspiring artificial neural networks

Evolution of Language Models

1943
McCulloch and Pitts propose the first mathematical model of a neural network.
Pre‑2017
Recurrent neural networks (RNNs) show predictive ability but are too slow for real‑time use.
2017
Google’s “Attention Is All You Need” paper introduces transformers, exploiting GPU parallelizability.
Present
Massive LLMs (GPT‑4, Llama, Gemini) with hundreds of billions to trillions of parameters dominate.

LLMs as compression engines

A large language model compresses a huge text corpus down to its essence — capturing patterns like “carness” — in the weights of the network, allowing it to generate coherent outputs.

There is no equation for the English language.
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Beyond Words: The Quantitative World of Numbers

The Quantitative Nature of Reality

Most real-world challenges, from drug design to battery chemistry, are governed by numbers and physical laws, not by language. Language models alone cannot solve these problems without precise mathematical understanding.

the majority of our world, Peter, is not words, but numbers.

Quantum Equations Underpinning Physical Laws

Schrödinger's equation
Describes the wave function of a quantum system.
Heisenberg's uncertainty principle
Limits the precision of simultaneous measurements.
Planck's equation
Relates energy to frequency of radiation.
Born's rule
Probabilistic interpretation of quantum mechanics.
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From Newton to Quantum: Equations as Ultimate Compressors

Equations as Ultimate Compressors

Newton's laws compress macro-scale dynamics into simple equations, while quantum equations compress subatomic behaviors, enabling precise modeling from rocket trajectories to drug design.

Everything is Quantum.
5
Papers in Einstein's 1905 annus mirabilis, including the photoelectric effect paper that won the Nobel Prize.

Key Quantum Milestones

1900
Max Planck presents blackbody radiation solution, introducing quantum theory.
1905
Einstein's annus mirabilis: photoelectric effect paper (Nobel Prize), building on Planck.
1920s
Schrödinger, Heisenberg, Dirac develop quantum mechanics equations.
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Modeling Drug Targets with Quantum Precision

Beyond Classical Protein Modeling

While AI tools like AlphaFold predict static protein folding, SandboxAQ leverages quantum equations to model dynamic electron interactions—critical for designing drugs like cancer immunotherapies that target protein receptors.

2-3 years
Time since SandboxAQ first achieved quantum-level modeling of electron interactions in biological systems.

Evolution of Protein Modeling

Classical AI
AlphaFold predicts folding from sequences but ignores electron interactions.
Quantum Equations
SandboxAQ applies Schrödinger's equation to model electron dynamics, revealing binding mechanisms.
Quantum Computers
Future fault-tolerant quantum computers will scale these computations further (decades away).
And that's the level now, finally, that we at SandboxAQ have been able to model things at. And that is a big breakthrough.
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The Breakthrough: Solving Quantum Equations on GPUs

GPUs Solve Quantum Equations Today

The same GPU infrastructure that enabled large language models can accurately solve classical quantum equations—Schrödinger, Bohr, Planck, Heisenberg—without requiring a scaled quantum computer. This allows immediate modeling of atoms, electrons, and ions, unlocking insights across health, materials, and the environment.

1 billion
Potential number of molecular permutations explored per drug candidate to train AI models.

How Large Quantitative Models (LQMs) Generate Pristine Training Data

  1. Start with a target molecule (e.g., a drug for glioblastoma).
  2. Create a digital twin of that molecule.
  3. Generate millions to billions of chemical permutations (adding/removing methyl, amine, nitrogen groups, etc.).
  4. Solve quantum equations on GPUs for each permutation to produce a clean, physics-based dataset.
  5. Train AI models on this generated data to predict which variants will effectively hit the target.

LLMs vs. LQMs: Where the Data Comes From

Large Language Models (LLMs)
Trained on existing text corpora; cannot discover molecules outside the training data.
Large Quantitative Models (LQMs)
Generate their own training data from quantum simulations, enabling discovery of novel compounds.
So, first there we take the quantum equations, we run those, and that becomes the data set. So, we're generating our data set and that's what we use to train the AI.
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LQMs in Action: From Simulations to Real-World Impact

Large Quantitative Models (LQMs)

Instead of relying on messy real-world data, LQMs start with the pristine quantum equations of the universe, generate accurate synthetic data, and train AI to tackle everything from drug discovery to new battery chemistries.

40+ years
Lithium-ion chemistry has dominated for over four decades; we need new battery chemistries for lighter, cheaper alternatives.

How LQMs Power Real-World Solutions

  1. Start with the quantum equations as the bedrock
  2. Generate diverse synthetic data by creating variations on themes (e.g., molecules, ions)
  3. Train large quantitative models on this pristine data
  4. Apply models to design new drugs, biomarkers, batteries, and materials
~1 year
Perovskite solar cells currently stable for only about a year, far short of the 25-year guarantee required for commercial deployment.
▸ 47:31

Revolutionizing Drug Development and Clinical Trials

Quantum-Enabled AI Tackles Molecular Complexity

By focusing only on the valence electrons at the business end of a molecule, SandboxAQ makes molecular modeling tractable on today's GPUs, unlocking a new era of quantitative AI for drug discovery.

90%
of drugs entering clinical trials fail to reach approval, highlighting the inefficiency of traditional drug development.

Transforming Drug Development Metrics

Cost per Drug
From $3B to $300M
Timeline
From 10 years to 5 years
Success Rate
From 10% to 50-60%
90% failure today in clinic. 90 out of 100 drugs that go into clinical phase one trial, phase two, phase three, never see the light of day.
▸ 1:04:48

The Road Ahead for Quantum Computing

1000:1
Estimated ratio of physical qubits needed for each error-corrected (logical) qubit to achieve useful quantum computing.

Quantum Computing Milestones

1979
Paul Benioff publishes the first paper describing a quantum computer, laying the conceptual foundation.
2029
Modular quantum building blocks with 1,000–5,000 physical qubits emerge, enabling daisy-chaining into mega computers.
2031–2032
Error-corrected, mega quantum computers with thousands of logical qubits become operational, marking a critical inflection point.

Current Qubit Technologies

Neutral atoms, a rapidly scaling dark horse, use lasers to manipulate atoms near absolute zero, offering room-temperature operation and compact form factors. Photonics leverages silicon photonics and semiconductor fabs for mass production, with key players like PsiQuantum and Photonic.

How a Qubit is Used

  1. Initiate a state on the qubit (e.g., using lasers to set a neutral atom into 0, 1, or a superposition).
  2. Perform a set of operations (gates) on the qubit states.
  3. Read out the final state at the end of the computation.
they can also be in superpositions, in combinations of zero and one. And that gives us an infinite palette to draw from.
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Information: The Unifying Principle

Quantum Sensing is Already Here

Unlike quantum computers, quantum sensors require no error correction or millions of qubits; they are operational today in planes for navigation when GPS is denied and in hospitals for magnetocardiography.

Information: The Unifying Principle

Both AI and quantum physics rely on summarizing large amounts of data or complexity into compact representations: neural networks learn compressed representations, while physics condenses dynamics into equations.

This fundamental insight that information is the building block of our universe.
▸ 1:18:45

Parting Thoughts: Questions over Answers

If we are in a simulation, then the beings who created the simulation, kudos to them. They've done a pretty good job.

Questions Spark Breakthroughs

1900
David Hilbert poses 23 mathematical challenges.
Early 1900s
Henri Poincaré publishes a book with physics challenges.
1905
Einstein, inspired by Poincaré, publishes his annus mirabilis papers.

The Power of Questions

Contributions to society can take the form of not just answers, but the questions we pose to ourselves and the next generation.

Let's focus on the questions and not just the answers.