Quantum machine learning × neural quantum states: Waterloo researcher, hardware-validated QML, reproducibility mean 78
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papers here
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reviewed by Evolve
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first-author
2
corresponding
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works · OpenAlex
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h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-10-09) 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 metric is computable yet: the snapshot holds only two arXiv entries, with no venue history or publication counts. This axis would measure annual paper volume, venue quality, and preprint-to-journal conversion over time.
Impact—
Citation-based impact cannot be assessed: no citation counts, h-index, or citing works are in scope. This axis would measure citation volume, per-paper influence, and uptake of the QML and neural-quantum-state methods across communities.
Focus0/100
The recorded field distribution splits exactly 50/50 between quantum computing / quantum machine learning and computational quantum many-body physics with neural quantum states — two adjacent halves of one program rather than scattered topics. The radar's focus score currently reads 0, which reflects the very thin catalog (two papers) rather than topic dispersion; the thesis itself spans neural quantum embedding, margin bounds, and Mamba-based decoders within those same two fields.
Collaboration0/100
The snapshot records 0 distinct co-authors, so the collaboration score is 0 and no frequent co-author list exists. Both catalog entries are first-authored and corresponding-authored arXiv items, meaning every reviewed output is led by Tak Hur; whether collaborators exist outside this thin snapshot is unknown. Treat team-level signals as unobserved rather than absent until a wider, name-resolved record is available.
Activity—
Recent-activity data is insufficient: both catalog entries carry 2026 dates and nothing else, so cadence or continuity over prior years cannot be computed. This axis would measure yearly output rhythm, recency of contributions, and sustained research engagement.
Reproducibility78/100
Reproducibility is the strongest recorded axis: a mean score of 78 across the 2 cataloged papers. The stochastic-reconfiguration paper rates high readiness — ablations back the ridge-regression reframing of stochastic reconfiguration — while the PhD thesis rates medium, hardware-validated on an NMR quantum processor under DQC1 but held back by thin classical baselines and one statistically insignificant online-energy result.
Research summary
Tak Hur is a researcher at the University of Waterloo whose Evolve profile rests on a compact but coherent arXiv footprint at the quantum–machine-learning intersection. The record splits evenly into two adjacent tracks — quantum computing / quantum machine learning (0.5) and computational quantum many-body physics with neural quantum states (0.5) — and every cataloged output is first-authored and corresponding-authored. The 2026 PhD thesis 'When Quantum Meets AI' bundles four contributions: a neural quantum embedding validated on an NMR quantum processor in the DQC1 setting, margin-based generalization bounds, and Mamba- and MS-SR-based components; Evolve judges the work hardware-validated while flagging thin classical baselines. The companion paper reframes stochastic reconfiguration in variational Monte Carlo as statistical filtering — a ridge-regression-regularized diagonal shift — with ablations in support, though online energy gains are not statistically significant; it earns a high reproducibility-readiness rating, and the two-paper reproducibility mean is 78. Citation counts, venue history, and any co-author network are absent from the snapshot (distinct co-authors recorded: 0), leaving output, impact, and activity unscored — an early-stage profile whose two-way quantum–ML agenda is already clearly legible.
Collaboration pattern
Within the current snapshot, Tak Hur records 0 distinct co-authors and no frequent co-author list; both cataloged outputs are first-authored and corresponding-authored arXiv works. The collaboration picture is therefore unobserved rather than demonstrated-solo: thesis-level work typically involves an advisor, but none is captured in this record. Collaboration inferences should wait for a broader, name-resolved record.
Research keywords
Highlights
A thesis-length bridge: quantum methods for ML and ML methods for quantum systems
Neural quantum embedding taken toward real hardware
Stochastic reconfiguration as statistical filtering
Timeline
- 2026PhD thesis 'When Quantum Meets AI' posted to arXiv, bundling neural quantum embedding (NQE), margin-based generalization bounds, a Mamba-based decoder, and MS-SR.
- 2026First-authored paper 'Stochastic Reconfiguration as Statistical Filtering for Overparameterized Neural Quantum States' posted to arXiv.
- 2026Both arXiv entries enter the Evolve catalog with first/corresponding authorship and reproducibility assessments: the thesis rates medium, the SR paper high, for a two-paper mean of 78.
Papers in the Evolve catalog
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 a thin arXiv-only snapshot: two entries, both dated 2026; output, impact, and activity axes remain unscored (insufficient data).
- No citation counts, h-index, or venue/publication history are available in the provided data.
- No ORCID is provided and no identity disambiguation was performed; this profile is name-resolved from public bibliographic data, and 'Tak Hur' may collide with other researchers.
- Co-author information is empty (distinct co-authors: 0; frequent co-authors: none), so collaboration metrics likely undercount real-world collaboration.