This content originally appeared on Level Up Coding - Medium and was authored by Mohit Sewak, Ph.D.
A review of the evidence for the quantum-native thesis and its profound implications for AI.
We’ve been building AI with the wrong math. The future of true intelligence might lie in the strange, beautiful world of quantum mechanics.
The other day, I was messing around with GPT-4, the AI wunderkind that’s got everyone from Silicon Valley CEOs to my auntie convinced the robots are coming for our jobs. For a laugh, I asked it to write a sonnet about my last kickboxing match, but in the style of Shakespeare. And you know what? It was terrifyingly good. “Hark, the shin doth crack upon the thigh,” it proclaimed, with perfect iambic pentameter. A masterpiece.
But then, I asked it a simple logic puzzle, something a clever kid could solve. The kind of thing that goes, “If a man has a fox, a goose, and a bag of beans..” And the all-powerful AI, the Shakespearean sonneteer, completely fumbled. It got stuck in a loop, offered a nonsensical answer, and then politely apologized for its confusion.
And that, right there, is the whole story of modern AI. It’s a genius and an idiot, all at the same time.
We’ve built technological marvels that can mimic human creativity with shocking fidelity. But deep down, there are fundamental cracks in the foundation. We whisper about it at conferences: they’re “stochastic parrots,” just mimicking statistical patterns without a clue what they mean (Bender et al., 2021). They’re “black boxes,” their reasoning a complete mystery, which is a terrifying prospect when we want to use them for medicine or finance. And they require planet-sized datasets and enough electricity to power a small country, which is just not sustainable.
So, what if these aren’t just engineering problems we can solve with more data and bigger computers? What if the problem is more fundamental?
What if we’ve been using the wrong kind of math all along?
Grab your tea, because we’re about to go down a rabbit hole that’s going to change how you think about language, reality, and the future of intelligence. The radical idea is this: Language isn’t fundamentally statistical; its structure is a perfect match for the mathematics of quantum mechanics.
This isn’t just a quirky analogy. This is a thesis, backed by decades of research and the first real-world experiments, that suggests we might be on the verge of a paradigm shift in AI. A shift from mimicry to meaning.
The Stakes: Why This Matters More Than Robot Poets
Look, the current way we build AI is like trying to build a skyscraper out of LEGOs. You can get impressively high, but the structure is inherently unstable, and it takes a ridiculous number of blocks. We’re hitting a wall of diminishing returns. The cost to train the next generation of LLMs is astronomical, and the gains in “intelligence” are getting smaller.
This leads us to two big, scary problems that keep people like me up at night.
First, the Interpretability Crisis. When a doctor uses an AI to help diagnose cancer, and it gives a recommendation, the first question is “Why?” If the AI’s answer is, “Because the statistical tea leaves in my billion-parameter network said so,” that’s not good enough. We can’t trust an AI we can’t audit. This is the “black box” problem, and it’s a hard stop for using AI in high-stakes fields.
Current AI models are “black boxes.” We can’t trust what we can’t understand, especially when lives are on the line.
Second, the search for “Meaning-Aware” AI. Today’s models are masters of association. They know “king” is statistically close to “queen” because those words hang out in the same sentences online (Mikolov et al., 2013). But the AI doesn’t understand royalty, power, or gender. It just knows about the statistical shadows these concepts cast in our language. It’s the difference between reading a cookbook and actually knowing how to cook.
This is where the story gets its wild plot twist. As we’ve been building bigger and bigger statistical steamrollers, another field has been coming of age: quantum computing. We’re now in what scientists call the “NISQ” era — Noisy Intermediate-Scale Quantum (Preskill, 2018). Quantum computers are no longer a sci-fi dream. They are real, noisy, cantankerous, but usable machines you can access through the cloud. And it turns out, they might be the exact tool we need to build an AI that doesn’t just mimic meaning, but actually computes it.
“The universe is not only queerer than we suppose, but queerer than we can suppose.” — J.B.S. Haldane
The Great Divorce in Linguistics (And How Quantum Heals It)
To understand why this is such a big deal, you need to know about a decades-long feud in the world of linguistics. Think of it as a clash between two rival schools of thought on how to define “meaning.”
Part A: The “Tasters” — Meaning from Context
The first school is all about Distributional Semantics. Their mantra, coined by linguist J.R. Firth, is simple and powerful: “You shall know a word by the company it keeps” (Firth, 1957).
Imagine creating a “social map” for every word in the English language. “Coffee,” “mug,” “morning,” and “caffeine” would all live in the same neighborhood on this map because they frequently appear together. “Kickboxing,” “heavy bag,” and “roundhouse” would live in another. This is the soul of modern LLMs. They are master map-makers, creating unimaginably complex vector spaces where words are just points, and meaning is the distance between them.
The Flaw? This approach is brilliant for individual words but has zero built-in understanding of grammar. The map knows “man,” “bites,” and “dog” are all words, but it has no inherent rules to understand the Grand Canyon of difference between “man bites dog” and “dog bites man.” It’s all about the ingredients, not the recipe.
Part B: The “Chemists” — Meaning from Grammar
The second school champions Compositional Semantics. They come from a world of formal logic, pioneered by thinkers like Richard Montague (Montague, 1970). They argue that the meaning of a sentence is built by applying grammatical rules to its parts, like a chemical equation.
For them, a sentence is a pristine, logical structure. A verb like “bites” is a function that needs a subject and an object to be complete. This is how they can instantly tell you that “dog bites man” is a story about an aggressive canine, not an unusual culinary choice.
The Flaw? This approach is too rigid. It’s a world of black and white, with no room for the fuzzy, nuanced, and context-dependent nature of human language. It has the recipe book but no sensory information — it can’t tell you that a “king” and a “monarch” are almost the same flavor, while a “king” and a “cabbage” are not.
For decades, these two schools have been at a standoff. Uniting the statistical “what” of word meanings with the grammatical “how” of sentence structure has been the grand challenge of NLP.
Part C: The Quantum Bridge
Then, a few brilliant researchers — most notably Bob Coecke, Mehrnoosh Sadrzadeh, and Stephen Clark — walked into the room, looked at the feud, and said, “You’re both right. And you’ve both been speaking quantum mechanics all along without knowing it.”
Quantum mechanics provides the mathematical bridge to finally unite the two warring schools of linguistics: meaning-from-context and meaning-from-grammar.
They developed a framework called the Distributional Compositional Categorical (DisCoCat) model (Coecke, Sadrzadeh, & Clark, 2010). Don’t let the name scare you. The idea is pure genius. They used a high-level mathematical toolkit called category theory to act as a universal translator. And what they found was stunning.
They proved, mathematically, that the diagram you draw to represent a sentence’s grammar is the exact same kind of diagram you draw to represent a quantum circuit (Abramsky & Coecke, 2004).
Let that sink in.
It’s not an analogy. It’s a direct, one-to-one mapping. The rules of grammar and the rules of quantum mechanics share a deep, identical mathematical structure. DisCoCat is the Rosetta Stone that allows us to translate the language of linguistics directly into the language of quantum computation. This is the bridge that heals the great divorce.
Fact Check: Category theory, the “Rosetta Stone” mentioned here, is so abstract it’s often called “generalized abstract nonsense” by mathematicians. But its power lies in revealing deep structural similarities between seemingly unrelated fields, from computer science and logic to, apparently, quantum physics and linguistics.
From Grammar to Gates: How a Sentence Becomes a Quantum Circuit
So, how does this actually work? How do you take a sentence and “run” it on a quantum computer? It’s one of the coolest ideas in modern science, and it happens in a few distinct steps. Let’s use a simple sentence: “The programmer codes the algorithm.”
Step 1: Parse the Grammar First, we do what the “Chemists” would do. We parse the sentence to get its grammatical structure. We see that “programmer” is a noun, “algorithm” is a noun, and “codes” is a transitive verb that connects them. This process creates a “wiring diagram” that shows how the words are meant to be combined (Coecke, Sadrzadeh, & Clark, 2010).
A sentence’s grammatical structure maps directly onto the structure of a quantum circuit. Nouns become quantum states (qubits) , and verbs become quantum operations (gates).
Step 2: Map Words to Quantum States This is where the quantum magic begins. Instead of representing words as statistical vectors on a classical computer, we represent them as quantum states.
- Nouns (“programmer,” “algorithm*, which carry rich meaning, become quantum states prepared on qubits. Think of them as our core ingredients.
- Verbs and relational words (“codes*, which describe actions or relationships, become quantum gates — operations that act on the qubits. Think of the verb as the recipe’s instruction: “mix,” “bake,” or “combine.”
Step 3: Entangle for Meaning When the quantum gate representing “codes” acts on the qubits for “programmer” and “algorithm,” something incredible happens: entanglement. This is the spooky quantum phenomenon where two particles become so deeply linked that their fates are intertwined, no matter how far apart they are.
In QNLP, entanglement is the perfect model for a semantic relationship. The state of “programmer” is now intrinsically linked to the states of “codes” and “algorithm.” You can no longer describe one without describing the whole system. The meaning isn’t in the individual words anymore; it’s in the holistic, entangled state of the entire circuit. The model has captured the fact that the programmer is the one doing the coding, and the algorithm is the one being coded. The recipe has been followed, and the ingredients are now a cake.
Step 4: From Sentences to Stories This idea doesn’t just stop at single sentences. A newer model called DisCoCirc extends this principle to entire texts (Coecke et al., 2020). It models a whole story as one giant, evolving quantum circuit, where each new sentence is a new set of gates that updates the states of the characters and concepts in the narrative. This is the theoretical path to modeling context, plot, and even reasoning.
This entire idea — that language is built for quantum hardware — is what researchers call the “quantum-native” thesis. It suggests that quantum computers aren’t just a faster way to do old AI; they are the correct way to compute meaning from first principles (The Quantum Insider, 2020).
QNLP in the Real World: It’s Not a Drill
This all sounds like brain-bending theory, but here’s the kicker: scientists are actually doing it. They’ve built the full pipeline to take these ideas from a whiteboard to real quantum silicon.
The process, first laid out by Meichanetzidis et al. (2020), is a hybrid affair. A classical computer parses the sentence and creates the quantum circuit blueprint. This blueprint is then sent to a quantum computer for execution. The quantum computer runs the circuit, measures the final entangled state, and sends the results back. A classical optimizer then fine-tunes the circuit’s parameters, and the process repeats. It’s a beautiful dance between the two computational paradigms.
This isn’t science fiction. Researchers are now running QNLP experiments on real quantum computers, translating sentences into circuits and executing them on actual hardware.
The first landmark proof-of-concept came from researchers at Cambridge Quantum (now Quantinuum), who ran a QNLP experiment on an IBM quantum computer (The Quantum Insider, 2020). Their detailed results were later published, showing they could classify simple sentences by converting them into quantum circuits (Lorenz et al., 2023). They were soon followed by teams at IonQ, who demonstrated similar tasks on their trapped-ion quantum systems (IonQ, n.d.).
Let’s be crystal clear. The task was simple, like distinguishing sentences about food from sentences about IT. They didn’t beat classical AI. But that wasn’t the point. The point was that it worked at all. They proved that the entire, mind-boggling theory was physically viable. The quantum oven turned on, and they successfully baked a tiny, meaningful sentence-cake.
To turbocharge this research, Quantinuum did something amazing: they open-sourced their software toolkit, lambeq (Kartsaklis et al., 2021). Think of lambeq as the "compiler for meaning." It automates the ridiculously complex process of translating a sentence into a quantum circuit, allowing any researcher with quantum access to start experimenting. This is how a niche academic theory becomes a global scientific revolution.
ProTip: The “hybrid quantum-classical” approach is the name of the game in the NISQ era. It uses quantum computers for the part of the problem they’re best at (like simulating entanglement) and classical computers for everything else (like optimization and data handling). It’s the most practical way to get value from today’s noisy quantum hardware.
A Reality Check: The Long Road to Quantum Advantage
Okay, it’s time for a splash of cold water in our chai. Before we declare the statistical parrot dead, we need to be brutally honest about the challenges.
The path to true quantum advantage is a steep and difficult climb. Today’s quantum computers are small and noisy, and they’re chasing the constantly moving target of classical AI.
First, the hardware hurdle. Today’s NISQ computers are miracles of engineering, but they’re also delicate, error-prone, and small. Noise from the environment can easily corrupt the computation, and the number of high-quality qubits is limited. This means current experiments are restricted to “toy problems” with tiny vocabularies and simple grammar. We are a long, long way from running a whole paragraph, let alone War and Peace, on a quantum computer.
Second, we must distinguish between Quantum vs. Quantum-Inspired.
- True Quantum algorithms, like the DisCoCat pipeline, must run on a quantum computer. This is a revolutionary, long-term bet on new hardware.
- Quantum-Inspired algorithms are classical tricks learned from quantum mechanics. Researchers are using the math of tensor networks, for example, to build better classical AI models that run on the GPUs we have today (Wu et al., 2021). These are providing real, near-term benefits, but they aren’t the same as the full quantum paradigm.
Finally, we’re aiming at a moving target. Classical LLMs are getting better at a ferocious pace. Proving a true “quantum advantage” — where a quantum computer solves a useful problem better, faster, or cheaper than any classical computer — is incredibly difficult when the classical competition is a multi-billion dollar industry that improves every week.
The Path Forward: A New Blueprint for AI
So, if the road is so long and the hardware so primitive, why are we so excited? Because this isn’t just about a potential speedup. It’s about building a completely new kind of AI.
The promise of QNLP is to build “Glass Box” AI. Because the structure of a quantum circuit directly mirrors the grammatical structure of the sentence, we can actually trace how the meaning was composed. We can look inside the box and see the reasoning. This provides a clear, auditable path from input to output, which is the holy grail for building trustworthy and responsible AI.
The ultimate goal of QNLP: “Glass Box” AI. By mirroring grammar in its very structure, this new type of AI promises to be interpretable, auditable, and trustworthy.
This is the path from mimicry to meaning. It’s a fundamental shift from AI that learns statistical correlations in data to AI that is grounded in the compositional, logical structure of human language.
The long-term vision is an AI that can handle nuance, ambiguity, and reasoning in a way that is far more human-like because it’s built on a mathematical framework that seems to be native to language itself. It’s a bold, first-principles approach that might just allow us to sidestep the colossal scaling and opacity problems of the current AI paradigm.
We’re at a critical fork in the road. The statistical approach has given us miracles, but it may be a dead end. Now, a new path has opened up. It suggests that the universe’s own operating system — quantum mechanics — is the native language of meaning.
The journey is long, and the quantum hardware is still in its infancy. But the intellectual foundation is solid. The question is no longer if we can build meaning-aware AI, but whether we’ve finally, after all these years, found the right blueprint to do so.
References
Here’s a reading list if you want to dive deeper into this fascinating world.
Foundational Theory (The “Why*
- Abramsky, S., & Coecke, B. (2004). A categorical semantics of quantum protocols. Proceedings of the 19th Annual IEEE Symposium on Logic in Computer Science, 2004, 415–425. https://doi.org/10.1109/LICS.2004.1319636
- Coecke, B., Sadrzadeh, M., & Clark, S. (2010). Mathematical foundations for a compositional distributional model of meaning. Journal of Logic and Computation, 20(6), 1211–1244. https://doi.org/10.1093/logcom/exq049
- Coecke, B., de Felice, G., Meichanetzidis, K., & Toumi, A. (2020). Foundations for near-term quantum natural language processing. arXiv preprint arXiv:2012.03755. https://arxiv.org/abs/2012.03755
- Zeng, W., & Coecke, B. (2016). Quantum algorithms for compositional natural language processing. arXiv preprint arXiv:1608.01406. [https://arxiv.org/abs/1608.01406]
- Firth, J. R. (1957). A synopsis of linguistic theory, 1930–1955. In Studies in Linguistic Analysis (pp. 1–32). Basil Blackwell.
- Lambek, J. (2008). From word to sentence. In From Word to Sentence: A Computational Algebraic Approach to Grammar (pp. 1–24). Polimetrica.
- Montague, R. (1970). Universal grammar. Theoria, 36(3), 373–398. https://doi.org/10.1111/j.1755-2567.1970.tb00434.x
Experimental Realizations (The “How*
- Lorenz, R., Pearson, A., Meichanetzidis, K., Kartsaklis, D., & Coecke, B. (2023). QNLP in practice: Running compositional models of meaning on a quantum computer. Journal of Artificial Intelligence Research, 76, 1305–1342. https://doi.org/10.1613/jair.1.14412
- Meichanetzidis, K., Gogioso, S., de Felice, G., Chiappori, N., Toumi, A., & Coecke, B. (2020). Quantum natural language processing on near-term quantum computers. arXiv preprint arXiv:2005.04147. [https://arxiv.org/abs/2005.04147]
- IonQ. (n.d.). Quantum natural language processing with IonQ hardware. Retrieved October 26, 2025, from https://ionq.com/resources/quantum-natural-language-processing-with-ionq-hardware
- Preskill, J. (2018). Quantum computing in the NISQ era and beyond. Quantum, 2, 79. https://doi.org/10.22331/q-2018-08-06-79
Tooling and Ecosystem (The “Tools*
- Kartsaklis, D., Fan, I., Yeung, R., Pearson, A., Lorenz, R., Toumi, A., de Felice, G., Meichanetzidis, K., Clark, S., & Coecke, B. (2021). lambeq: An efficient high-level python library for quantum NLP. arXiv preprint arXiv:2110.04236. [https://arxiv.org/abs/2110.04236]
- Sivarajah, S., Dilkes, S., Cowtan, A., Simmons, W., Edgington, A., & Duncan, R. (2020). t|ket⟩: A retargetable compiler for NISQ devices. Quantum Science and Technology, 6(1), 014003. https://doi.org/10.1088/2058-9565/ab8e92
- The Quantum Insider. (2020, December 10). CQC researchers make major quantum NLP advance in steps toward ‘meaning aware’ computers. Retrieved October 26, 2025, from https://thequantuminsider.com/2020/12/10/cqc-researchers-make-major-quantum-nlp-advance-in-steps-toward-meaning-aware-computers/
Surveys and Future Directions (The “Big Picture*
- Wu, S., Li, J., Zhang, P., & Zhang, Y. (2021). Natural language processing meets quantum physics: A survey and categorization. In Proceedings of the 2021 Conference on Empirical Methods in Natural language Processing (pp. 3172–3182). Association for Computational Linguistics. https://aclanthology.org/2021.emnlp-main.256/
- The Quantum Insider. (2024, February 18). How can quantum computers make AI smarter? Quantinuum researchers review quantum path to effective, transparent AI. Retrieved October 26, 2025, from https://thequantuminsider.com/2024/02/18/how-can-quantum-computers-make-ai-smarter-quantinuum-researchers-review-quantum-path-to-effective-transparent-ai/
- Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). https://doi.org/10.1145/3442188.3445922
- Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems 26 (pp. 3111–3119). https://papers.nips.cc/paper/2013/hash/9aa42b31882ec039965f3c4923ce901b-Abstract.html
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems 30 (pp. 5998–6008). Curran Associates, Inc. https://papers.nips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html
Disclaimer: The views and opinions expressed in this article are solely my own and do not reflect the official policy or position of any past or present employer. AI assistance was used in the research and drafting of this article, including the generation of illustrative images. This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License (CC BY-ND 4.0).
Language Is Not Statistical; It’s Quantum Mechanical was originally published in Level Up Coding on Medium, where people are continuing the conversation by highlighting and responding to this story.
This content originally appeared on Level Up Coding - Medium and was authored by Mohit Sewak, Ph.D.
Mohit Sewak, Ph.D. | Sciencx (2025-11-19T17:48:21+00:00) Language Is Not Statistical; It’s Quantum Mechanical. Retrieved from https://www.scien.cx/2025/11/19/language-is-not-statistical-its-quantum-mechanical/
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