Yes. I carried out a current feasibility review of the eight sustainability domains proposed in International Collaborative Innovation in Quantum Computing for Sustainability, using recent peer-reviewed/research literature through 2026.
The most important finding is that the eight-domain framework is broadly feasible as a research agenda, but not all eight domains are equally mature for actual quantum-computing deployment today. The strongest evidence is around optimization, while the weakest is around using quantum computing directly for peace/conflict resolution.
1. Executive feasibility assessment
I use a 1–5 feasibility scale:
- 5 = strong practical research feasibility now
- 4 = feasible pilot/research application
- 3 = promising but substantial validation required
- 2 = mainly exploratory/theoretical today
- 1 = currently too immature for credible deployment
| # | Sustainability domain | Feasibility | Current position | Main opportunity |
|---|---|---|---|---|
| 1 | Climate modelling | 3/5 | Research/pilot | QML, parameter optimization, climate-risk modelling |
| 2 | Supply-chain optimization | 4/5 | Pilot/early applied | Routing, scheduling, inventory, logistics |
| 3 | Energy systems | 4/5 | Pilot/early applied | Grid optimization, dispatch, storage |
| 4 | Materials science | 3.5/5 | Research/pilot | Materials discovery, chemistry, carbon capture |
| 5 | Environmental monitoring | 3/5 | Research/pilot | Sensing, prediction, environmental data analysis |
| 6 | Circular economy | 3.5/5 | Emerging applied | Waste, recycling, resource allocation |
| 7 | Social network analysis | 2.5/5 | Early research | Community detection, influence, diffusion |
| 8 | Peace/conflict resolution | 2/5 | Conceptual/early research | Game theory, scenario modelling, negotiation |
Overall framework feasibility: approximately 3.2–3.5/5.
That is a respectable result for a framework that combines quantum computing with eight very different sustainability disciplines.
2. Domain-by-domain feasibility study
1. Climate modelling — 3/5
This is scientifically interesting but technically difficult.
Current research identifies several possible quantum roles:
- solving components of climate-model equations;
- parameter optimization;
- quantum machine learning;
- sub-grid parameterization;
- climate-data analysis;
- extreme-event prediction;
- climate-risk optimization.
A 2025 Cambridge position paper specifically argues that quantum computing could eventually help with differential equations, QML-based sub-grid modelling, model parameter tuning and climate-model analysis. However, it also identifies major obstacles: short coherence times, coupling quantum systems to HPC, huge climate datasets and limited quantum readout.
Critical finding
Your book's idea is feasible as a research programme, but it would be too strong to claim that quantum computers can presently replace conventional climate supercomputers.
The strongest research question would be:
Can hybrid quantum-classical algorithms improve a specific climate-model component sufficiently to justify their computational and energy cost?
That is much more defensible than:
"Can quantum computing solve climate modelling?"
Particularly promising project
For example:
Singapore/Malaysia haze + flooding prediction
Classical HPC/AI → hybrid quantum-classical model → compare prediction accuracy, computation time and energy consumption.
That would turn the book's conceptual proposal into a testable research project.
3. Supply-chain optimization — 4/5
This is probably one of the most immediately testable domains in your book.
Typical problems include:
- vehicle routing;
- warehouse allocation;
- production scheduling;
- inventory optimization;
- facility location;
- fleet management;
- transportation networks.
Recent systematic reviews show substantial quantum/quantum-inspired research in these areas. However, they also identify a recurring problem: many experiments use synthetic datasets, small instances or insufficiently rigorous comparisons with classical algorithms.
A 2026 study also investigated hybrid quantum-classical approaches across routing, scheduling, facility location, inventory and demand forecasting.
Critical finding
The optimization problem is real; the quantum advantage is not yet established universally.
Therefore the correct methodology is:
Classical baseline → quantum/quantum-inspired algorithm → hybrid algorithm → identical dataset → statistical comparison.
Not:
Quantum algorithm → assume improvement.
Feasible pilot
A Malaysian/Singaporean logistics company could provide:
100–1,000 delivery locations + vehicle constraints + fuel/carbon cost.
Then compare:
OR-Tools / MILP / classical heuristic / quantum annealing / QAOA / quantum-inspired optimization.
That would be an excellent empirical study.
4. Energy systems — 4/5
This is another particularly strong domain.
Potential applications include:
- optimal power flow;
- unit commitment;
- economic dispatch;
- grid reconfiguration;
- renewable-energy scheduling;
- battery/storage optimization;
- EV charging;
- demand response.
A 2025 Pacific Northwest National Laboratory review identifies quantum methods for precisely these power-system optimization problems, including gate-based methods, quantum annealing, variational algorithms and quantum-inspired algorithms. It also emphasizes hybrid quantum-classical strategies.
A systematic review of quantum computing for greenhouse-gas reduction similarly found a strong concentration of research around optimization, energy and logistics.
Critical finding
This is one of the strongest parts of your framework because sustainability naturally creates large constrained optimization problems.
For example:
Minimize carbon emissions + electricity cost + grid instability
subject to renewable generation + storage + demand + transmission constraints.
That is exactly the type of problem where quantum optimization is being investigated.
But again:
potential quantum advantage ≠ demonstrated commercial quantum advantage.
5. Materials science — 3.5/5
This is scientifically important because quantum mechanics is already fundamental to understanding molecules and materials.
Possible applications:
- battery materials;
- catalysts;
- carbon-capture materials;
- solar materials;
- hydrogen materials;
- recyclable materials;
- low-carbon industrial materials.
Quantum computers are particularly interesting for quantum chemistry because the underlying physical problem itself is quantum mechanical.
Research in computational materials science already contributes to clean energy, water purification, climate-resilient infrastructure and sustainable material cycles.
There is also increasing research examining not merely the performance of quantum materials but their cost, energy demand, toxicity, supply-chain resilience and environmental footprint.
Critical distinction
There are two different ideas:
A. Quantum computing → discover sustainable materials
and
B. Quantum materials → build quantum computers.
Your book is primarily about A.
Keeping that distinction explicit would make the book academically stronger.
6. Environmental monitoring — 3/5
This domain needs an important refinement.
Quantum computing can potentially help with:
- environmental-data analysis;
- prediction;
- classification;
- anomaly detection;
- satellite/environmental datasets;
- optimization of monitoring networks.
But quantum sensing may actually be more directly relevant to environmental monitoring.
Quantum sensing can exploit quantum phenomena to achieve extremely sensitive measurements, and current research is examining quantum sensing for clean-energy and environmental applications.
For example:
methane detection → atmospheric measurement → source identification → emissions optimization.
Methane monitoring itself is an active research area involving optical, satellite and other sensor technologies.
Critical review of the book
This is an area where I would modify rather than remove your original idea.
Instead of:
"Quantum computing for environmental monitoring"
I would use:
"Quantum technologies for environmental monitoring: quantum sensing + quantum computing + AI."
That is scientifically broader and more defensible.
7. Circular economy — 3.5/5
This is a surprisingly good fit for your framework.
Circular economy systems contain many interconnected optimization problems:
materials → manufacturing → distribution → consumption → collection → sorting → recycling → reuse
Quantum approaches could potentially optimize:
- reverse logistics;
- waste collection;
- recycling allocation;
- material recovery;
- production scheduling;
- resource allocation;
- supply-chain networks.
A 2025 peer-reviewed study specifically investigated quantum computing as an enabler of circular economy and Industry 4.0, reviewing 70 selected documents and identifying applications in supply-chain optimization, energy efficiency and sustainable material innovation.
Critical finding
This domain becomes considerably stronger if you formulate it as:
Circular economy = network optimization + materials optimization + energy optimization.
In other words, quantum computing does not need to "solve the circular economy."
It needs to solve specific computational bottlenecks within circular systems.
That is a much stronger research proposition.
8. Social network analysis — 2.5/5
This is now more credible than it might have appeared a few years ago.
A 2025 Information Fusion review examined quantum social network analysis involving:
- link prediction;
- influence maximization;
- community detection;
- information diffusion;
- hybrid quantum-classical approaches.
It also implemented and examined quantum approaches using Qiskit. But the authors identify scalability, computational complexity, hardware limitations and interdisciplinary requirements as major challenges.
Why your sustainability connection is interesting
You can connect social networks to:
citizens → NGOs → researchers → companies → government → environmental organizations
and examine how sustainability behaviours spread through the network.
For example:
Public transport adoption
Network:
Citizens
↓
friends/family
↓
social communities
↓
environmental organizations
↓
government campaigns
↓
transport policy
The research question could be:
Can quantum or quantum-inspired network optimization identify intervention points that accelerate sustainable behaviour adoption?
That is much more interesting than simply saying "quantum computing can analyse social networks."
Major ethical issue
Influence maximization can become a tool for manipulation.
Therefore a serious academic version should include:
- informed participation;
- privacy;
- transparency;
- fairness;
- avoidance of political manipulation;
- explainability.
This actually strengthens your book because it transforms the proposal from technology enthusiasm into responsible innovation.
9. Peace and conflict resolution — 2/5
This is the most speculative of the eight.
Quantum game theory and related quantum approaches are being studied for strategic interaction, decision-making and conflict-resolution models. A 2025 systematic review describes quantum game theory as an emerging framework for strategic interactions and conflict-resolution research.
However, there is a very large gap between:
mathematical modelling of conflict
and
actually resolving human conflicts.
Peace involves:
- history;
- identity;
- trust;
- emotions;
- institutions;
- power;
- culture;
- misinformation;
- negotiation;
- political legitimacy.
Quantum computation cannot simply calculate "the solution to peace."
Better formulation
Instead of:
Quantum computing for peace resolution
I recommend framing the research as:
Quantum-enhanced decision modelling for complex conflict and resource-allocation scenarios.
For example:
water allocation between competing regions
or
shared-resource negotiation
or
humanitarian logistics during conflict.
That makes the domain much more scientifically testable.
10. What the eight domains reveal collectively
There is actually a very interesting pattern.
The eight domains are not equally independent.
They can be reorganized into three computational layers:
Layer 1 — Optimization
Supply chain
↓
Energy
↓
Circular economy
These are the strongest immediate quantum-computing candidates.
Layer 2 — Scientific simulation and prediction
Climate modelling
↓
Materials science
↓
Environmental monitoring
These require more scientific validation and larger computational infrastructure.
Layer 3 — Human/social systems
Social network analysis
↓
Conflict/peace modelling
These are substantially more complex because the problem is not only computational—it is also behavioural, ethical and institutional.
11. The biggest critical issue with the book
After comparing the framework with current research, I would identify one central weakness:
The book sometimes risks moving too quickly from "quantum computing could potentially help" to "quantum computing can provide a sustainability solution."
Current literature supports the potential and research relevance much more strongly than it supports universal quantum advantage.
This distinction is extremely important.
For example:
Weak claim
Quantum computing will solve climate change.
Stronger academic claim
Quantum computing may provide new computational approaches to selected optimization, simulation and machine-learning problems relevant to climate mitigation and adaptation, subject to demonstrated performance against classical baselines.
The second formulation is much more defensible.
12. The second major criticism: classical computing must remain the control group
This is probably the single most important recommendation I would make to you.
Every proposed sustainability application should have:
Classical baseline
versus
Quantum
versus
Hybrid quantum-classical
and ideally:
Quantum-inspired classical algorithm
For example:
| Method | Result |
|---|---|
| Classical optimization | baseline |
| Quantum optimization | compare |
| Quantum-inspired optimization | compare |
| Hybrid QC/classical | compare |
| Energy consumption | compare |
| Cost | compare |
| Accuracy | compare |
| Scalability | compare |
| Execution time | compare |
Recent logistics research specifically warns that data preparation, queue latency and energy consumption can dominate the quantum portion of the computation, while many published experiments lack standardized benchmarks.
That is a very important critical-review point for your book.
13. Third major criticism: sustainability of quantum computing itself
There is an interesting paradox:
Can we use an energy-intensive technology to make society more sustainable?
Quantum computing has its own environmental footprint.
Recent literature highlights the energy requirements associated with quantum hardware, particularly cryogenic cooling, as well as material requirements and the need for life-cycle assessment.
Therefore the real equation should be:
Sustainability benefit from application
minus
energy + hardware + cooling + materials + data-centre + lifecycle cost
= net sustainability benefit
This would make your book much more rigorous.
14. My overall critical review
What the book gets right
1. The eight-domain framework is genuinely interdisciplinary.
It connects quantum computing with environmental science, engineering, economics, operations research and social science.
2. Optimization is a particularly strong foundation.
Supply chains, energy and circular economy naturally produce difficult combinatorial optimization problems.
3. The framework encourages collaboration.
Climate scientists, engineers, quantum researchers, governments, companies and sustainability researchers need to work together. Current climate-QC literature explicitly calls for this interdisciplinary co-design.
4. The book was directionally ahead of the evidence in several areas.
Some of the topics that were initially conceptual are now becoming recognizable research areas—for example quantum social-network analysis and quantum-enabled circular-economy research.
5. It works well as a research agenda.
That may actually be the most defensible academic description of the book.
15. What needs improvement
1. Do not assume quantum advantage
This should be the strongest qualification.
2. Separate three technologies
The book should distinguish:
Quantum computing
Quantum-inspired computing
Quantum sensing
They are related but scientifically different.
3. Introduce classical benchmarks
Every proposed application should specify its classical control group.
4. Introduce measurable KPIs
For example:
- accuracy;
- speed;
- energy consumption;
- carbon footprint;
- cost;
- scalability;
- robustness;
- solution quality.
5. Reduce claims around peace/conflict
Treat it as an exploratory research domain rather than an established application.
6. Strengthen social-network ethics
Especially where influence maximization and behavioural intervention are involved.
7. Add life-cycle assessment
Quantum technology itself must be evaluated for sustainability.
16. My final assessment of the book's eight-domain framework
If I were writing an academic critical-review conclusion, I would summarize it like this:
The eight-domain framework proposed in International Collaborative Innovation in Quantum Computing for Sustainability is feasible primarily as an interdisciplinary research and innovation agenda rather than as a collection of currently deployable quantum solutions. Its strongest foundations are in optimization-intensive sustainability problems, particularly supply chains, energy systems and circular economy applications. Climate modelling and materials science represent scientifically promising but technically demanding medium-term opportunities. Environmental monitoring may benefit substantially from a broader quantum-technology perspective incorporating quantum sensing. Social-network analysis is an emerging research area requiring stronger empirical validation, while peace and conflict resolution remains the most exploratory domain and should presently be framed as decision and game-theoretic modelling rather than direct conflict resolution. Across all eight domains, the decisive research requirement is rigorous comparison with state-of-the-art classical and quantum-inspired methods, including computational cost, energy consumption, scalability and real-world data.
In simple terms:
Your book's biggest contribution is not that it has proved quantum computing can solve eight sustainability problems.
It is that it proposes:
"Here are eight major sustainability systems where researchers can investigate whether quantum technologies can create measurable computational or decision-making advantages."
That is a much stronger and more academically defensible contribution.
And the 2025–2026 literature is increasingly moving in that direction: from broad claims about "quantum computing for sustainability" toward specific problems, hybrid architectures, benchmark comparisons and measurable sustainability outcomes.
My critical academic assessment: the framework is worth pursuing as a research agenda, but its next stage should move from conceptual breadth → benchmarked experiments → real-world pilot projects.