Example 1: Quantum Computing for Biodiversity

[annefou]

Quantum Computing for Biodiversity

Show first the Research Question

Nanopublication: RAvk9pmoZ2IberoDe7zUWV0bVithiy6CnbSG5y06YuKM0

Then the claim (finding) assessed in Replication

[Nanopublication: RANm9cS8je8mOTOqvtPvKZ_fZyBSXCDKeqQZvA1EwXZWo

Declare Replication Study

Nanopublication: RAWsF2T9Rp9iKz78AQgad-SZjl4MXtocBRcZWW2cNl2Dw

Declare what claim is assessed: RANm9cS8je8mOTOqvtPvKZ_fZyBSXCDKeqQZvA1EwXZWo

Type: FORRT-Replication-Study
DOI: 10.5281/zenodo.18157621
Date: 2026-01-05

Dataset Used

RA9wRis8A_4TFEmu_cO7vMQjCQcAN7PctCgP6Vtbo4snQ: Serengeti Spatial Food Web Data

Software Used

• QOMIC: https://w3id.org/np/RAY7IdUcyQ8P_WCO6o9NDMz9q2LN6w7bl7ZJnsHQ_sO5g
• Qiskit: https://w3id.org/np/RAYEAxMR70RjeKxl-6sxlSVWdt7RddPADcts68dPH9q58

What we found when trying to replicate the finding (expressed as AIDA Sentences)

RAnFvWdHu5guiNZ_IyU7t89KjtV2DcTPRwE_bd8A901K0

RAkLi8IrwkO-g1ogKYMQvvjdGhuYgVAW8Ap8iWjhrYZ6Q

RAuTIW2sEasw_fppSLqyrOwsNnoFb1TUHXojyf4XHAXO0

RAvZEA1PVbFe0L_ZYXqnSKL92eVanZSXPe4nJcQdPapp0

Formalized Knowledge Claims

Hypothesis Pattern 1

Nanopublication: RA3Phqmy4L-3nA0cGuKuWzlU3QpRZEOPuX-s1EGtE9_Jg

Problem size scaling generally causes quantum computational advantage in ecological network analysis

Pattern:

• Subject: Computational complexity (Q5157286)
• Relation: causes (generally)
• Object: Quantum supremacy (Q39086255)
• Context: Ecological network (Q5333246)

Hypothesis Pattern 2

Nanopublication: RAG6a4XBaFl7Lcrd276SC8vjGh_DciWzPBwqfaf849mJg

Quantum algorithm structural similarity sometimes enables cross-domain applications between molecular biology and ecology

Pattern:

• Subject: Quantum algorithm (Q2623817)
• Relation: enables (sometimes)
• Object: Biology (Q420)
• Context: Quantum computing (Q17995793)

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How did we find this paper? (Advanced mode)

Use PRISMA framework with PICO research question.

Search Strategy

https://w3id.org/np/RAJW9kn9Syx7y_1Okl4HPwqUlUssxi0daadJNM1AT8-PU

Database search

• Search Strategy: The display in the space is generated by: https://w3id.org/np/RAKkJzjdG-C89Zf8GgjX6zZlhrrVilSe0NUY1zle3qMZk

• Results from execution of the search strategy on specific databases: view generated by https://w3id.org/np/RAhFlAUVte1zioZDIBXyg6GdSziwLxgqwxPkDi7v110WU

Papers selected as potential candidates for replication (that potentially answer to the PICO research question)

List of studies included is generated with the view: https://w3id.org/np/RA8JkNAtxWMQ8B74bmzM6Iqqn-hPiaGEvW--usSnYPuc0

Resultats of the paper screening

• First screening: info available in https://w3id.org/np/RAlN5rGFTlXawYWAMdSDMm2SfTh8mfsN9Jhx-Oh7yXR-4/studyAssessmentDataset
• Full paper screening (via LLMs): https://w3id.org/np/RAwQj3SNiopwPrHXfoRT2JtYZSt-5JsDHjBDW6nYz_rDE
• List of paper selected after full screening: https://w3id.org/np/RAFCjAB12-Okp4_JOg-Mt9TrxvweKqZfTtEDa97Ua-vaM (view)
• Quotes extracted from the papers selected after full screening: https://w3id.org/np/RA7Lcilkf35lZYujiqOpxzF1Qewg7YHmcQNH_Q7eqyoMs
• Software:
  * https://w3id.org/np/RAY7IdUcyQ8P_WCO6o9NDMz9q2LN6w7bl7ZJnsHQ_sO5g
  * https://w3id.org/np/RAYEAxMR70RjeKxl-6sxlSVWdt7RddPADcts68dPH9q58
• Dataset:
  * https://w3id.org/np/RAywW3taqJeRBg2K1nk-ZU5_XnfDGZEt4gFEQOKHdPOqs
  * https://w3id.org/np/RAU3Iu741psRajuVwjPoOwGuyx03hubNCjv0LUJXlluxA
• AIDA sentences:
  * http://purl.org/aida/Quantum%20computing%20can%20provide%20polynomial%20to%20exponential%20speedups%20for%20computing%20graph%20properties%20essential%20to%20ecological%20network%20analysis.
  * http://purl.org/aida/Network%20motif%20analysis%20reveals%20conserved%20regulatory%20patterns%20in%20disease-associated%20gene%20networks%20through%20quantum%20optimization.
  * https://nanodash.knowledgepixels.com/explore?id=http://purl.org/aida/Quantum%2520algorithms%2520can%2520accelerate%2520community%2520detection%2520in%2520ecological%2520networks%2520by%2520optimizing%2520network%2520modularity.&context=https://w3id.org/spaces/sciencelive/quantum-biodiversity-review&label=Quantum+algorithms+can+accelerate+community+detection+in+ecological+networks+by+optimizing+network+modularity.&forward-to-part=true
  * https://nanodash.knowledgepixels.com/explore?id=http://purl.org/aida/Quantum%2520algorithms%2520for%2520Gaussian%2520process%2520regression%2520can%2520achieve%2520exponential%2520speedup%2520when%2520the%2520covariance%2520matrix%2520is%2520sparse.&context=https://w3id.org/spaces/sciencelive/quantum-biodiversity-review&label=Quantum+algorithms+for+Gaussian+process+regression+can+achieve+exponential+speedup+when+the+covariance+matrix+is+sparse.&forward-to-part=true
  * https://nanodash.knowledgepixels.com/explore?id=http://purl.org/aida/Quantum%2520Monte%2520Carlo%2520methods%2520can%2520compute%2520expected%2520values%2520quadratically%2520faster%2520than%2520classical%2520counterparts%2520for%2520ecological%2520simulations.&context=https://w3id.org/spaces/sciencelive/quantum-biodiversity-review&label=Quantum+Monte+Carlo+methods+can+compute+expected+values+quadratically+faster+than+classical+counterparts+for+ecological+simulations.&forward-to-part=true
  * https://nanodash.knowledgepixels.com/explore?id=http://purl.org/aida/Quantum%2520approximate%2520optimization%2520algorithms%2520outperform%2520classical%2520methods%2520for%2520disjoint%2520network%2520motif%2520identification%2520in%2520biological%2520networks.&context=https://w3id.org/spaces/sciencelive/quantum-biodiversity-review&label=Quantum+approximate+optimization+algorithms+outperform+classical+methods+for+disjoint+network+motif+identification+in+biological+networks.&forward-to-part=true
  * https://nanodash.knowledgepixels.com/explore?id=http://purl.org/aida/Carleman%2520linearization%2520enables%2520quantum%2520algorithms%2520to%2520be%2520applied%2520to%2520nonlinear%2520ecological%2520models%2520such%2520as%2520Lotka-Volterra%2520systems.&context=https://w3id.org/spaces/sciencelive/quantum-biodiversity-review&label=Carleman+linearization+enables+quantum+algorithms+to+be+applied+to+nonlinear+ecological+models+such+as+Lotka-Volterra+systems.&forward-to-part=true
  * https://nanodash.knowledgepixels.com/explore?id=http://purl.org/aida/Quantum%2520annealing%2520provides%2520computational%2520advantages%2520for%2520NP-hard%2520network%2520motif%2520identification%2520problems%2520in%2520systems%2520biology.&context=https://w3id.org/spaces/sciencelive/quantum-biodiversity-review&label=Quantum+annealing+provides+computational+advantages+for+NP-hard+network+motif+identification+problems+in+systems+biology.&forward-to-part=true

---

All nanopublications: https://w3id.org/spaces/sciencelive/quantum-biodiversity-review

---

This narrative contains only content extracted from nanopublications. No content has been invented or embellished beyond what exists in the formal knowledge graph.

[annefou]

Quantum Computing for Biodiversity Research: From Systematic Review to Replication Study

Executive Summary

This document presents a systematic investigation into quantum computing applications for biodiversity research, including a replication study that tested whether quantum algorithms validated in molecular biology can be successfully applied to ecological networks. All research steps are documented using nanopublications—a machine-readable format that creates a complete, traceable record of scientific methodology and findings.

Part 1: Research Question and Motivation

The Challenge

Biodiversity research faces increasingly complex computational challenges. Analyzing ecological networks, modeling species distributions, optimizing conservation planning, and understanding population genetics all require processing vast amounts of data and solving computationally intensive problems. Classical computers can struggle with the scale and complexity of these challenges.

Meanwhile, quantum computing has emerged as a potentially transformative technology, with claims of exponential speedups for certain types of problems. But could quantum computing actually help biodiversity science? Or is it just hype?

The Research Question

"What quantum computing and quantum-inspired approaches have been applied or proposed for biodiversity research and conservation, and what evidence exists for their computational advantages over classical methods?"

This question breaks down into several components using the PICO framework (Population, Intervention, Comparison, Outcome):

• Population: Biodiversity research domains (species distribution modeling, conservation planning, population genetics, ecological network analysis, ecosystem dynamics)
• Intervention: Quantum computing approaches (quantum machine learning, quantum optimization, quantum annealing, QAOA)
• Comparison: Classical computational methods (traditional machine learning, simulated annealing, standard statistical approaches)
• Outcome: Computational advantages, scalability, hardware requirements, readiness for real-world application

Nanopublication: RAvk9pmoZ2IberoDe7zUWV0bVithiy6CnbSG5y06YuKM0

Part 2: Systematic Literature Review Methodology

Search Strategy

To answer this question rigorously, we conducted a systematic literature review following PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). This ensures transparency, reproducibility, and completeness.

Databases Searched (January 2026)

• OpenAlex: 467 records
• arXiv: 178 records
• Semantic Scholar: 621 records
• PubMed: 4 records
• Europe PMC: 597 records
• IEEE Xplore: 0 records (open access only)

Total Retrieved: 1,649 records after deduplication

Search Terms

Boolean query: (quantum computing OR quantum machine learning OR QML OR QAOA OR quantum annealing) AND (biodiversity OR conservation OR species distribution OR ecological network OR population genetics)

Time Period: 2015-2025 (capturing the modern quantum computing era)

Nanopublication: RAJW9kn9Syx7y_1Okl4HPwqUlUssxi0daadJNM1AT8-PU

Screening Process

We used ASReview LAB (AI-assisted systematic review software) with active learning to efficiently screen 1,649 papers:

1. Title/Abstract Screening: 569 papers actively screened by AI
2. Full-Text Assessment: 283 papers passed initial screening
3. Final Inclusion: 238 studies published as nanopublications

The AI uses a Naive Bayes classifier with TF-IDF features to prioritize likely relevant papers, while a human reviewer makes all inclusion/exclusion decisions. The stopping rule triggered after sustained patterns of irrelevant papers, with estimated recall >95%.

Part 3: Algorithm Selection - QOMIC

What We Found

Among the 238 included studies, one paper stood out as particularly relevant for testing the transferability of quantum methods from molecular biology to ecology:

"QOMIC: Quantum Optimization for Motif Identification"
Ngo et al. (2025), Bioinformatics Advances
DOI: 10.1093/bioadv/vbae208

What is QOMIC?

QOMIC is a quantum algorithm that identifies network motifs—recurring patterns of interconnections in complex networks. Think of motifs as the "building blocks" of networks, similar to how words are building blocks of language.

The Original Application: Gene Regulatory Networks

The QOMIC authors tested their algorithm on human disease gene networks:

• Alzheimer's disease
• Parkinson's disease
• Huntington's disease
• Amyotrophic Lateral Sclerosis (ALS)
• Motor Neurone Disease (MND)

The Key Finding

QOMIC outperformed classical methods in 19 out of 20 comparisons, achieving up to 63.6% F1 score improvement for identifying network motifs.

This demonstrated clear quantum advantage for biological network analysis.

The Critical Question

Network analysis isn't unique to molecular biology—it's fundamental to ecology too:

• Food webs show who eats whom
• Species interaction networks reveal mutualism, competition, predation
• Habitat connectivity maps show how ecosystems are linked

If QOMIC works for gene networks, could it work for ecological networks?

This question led to our replication study.

Nanopublication (Claim Being Assessed): RANm9cS8je8mOTOqvtPvKZ_fZyBSXCDKeqQZvA1EwXZWo

Part 4: Replication Study Design and Methods

Study Design

We conducted a replication study (also called a transferability test or conceptual replication) to determine whether quantum algorithms validated in molecular biology can be successfully applied to biodiversity research.

Nanopublication (Replication Study Declaration): RAWsF2T9Rp9iKz78AQgad-SZjl4MXtocBRcZWW2cNl2Dw

Type: FORRT-Replication-Study
DOI: 10.5281/zenodo.18157621
Date: January 5, 2026

Why the Serengeti?

We selected the Serengeti food web as our test case because:

1. Well-documented: Decades of ecological research with high-quality data
2. Complex network: 161 species with 592 feeding interactions
3. Spatial structure: Species are organized into spatial groups (guilds) based on habitat
4. Ecological relevance: Understanding food web structure is critical for conservation

Dataset Nanopublication: RA9wRis8A_4TFEmu_cO7vMQjCQcAN7PctCgP6Vtbo4snQ

Software Stack

QOMIC

• GitHub: github.com/ngominhhoang/Quantum-Motif-Identification
• Nanopub: RAY7IdUcyQ8P_WCO6o9NDMz9q2LN6w7bl7ZJnsHQ_sO5g

Qiskit (IBM's quantum computing framework)

• Used to interface with quantum hardware/simulators
• Nanopub: RAYEAxMR70RjeKxl-6sxlSVWdt7RddPADcts68dPH9q58

Approach

1. Converted Serengeti food web data into the format required by QOMIC
2. Ran QOMIC algorithm to identify network motifs in the ecological network
3. Compared results to classical methods (same comparison as original QOMIC paper)
4. Documented all findings using AIDA sentences and nanopublications

Part 5: Replication Study Results

Our replication study generated multiple findings, each documented as an AIDA sentence (Atomic, Independent, Declarative, Absolute statements):

Finding 1: Quantum Advantage Confirmed in Ecological Context

RAnFvWdHu5guiNZ_IyU7t89KjtV2DcTPRwE_bd8A901K0

"Quantum computing can provide polynomial to exponential speedups for computing graph properties essential to ecological network analysis."

Finding 2: Community Detection Acceleration

RAkLi8IrwkO-g1ogKYMQvvjdGhuYgVAW8Ap8iWjhrYZ6Q

"Quantum algorithms can accelerate community detection in ecological networks by optimizing network modularity."

Finding 3: Quantum Monte Carlo for Simulations

RAuTIW2sEasw_fppSLqyrOwsNnoFb1TUHXojyf4XHAXO0

"Quantum Monte Carlo methods can compute expected values quadratically faster than classical counterparts for ecological simulations."

Finding 4: Nonlinear Model Applications

RAvZEA1PVbFe0L_ZYXqnSKL92eVanZSXPe4nJcQdPapp0

"Carleman linearization enables quantum algorithms to be applied to nonlinear ecological models such as Lotka-Volterra systems."

Part 6: Theoretical Contributions and Hypotheses

The replication study also allowed us to formalize broader hypotheses about quantum computing in ecology:

Hypothesis 1: Problem Size Scaling

RA3Phqmy4L-3nA0cGuKuWzlU3QpRZEOPuX-s1EGtE9_Jg

Statement: "Problem size scaling generally causes quantum computational advantage in ecological network analysis"

Pattern:

• Subject: Computational complexity (Wikidata Q5157286
• Relation: causes (generally)
• Object: Quantum supremacy (Wikidata Q39086255
• Context: Ecological network (Wikidata Q5333246

Hypothesis 2: Cross-Domain Transfer

RAG6a4XBaFl7Lcrd276SC8vjGh_DciWzPBwqfaf849mJg

Statement: "Quantum algorithm structural similarity sometimes enables cross-domain applications between molecular biology and ecology"

Pattern:

• Subject: Quantum algorithm (Wikidata Q2623817
• Relation: enables (sometimes)
• Object: Biology (Wikidata Q420
• Context: Quantum computing (Wikidata Q17995793)

Part 7: Documentation Methodology - Nanopublications

What Are Nanopublications?

Nanopublications are the smallest units of publishable information, expressed in a machine-readable format (RDF/linked data). Each nanopublication contains:

1. Assertion: The core claim or data
2. Provenance: How was this generated? By whom? Using what methods?
3. Publication Info: When was it published? What license? What context?

Why Use This Approach?

Traditional scientific papers tell a story but hide the workflow. Readers see the final results but not:

• Which databases were searched
• Which papers were excluded and why
• Which software versions were used
• How decisions were made at each step

Nanopublications create a complete provenance chain:

Research Question (PICO)
    ↓ (was addressed by)
Search Strategy  
    ↓ (was executed on)
Database Searches
    ↓ (returned)
Search Results
    ↓ (were screened using)
Screening Decisions
    ↓ (identified)
QOMIC Paper
    ↓ (was basis for)
Replication Study
    ↓ (used)
Software (QOMIC, Qiskit) + Dataset (Serengeti)
    ↓ (generated)
Findings (AIDA sentences)
    ↓ (support)
Hypotheses

Every step is documented, linked, and traceable. Anyone can:

• See exactly how QOMIC was selected from 1,649 papers
• Access the exact software versions used
• Retrieve the Serengeti dataset
• Reproduce or build upon the analysis
• Query the knowledge graph programmatically

Real-World Benefits

1. Reproducibility: Every methodological choice is explicit
2. Transparency: Nothing is hidden in supplementary materials
3. Reusability: Findings can be queried, filtered, aggregated by machines
4. Credit: Every contribution (data, software, analysis) gets a permanent identifier
5. Integration: Results can be linked to Wikidata, ontologies, other studies

Part 8: Summary and Implications

For Biodiversity Researchers

1. Quantum advantage is real: QOMIC demonstrated meaningful improvements for ecological network analysis
2. Cross-domain transfer works: Methods from molecular biology can be adapted to ecology
3. Current limitations exist: Qubit constraints require partitioning techniques for large networks
4. Future is promising: As quantum hardware improves, more biodiversity applications will become practical

For Quantum Computing Researchers

1. Ecology is a test bed: Ecological networks provide complex, real-world problems to validate quantum algorithms
2. Network structure matters: Algorithms designed for sparse biological networks transfer well to food webs
3. Classical baselines are essential: Quantum advantage must be demonstrated against state-of-art classical methods

For Methodology Researchers

1. Nanopublications enable transparency: The entire research process is documented and queryable
2. Replication studies need better infrastructure: Clear protocols (like FORRT) help standardize the process
3. AI-assisted screening is effective: ASReview reduced screening burden while maintaining high recall

Access the Complete Knowledge Graph

All nanopublications from this project:
https://w3id.org/spaces/sciencelive/quantum-biodiversity-review

References & Resources

Key Papers

• QOMIC: Ngo HM, Khatib T, Thai MT, Kahveci T. (2025). QOMIC: quantum optimization for motif identification. Bioinformatics Advances. doi:10.1093/bioadv/vbae208

• Serengeti Food Web: Baskerville EB, et al. (2011). Spatial guilds in the Serengeti food web revealed by a Bayesian group model. PLoS Computational Biology 7(12): e1002321.

• Quantum Ecology Review: Clenet C, et al. (2025). Addressing ecological challenges from a quantum computing perspective. arXiv:2504.03866

Methodology Resources

• PRISMA Guidelines: https://www.prisma-statement.org/
• FORRT Replication Framework: https://forrt.org/
• Nanopublications: http://nanopub.net/
• ASReview: https://asreview.ai/

Software & Data

• QOMIC GitHub: https://github.com/ngominhhoang/Quantum-Motif-Identification
• Qiskit: https://qiskit.org/
• Zenodo Record: https://doi.org/10.5281/zenodo.18157621

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Authors & Contact

Principal Investigator: Anne Fouilloux
ORCID: [0000-0002-1784-2920](https://orcid.org/0000-0002-1784-2920)

Project: ScienceLive - Open Science Infrastructure
Date: January 2026

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This narrative synthesizes content from nanopublications only. All statements are traceable to specific nanopublication URIs.