In traditional software engineering, requirements gathering is a foundational phase that defines what a system should do, who it should serve, and how it should operate. In quantum computing projects, this process becomes more nuanced due to the emerging nature of the technology, its hybrid complexity, and the interdisciplinary collaboration often required. Capturing correct requirements is crucial to ensure that quantum solutions align with both scientific objectives and real-world business needs.
1. Why Is Requirements Gathering Different in Quantum Projects?
Unlike conventional systems, quantum projects operate under unique constraints and opportunities:
- Quantum algorithms may not yet outperform classical ones in practical settings.
- Hardware availability, noise levels, and qubit counts significantly affect feasibility.
- Cross-functional understanding is essential—quantum physicists, computer scientists, domain experts, and business stakeholders must collaborate.
- Hybrid quantum-classical solutions are the norm.
As a result, requirements gathering must be both technically informed and flexible, adapting as quantum capabilities evolve.
2. Key Stakeholders to Involve
Successful requirements gathering begins with identifying the right participants:
a. Business Stakeholders
- Define strategic goals, use cases, KPIs.
- Identify the business value and success metrics.
b. Domain Experts
- Provide industry-specific insights (e.g., chemistry, finance, logistics).
- Help translate quantum outcomes into business advantages.
c. Quantum Developers and Researchers
- Assess technical feasibility, select algorithms, and identify hardware constraints.
d. IT and DevOps Teams
- Understand integration requirements, security, deployment models, and data workflows.
3. Gathering High-Level Requirements
Begin with a problem statement: what are we trying to solve, and why is quantum computing a candidate?
Questions to explore:
- What challenge are we addressing?
- Why is classical computing insufficient?
- Is there a known quantum algorithm that suits this problem?
- Are we targeting a proof of concept, an MVP, or a production system?
Output from this step:
- A clear vision statement
- Definition of project scope
- Initial roadmap with milestones
4. Technical Feasibility Analysis
Before committing to development, conduct a technical discovery phase:
a. Algorithm Suitability
- Identify quantum algorithms that can address the problem (e.g., QAOA for optimization, VQE for chemistry).
- Determine classical baselines for performance comparison.
b. Hardware Fit
- Are simulators sufficient, or is quantum hardware needed?
- What type of qubit technology is required—superconducting, ion trap, photonic?
c. Scalability
- How many qubits are needed now and in the future?
- Can the algorithm scale with hybrid techniques?
d. Execution Model
- Will the workload run in the cloud, on-prem, or in hybrid environments?
Deliverables:
- Technology stack recommendations
- Initial architecture sketches
- Risk assessment (e.g., access latency, noise tolerance, vendor lock-in)
5. Functional Requirements in Quantum Projects
These define what the system should do from the user’s perspective.
Examples:
- Allow researchers to input molecule configurations and receive energy estimations.
- Optimize delivery routes for logistics using quantum solvers.
- Analyze large datasets using quantum-enhanced sampling.
Each requirement should include:
- Description of functionality
- User roles
- Input and output formats
- Execution conditions (e.g., trigger events or scheduled jobs)
6. Non-Functional Requirements
These describe how the system should behave.
Important non-functional categories in quantum projects:
- Performance: Quantum job turnaround times, simulator speed.
- Reliability: Retry logic for hardware failures or timeouts.
- Security: Data encryption, secure API access, authentication to QPUs.
- Interoperability: Integration with classical services, APIs, and hybrid frameworks.
- Maintainability: Modularity in quantum circuits and job management.
7. Tool and Platform Considerations
Gather requirements on:
- Preferred development platforms (e.g., Qiskit, Cirq, PennyLane).
- Targeted quantum hardware vendors (IBM, Rigetti, IonQ, etc.).
- CI/CD pipelines or DevOps needs for version control, test coverage.
- Cloud providers supporting quantum access (AWS Braket, Azure Quantum).
8. Compliance and Ethical Concerns
As quantum projects evolve, organizations should account for:
- Data compliance: Especially in industries like healthcare and finance.
- Ethical considerations: Bias in quantum ML models, use of sensitive data.
- IP management: Protecting proprietary quantum algorithms or methods.
9. Documentation and Communication
Maintain detailed artifacts:
- Requirement specification document (with traceability matrix)
- Meeting notes and stakeholder feedback
- Diagrams and flowcharts showing workflows
- Glossaries for bridging knowledge gaps between domain experts and quantum specialists
Effective communication ensures long-term project alignment and reduces rework.
10. Iterative Updates and Validation
Quantum technologies evolve rapidly, and requirements may need continuous refinement.
Best practices:
- Treat requirements as living documents
- Schedule regular review cycles as hardware or algorithms change
- Validate assumptions through prototyping or benchmarking
- Use feedback loops from early-stage results to refine goals