Quantum machine learning researcher applying variational quantum models to time-series and financial forecasting
2
papers here
1
reviewed by Evolve
0
first-author
0
corresponding
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works · OpenAlex
0
h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-10-17) and cover the author's entire public record, not just the papers listed here. Reproducibility signal comes from Evolve's own reviews.
Research profile
Output—
No output score is available: the snapshot records an h-index and i10-index of 0 with no reliable publication totals beyond the two captured works. This axis would measure total papers, preprints, and venue types under fuller indexing.
Impact—
No impact score is available: with an h-index and i10-index of 0 and no citation counts in the snapshot, influence cannot yet be assessed. This axis would measure citations, citations-per-paper, and citation trajectory as the record matures.
Focus100/100
This researcher holds the platform's maximum concentration score (100/100). The OpenAlex topic distribution is tightly clustered: Quantum Computing Algorithms and Architecture (2 works) and Stock Market Forecasting Methods (2 works) lead, with single works in Risk and Portfolio Optimization and Hydrological Forecasting Using AI, and the recorded field distribution lists quantum machine learning alone. The consistent thread is quantum models pointed at forecasting problems.
Collaboration36/100
The collaboration score is 36/100, based on 5 distinct coauthors in the OpenAlex snapshot. The frequent-collaborator list — Yun-Cheng Tsai, Mu-En Lee, Yen-Ku Liu, Ming-Kai Hung, and Jun-Hao Chen — matches that count exactly, suggesting a small, stable working group. On the two cataloged 2026 papers the positions are middle and last author, neither corresponding, consistent with a contributing member of a compact team.
Activity32/100
The activity score is 32/100. The OpenAlex snapshot records 2 works in the recent three-year window, and the by-year counts show 2 publications in 2026 with no earlier years captured. Both cataloged papers are 2026 arXiv preprints, so observed output is concentrated in a single year; the low score partly reflects the snapshot's limited coverage window rather than confirmed inactivity.
Reproducibility55/100
The reproducibility score is 55/100, computed from 1 reviewed paper with a mean readiness of 55. The reviewed recursive QLSTM temperature-forecasting study was rated medium: Evolve credits a solid multi-seed experimental design on Toronto temperature data, but also flags the absence of classical baselines and statistical significance tests, which currently caps how far the results can be independently verified.
Research summary
Samuel Yen-Chi Chen is a quantum machine learning researcher whose recent work applies variational and recurrent quantum architectures to forecasting problems. The OpenAlex-linked snapshot shows an exceptionally concentrated research profile — a focus score of 100/100 — with recorded topics spanning Quantum Computing Algorithms and Architecture (2 works), Stock Market Forecasting Methods (2 works), Risk and Portfolio Optimization (1), and Hydrological Forecasting Using AI (1), and a field distribution listing quantum machine learning. Two 2026 arXiv preprints anchor the catalog: a recursive quantum long short-term memory (QLSTM) model for stable short-horizon temperature forecasting, which Evolve rated a solid multi-seed study while noting missing classical baselines and significance tests (medium reproducibility readiness), and Titans-QFWP, a regime-aware hybrid quantum fast weight programmer for portfolio optimization. Authorship on both is collaborative — middle author on the former, last author on the latter, neither corresponding — within a compact group of 5 distinct coauthors including Yun-Cheng Tsai, Mu-En Lee, Yen-Ku Liu, Ming-Kai Hung, and Jun-Hao Chen. The quantitative footprint is still small: the snapshot reports an h-index and i10-index of 0, 2 works in the recent three-year window, and no output or impact scores. This is best read as an early-stage profile whose methodological direction is clear but whose bibliometric record has not yet matured.
Collaboration pattern
Collaboration is concentrated in one small group: the snapshot lists 5 distinct coauthors, exactly matching the frequent-collaborator set of Yun-Cheng Tsai, Mu-En Lee, Yen-Ku Liu, Ming-Kai Hung, and Jun-Hao Chen. The collaboration score is a modest 36/100. On the two cataloged 2026 papers this researcher appears as middle author on the recursive QLSTM forecasting study and last author on Titans-QFWP, neither corresponding — a pattern of steady contributions within one fixed team.
Research keywords
Highlights
Recursive QLSTM for stable short-horizon temperature forecasting
Titans-QFWP: quantum fast weight programming for portfolio optimization
A second 2026 arXiv preprint (last authorship) introduces a regime-aware hybrid quantum fast weight programmer targeting portfolio optimization, extending the group's quantum-forecasting line from weather data into financial applications. It is listed in the catalog without an Evolve verdict or reproducibility rating yet.
A tightly focused quantum-ML-for-forecasting agenda
Across both papers and the OpenAlex topic record — Quantum Computing Algorithms and Architecture (2), Stock Market Forecasting Methods (2), Risk and Portfolio Optimization (1), Hydrological Forecasting Using AI (1) — the work consistently pairs quantum models with time-series prediction, which is why the profile earns a maximum focus score of 100/100.
Timeline
- 2026Published the recursive QLSTM study for stable short-horizon temperature forecasting on arXiv (middle author); Evolve rated the multi-seed study medium for reproducibility, noting missing classical baselines and significance tests.
- 2026Released Titans-QFWP, a regime-aware hybrid quantum fast weight programmer for portfolio optimization, on arXiv as last author.
- 2026OpenAlex snapshot records both works in this single year: 2 publications in the by-year counts, 1 paper reviewed for reproducibility (mean 55), and 5 distinct coauthors.
Papers in the Evolve catalog
Frequent co-authors
About this profile
This profile is AI-structured by Evolve.Pub from public bibliographic data (OpenAlex) and Evolve's own paper reviews. It is not written or endorsed by the researcher unless the profile is claimed. Radar dimensions are computed from real bibliometrics with fixed formulas — raw figures are shown alongside; missing dimensions mean insufficient data, never zero. Errors? Claim the profile to correct or hide it.
- Bibliometrics come from an OpenAlex snapshot only; the h-index and i10-index are recorded as 0 and no citation data is available, so the output and impact dimensions are unscored.
- This profile is name-resolved from public bibliographic data; institution, country, and ORCID are not available, and name collisions with other researchers are possible.
- The snapshot captures only 2 works (both 2026); any earlier publications are not reflected in these metrics.
- The reproducibility score (55) rests on a single reviewed paper, so it should be treated as indicative rather than established.