Pith Number

pith:E23TLP3F

pith:2025:E23TLP3F4AZIL5PWIXZ3ZYADFM

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Limitations of Quantum Advantage in Unsupervised Machine Learning

Apoorva D. Patel

Quantum advantage in unsupervised machine learning arises only when exploiting features of density matrices not found in classical probability distributions.

arxiv:2511.10709 v2 · 2025-11-13 · quant-ph · cs.LG

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

An advantage can be obtained only when features of density matrices that are absent in classical probability distributions are exploited. Such situations depend on the input data as well as the targeted observables.

C2weakest assumption

That the classical baseline is restricted to Boltzmann distributions with tunable parameters and that no other classical or hybrid techniques can capture the same quantum-like features without actual quantum hardware.

C3one line summary

Quantum advantage in unsupervised machine learning is limited to cases where density-matrix features absent from classical distributions can be exploited, with explicit examples showing strong dependence on input data and target observables.

References

10 extracted · 10 resolved · 0 Pith anchors

[1] 1 A2# QB a 3 Rq b 5'S6DTsEF7Gc(UVW dte)8fu*9:HIJXYZghijvwxyz m ! 1 1914

[2] J. D. Kelleher, B. Mac Namee and A. D'Arcy, Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies , Second Edition, The MIT Press, Cambridge MA, 2015

[3] S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach , Fourth Edition, Pearson, India; 2022 2022

[4] R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction , Second Edition, The MIT Press, Cambridge MA, USA; 2020 2020

[5] M. Schuld and F. Petruccione, Machine Learning with Quantum Computers , Second Edition, Springer Nature Switzerland AG; 2021 2021

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T02:44:32.894365Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

26b735bf65e03285f5f645f3bce0032b0b1a195e4472b0c676940c79f6f5d9d2

Aliases

arxiv: 2511.10709 · arxiv_version: 2511.10709v2 · doi: 10.48550/arxiv.2511.10709 · pith_short_12: E23TLP3F4AZI · pith_short_16: E23TLP3F4AZIL5PW · pith_short_8: E23TLP3F

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/E23TLP3F4AZIL5PWIXZ3ZYADFM \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 26b735bf65e03285f5f645f3bce0032b0b1a195e4472b0c676940c79f6f5d9d2

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "edc7bba07b767902011bd0b924907a987b2e08f495dde2d13c706b21a81e1889",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "quant-ph",
    "submitted_at": "2025-11-13T08:50:40Z",
    "title_canon_sha256": "31f8dabc9e554fe3923855f784c7096a629a4702cf5c89b5662bd6d26cb7b274"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2511.10709",
    "kind": "arxiv",
    "version": 2
  }
}