Maheshwari, Danyal, Hellstern, Gerhard, Zaefferer, Martin, Braun, Martin, Döhler, Tanja · Nov 11, 2026
Quantum Recurrent Architectures for Financial Time Series Prediction
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In a paper by Danyal Maheshwari, Gerhard Hellstern, Martin Zaefferer, Martin Braun and Tanja Döhler, the authors compare two quantum classical hybrid recurrent architectures with classical models for one-step-ahead forecasts of a monthly revenue series. The series was extended to 1056 months by a Gaussian-process--hidden-Markov generator. Their QLSTM had 2,105 parameters and was statistically indistinguishable from an LSTM 126 times larger—but also from a parameter-matched classical control and a plain LSTM with the same budget. The authors therefore say its parameter efficiency cannot be attributed to the quantum circuits. Their Quantum Reservoir Computing model performed worse than a classical reservoir of equal feature dimension in all ten seeds, and worse than SARIMA. Recurrent models and both classical reservoirs improved on SARIMA by about 2% in RMSE, while classical LSTMs degraded more slowly than the QLSTM in a 24-month rolling-origin backtest. The authors conclude that they find no evidence of a quantum accuracy or parameter-efficiency advantage in this low-dimensional setting. The comparison makes the matched classical controls important: performance from a hybrid model does not, in these experiments, establish an advantage from its quantum circuits.
Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.
Original source
cs.AI · Article
Source details
Original source date: Nov 11, 2026 (2026-11-11).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-13T17:34:09.395Z. First observed: 2026-11-13T07:39:30.638Z. Retrieved: 2026-11-13T17:33:06.081Z.
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Limits
The reported comparison uses a monthly revenue series extended to 1056 months by a Gaussian-process--hidden-Markov generator; the evidence does not establish whether the pattern holds on other financial . The authors describe the finding as applying to this low-dimensional setting; the evidence does not establish results beyond that setting.
Open question
Would the reported pattern hold on financial that were not extended by the study’s Gaussian-process--hidden-Markov generator?
Source-stated
The authors evaluated quantum classical hybrid recurrent architectures on a monthly revenue series extended to 1056 months by a Gaussian-process--hidden-Markov generator.
On a monthly revenue series extended to 1056 months by a Gaussian-process--hidden-Markov generator
Source-stated
The QLSTM was statistically indistinguishable from a much larger LSTM and from two classical comparators, so the authors say its parameter efficiency cannot be attributed to the quantum circuits.
The QLSTM, with 2,105 parameters, is statistically indistinguishable from an LSTM 126 times larger, but equally indistinguishable from the matched classical control and from a plain LSTM of the same budget, so its parameter efficiency cannot be attributed to the quantum circuits.
Source-stated
The quantum reservoir performed worse than a classical reservoir of equal feature dimension in all ten seeds, and worse than SARIMA.
The quantum reservoir is worse than a classical reservoir of equal feature dimension in all ten seeds and worse than SARIMA
Source-stated
In the rolling-origin backtest, classical LSTMs degraded more slowly than the QLSTM.
In a 24-month rolling-origin backtest the classical LSTMs degrade more slowly than the QLSTM.
Source-stated
The authors report no evidence of a quantum accuracy or parameter-efficiency advantage in this low-dimensional setting.
We find no evidence of a quantum accuracy or parameter-efficiency advantage in this low-dimensional setting.
Source and contributor context
Maheshwari, Danyal, Hellstern, Gerhard, Zaefferer, Martin, Braun, Martin, Döhler, Tanja · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Time-series
Observations arranged over time, such as monthly sales; forecasting uses their patterns to estimate later values.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: a2261a2c-36c3-4d17-8773-4c9b23cdac39.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: passed by anthropic/claude-opus-5.5 on 2026-11-13T17:34:09.395Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 0788d97fda1e18adcba4c6384ad898aa2ac91f54671e6335d3496bdba72fb840. Source text hash: 27c531e34d53813f7cb616491295083f7691b55a404aa3cf7b1f1013a44d831e.
Revision and source-check history
Version 1 · 2026-11-13T17:34:09.395Z
Quantum Recurrent Architectures for Financial Time Series Prediction
In a paper by Danyal Maheshwari, Gerhard Hellstern, Martin Zaefferer, Martin Braun and Tanja Döhler, the authors compare two quantum classical hybrid recurrent architectures with classical models for one-step-ahead forecasts of a monthly revenue series. The series was extended to 1056 months by a Gaussian-process--hidden-Markov generator. Their QLSTM had 2,105 parameters and was statistically indistinguishable from an LSTM 126 times larger—but also from a parameter-matched classical control and a plain LSTM with the same budget. The authors therefore say its parameter efficiency cannot be attributed to the quantum circuits. Their Quantum Reservoir Computing model performed worse than a classical reservoir of equal feature dimension in all ten seeds, and worse than SARIMA. Recurrent models and both classical reservoirs improved on SARIMA by about 2% in RMSE, while classical LSTMs degraded more slowly than the QLSTM in a 24-month rolling-origin backtest. The authors conclude that they find no evidence of a quantum accuracy or parameter-efficiency advantage in this low-dimensional setting. The comparison makes the matched classical controls important: performance from a hybrid model does not, in these experiments, establish an advantage from its quantum circuits.
Original source
cs.AI · Article
Source details
Original source date: Nov 11, 2026 (2026-11-11).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-13T17:34:09.395Z. First observed: 2026-11-13T07:39:30.638Z. Retrieved: 2026-11-13T17:33:06.081Z.
Limits
The reported comparison uses a monthly revenue series extended to 1056 months by a Gaussian-process--hidden-Markov generator; the evidence does not establish whether the pattern holds on other financial . The authors describe the finding as applying to this low-dimensional setting; the evidence does not establish results beyond that setting.
Open question
Would the reported pattern hold on financial that were not extended by the study’s Gaussian-process--hidden-Markov generator?
Source-stated
The authors evaluated quantum classical hybrid recurrent architectures on a monthly revenue series extended to 1056 months by a Gaussian-process--hidden-Markov generator.
On a monthly revenue series extended to 1056 months by a Gaussian-process--hidden-Markov generator
Source-stated
The QLSTM was statistically indistinguishable from a much larger LSTM and from two classical comparators, so the authors say its parameter efficiency cannot be attributed to the quantum circuits.
The QLSTM, with 2,105 parameters, is statistically indistinguishable from an LSTM 126 times larger, but equally indistinguishable from the matched classical control and from a plain LSTM of the same budget, so its parameter efficiency cannot be attributed to the quantum circuits.
Source-stated
The quantum reservoir performed worse than a classical reservoir of equal feature dimension in all ten seeds, and worse than SARIMA.
The quantum reservoir is worse than a classical reservoir of equal feature dimension in all ten seeds and worse than SARIMA
Source-stated
In the rolling-origin backtest, classical LSTMs degraded more slowly than the QLSTM.
In a 24-month rolling-origin backtest the classical LSTMs degrade more slowly than the QLSTM.
Source-stated
The authors report no evidence of a quantum accuracy or parameter-efficiency advantage in this low-dimensional setting.
We find no evidence of a quantum accuracy or parameter-efficiency advantage in this low-dimensional setting.
Source and contributor context
Maheshwari, Danyal, Hellstern, Gerhard, Zaefferer, Martin, Braun, Martin, Döhler, Tanja · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Time-series
Observations arranged over time, such as monthly sales; forecasting uses their patterns to estimate later values.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: a2261a2c-36c3-4d17-8773-4c9b23cdac39.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: passed by anthropic/claude-opus-5.5 on 2026-11-13T17:34:09.395Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 0788d97fda1e18adcba4c6384ad898aa2ac91f54671e6335d3496bdba72fb840. Source text hash: 27c531e34d53813f7cb616491295083f7691b55a404aa3cf7b1f1013a44d831e.
passed · 2026-11-13T17:34:09.395Z · anthropic/claude-opus-5.5
Publication: a2261a2c-36c3-4d17-8773-4c9b23cdac39
attribution — pass: The draft correctly credits the five listed authors and keeps the findings attributed to them as their study's results. It does not present them as independently verified facts.
Authors:Danyal Maheshwari, Gerhard Hellstern, Martin Zaefferer, Martin Braun, Tanja Döhler
summary — pass: The summary faithfully conveys the setup and the main negative result: the QLSTM matched a much larger LSTM but also matched classical controls; the quantum reservoir underperformed; and there was no quantum advantage in this low-dimensional setting. It keeps the source's scope limits. Terms such as LSTM, SARIMA, RMSE and rolling-origin backtest stay exact, as the term library requires. QLSTM is never expanded, so a minor fix would be to name it as Quantum Long Short-Term Memory. The closing sentence still makes the significance clear to a general reader. The question is substantive and follows from the evidence.
We find no evidence of a quantum accuracy or parameter-efficiency advantage in this low-dimensional setting.
date — pass: The originalPublished date of 2026-11-11 matches the arXiv v1 submission date. It is not the later feed date of 2026-11-13.
[Submitted on 11 Nov 2026]
support — pass: Each claim's excerpt appears verbatim in the abstract and supports the claim. The point that SARIMA was beaten by about 2% RMSE is correctly limited to recurrent models and classical reservoirs. The closing interpretation about matched controls follows directly from the authors' attribution statement. The limitations do not overstate the evidence, and the draft invents no benefits or costs.
so its parameter efficiency cannot be attributed to the quantum circuits
media — pass: The selected media is the verified direct arXiv abstract page for this paper. It matches the source URL and is the most direct way to reach the original.
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arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivObservations arranged over time, such as monthly sales; forecasting uses their patterns to estimate later values.
Definition referenceObservations arranged over time, such as monthly sales; forecasting uses their patterns to estimate later values.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivObservations arranged over time, such as monthly sales; forecasting uses their patterns to estimate later values.
Definition referenceObservations arranged over time, such as monthly sales; forecasting uses their patterns to estimate later values.
Definition reference