Pith Number

pith:SNKGECC7

pith:2026:SNKGECC72POWEMYDAOQ5EK5LQN

not attested not anchored not stored refs resolved

A Quantum Reservoir Computing Approach to Quantum Stock Movement Forecasting in Quantum-Invested Markets

Alexander G. Balanov, Alexandre Zagoskin, Juan Totero Gongora, Sergey E. Savel'ev, Wendy Otieno

A quantum reservoir of six qubits classifies stock trends with over 86% accuracy.

arxiv:2602.13094 v2 · 2026-02-13 · quant-ph · physics.data-an

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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

Our analysis identifies optimal reservoir parameters that yield stock trend (up/down) classification accuracies exceeding 86 %.

C2weakest assumption

The quantum reservoir dynamics, when driven by the chosen parameters, genuinely capture the temporal correlations present in the financial volume data rather than fitting noise or historical artifacts specific to the 2020-2025 period.

C3one line summary

A six-qubit quantum reservoir achieves over 86% accuracy in classifying stock trend movements for quantum-sector companies using daily and intraday volume data.

References

83 extracted · 83 resolved · 4 Pith anchors

[1] A Quantum Reservoir Computing Approach to Quantum Stock Movement Forecasting in Quantum-Invested Markets 1992 · arXiv:2602.13094

[2] by showing that mood dimensions significantly im- proves DJIA prediction accuracy (predicted accuracy of 87.6%) in DJIA’s up/down closing values [10]. Apart from forecasting the stock market’s directi

[3] All these are beneficial in tackling physical qubit constraints, fabri- cation challenges, noise sensitivity and control complexi- ties of qubits 2020

[4] Micro/Small-cap markets - QMCO, RGTI, QS, IONQ, QTUM, QSI, QBTS, LAES, ZPTA, FORM, ARQQ

[5] Large-cap markets - IBM, HON, INTC

Formal links

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1 paper in Pith

A Quantum Reservoir Computing Approach to Quantum Stock Movement Forecasting in Quantum-Invested Markets

Receipt and verification

First computed	2026-05-18T02:44:31.180207Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

935462085fd3dd62330303a1d22bab8352ca61743403a41d70bcd68964c50228

Aliases

arxiv: 2602.13094 · arxiv_version: 2602.13094v2 · doi: 10.48550/arxiv.2602.13094 · pith_short_12: SNKGECC72POW · pith_short_16: SNKGECC72POWEMYD · pith_short_8: SNKGECC7

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/SNKGECC72POWEMYDAOQ5EK5LQN \
  | 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: 935462085fd3dd62330303a1d22bab8352ca61743403a41d70bcd68964c50228

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "dc61de12803c8615de1f3453d9e0b8ea7e5b4b565ce0257f1ada5dd214eb40d9",
    "cross_cats_sorted": [
      "physics.data-an"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "quant-ph",
    "submitted_at": "2026-02-13T17:00:03Z",
    "title_canon_sha256": "9f979422ba234858e147a5421639b324155f62df9101e373a5c6d03ce497ec92"
  },
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  "source": {
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    "kind": "arxiv",
    "version": 2
  }
}