Quantum Significant Requirements (QSRs)

• Quantum Significant Requirements (QSRs) are a subset of requirements that capture unique quantum hardware behaviors, probabilistic outcomes, and hybrid integration challenges.
• They extend traditional software specifications by incorporating quantum-specific constraints such as qubit quality, gate errors, and noise model validations.
• QSRs guide the development lifecycle by providing measurable, traceable criteria to ensure reliability, performance, and maintainability in quantum-classical systems.

Quantum Significant Requirements (QSRs) are the subset of functional and nonfunctional requirements that are uniquely significant to quantum software because of quantum hardware behavior, algorithmic properties, probabilistic execution, and hybrid integration; in QCaaS literature they are also introduced as the quantum-specific counterpart of architecturally significant requirements. Adjacent literature often addresses the same concern without using the term explicitly, referring instead to quantum-specific requirements, extra-functional quantum requirements, quantum software requirements engineering, or quantum requirement specification for hybrid classical–quantum systems (Akbar et al., 2022, Ahmad et al., 6 Oct 2025, Yue et al., 2023, Dey et al., 2020).

1. Terminology and conceptual scope

Within quantum software engineering, QSRs denote requirements that conventional requirements specifications do not capture adequately because they must encode qubits, gate sets, probabilistic outputs, backend variability, hybrid orchestration, and quantum-specific validation criteria. One explicit formulation defines QSRs as the requirements that “quantify constraints, targets, and validation criteria that cannot be adequately captured using conventional requirements alone.” A related QCaaS formulation treats them as the quantum-specific counterpart of architecturally significant requirements, with direct impact on design, development, execution, integration, and evolution. Other work reaches the same conceptual space by retaining functional and extra-functional requirements while adding a quantum/classical/hybrid split, or by splitting “Quantum Requirement Specification” into “Quantum Software Requirement Specification” and “Quantum Hardware Requirement Specification” (Akbar et al., 2022, Ahmad et al., 6 Oct 2025, Yue et al., 2023, Dey et al., 2020).

The requirements-engineering framing is explicit in work on requirements engineering for quantum computing. There, requirements engineering is described as the activity concerned with “the identification, modeling, communication, and documentation of system requirements and the context of their usage,” and quantum computing is presented as requiring “new models and methodologies” because “traditional software development methodologies may not seamlessly transition to the quantum field,” especially in the presence of “superposition and entanglement,” “the probabilistic nature of qubits,” intricate algorithm formulation, and an immature toolchain (Sepúlveda et al., 2023).

2. Placement in quantum development life cycles

In the Quantum Software Engineering life cycle, requirements are the first of five phases: quantum requirements engineering, quantum software design, quantum software implementation, quantum software testing, and quantum software maintenance. This phase shares classical processes such as elicitation, analysis, specification, and management, but it is extended to capture quantum features, quantum mechanisms in use cases, hybrid constraints, backend selection, noise model assumptions, and traceability to architecture, code generation, testing, and maintenance. The same source explicitly calls out “Hybrid Requirements Engineering (RE), Hybrid RE Team,” and identifies multidisciplinary roles including software engineers, quantum hardware engineers, quantum algorithm experts, domain scientists, DevOps for quantum pipelines, operations, and compliance specialists (Akbar et al., 2022).

A closely related waterfall-style Quantum Development Life Cycle places quantum requirement specification after quantum feasibility study and before design, coding/implementation, testing, and quantum software quality management. In that model, requirement analysis transforms needs and high-level requirements into “unambiguous,” “definitive,” “measurable and testable,” “traceable,” “complete,” “consistent and stakeholder-approved requirements,” and separates them into quantum software and quantum hardware requirement specification. Hardware-side requirements include “Qubit Count,” “Quantum Volume,” “Physical Machine Description (PMD),” gate libraries, and classical host processors; software-side requirements include toolchains, integrator plugins, logical synthesis, and validator modules (Dey et al., 2020).

Service-oriented work relocates the same construct into a four-phase life cycle—conception, modeling, assembly, deployment—and a layered reference architecture comprising a service development layer, a service split layer, and a service deployment layer. There, QSRs begin as an explicit artifact in conception, are mapped to UML, workflow, or graph-based models, are realized in implementation choices such as Qiskit, Cirq, Q#, Python, and C#, and become deployment-time policies, SLAs, backend-selection rules, and orchestration constraints in QCaaS platforms (Ahmad et al., 6 Oct 2025).

3. Taxonomies and modeling schemes

The literature organizes QSRs through several overlapping classification schemes. One is explicitly requirement-centric, one is quantum/classical/hybrid, and one is service-architectural. Taken together, these schemes show that QSRs are not a single checklist but a structured family of constraints and quality attributes (Akbar et al., 2022, Yue et al., 2023, Ahmad et al., 6 Oct 2025).

Lens	Categories or notation	Representative content
Requirement partition	Functional, Extra-functional, «qReq», «cReq», «hReq»	Quantum/classical/hybrid splitting; SysML requirements diagrams; use-case modeling
Core QSR taxonomy	Hardware/resource, Algorithmic, Execution, Portability/interoperability, Hybrid orchestration, Verification and validation, Performance/scalability, Reliability/robustness, Security/compliance, Maintainability/monitorability	Qubits, connectivity, topology, T1/T2, gate/readout error, shots, fidelity, latency, auditability, versioning
QCaaS/service lens	Algorithm-level, Hardware-level, Platform/service-level, Performance, Reliability/quality, Security/compliance, Usability/portability, Cost/operational, Evolution/interoperability	API Gateway, Service Composition, Service Façade, backend selection, load balancing, vendor lock-in avoidance

In the most explicit software taxonomy, hardware/resource constraints include qubits, connectivity, topology, coherence times $T_1/T_2$, single- and two-qubit gate error rates $\epsilon_1, \epsilon_2$, readout error, calibration schedules and drift, queue latency, and access policies. Algorithmic constraints include circuit depth $d$, gate counts, $T$-count, ancilla usage, oracle availability, logical-versus-physical mapping, and transpilation cost. Execution constraints cover shots $S$, sampling error, probabilistic outputs, confidence intervals, and mitigation strategies. The same taxonomy extends to portability/interoperability, hybrid orchestration, verification and validation, performance and scalability, reliability and robustness, security and compliance, and maintainability and monitorability (Akbar et al., 2022).

QSRE work preserves the classical distinction between functional and extra-functional requirements but adds a further dimension: requirements for the quantum part, the classical part, and hybrid cross-cutting parts. In the motivating VaR/CVaR example, functional requirements include determining VaR and CVaR and defining confidence levels, while extra-functional requirements include performance expressed as quadratic speed-up over classical Monte Carlo and resource constraints expressed through number of gates, ancilla qubits, limited qubits, and limited circuit depth. The same work emphasizes that portability, reliability, scalability, maintainability, and reusability become quantum-significant either because they are unique to quantum computing or because they become critical under current hardware limitations (Yue et al., 2023).

4. Formalization and measurable acceptance criteria

A distinctive feature of QSRs is their conversion into explicit thresholds, formulas, and acceptance tests. In the most detailed formulation, QSRs are written as measurable requirements against backend metadata, calibration reports, transpilation outputs, statistical confidence bounds, and cross-backend equivalence tests. Representative formulas include the fidelity criterion

$$F(\rho, \sigma) = \left(\mathrm{Tr}\sqrt{\sqrt{\rho}\sigma\sqrt{\rho}}\right)^2,$$

the decoherence-survival condition

$e^{-d\tau/T_2} \ge \alpha,$

the shot-allocation rule

$S \ge c\,\epsilon^{-2}\log(1/\delta),$

and the depth-dependent success estimate

$P_{\text{success}} \approx (1-\bar{\epsilon})^d.$

The same source also lists $p(t)=e^{-t/T_2}$, amplitude-damping survival $\epsilon_1, \epsilon_2$0, the logical error estimate $\epsilon_1, \epsilon_2$1, and the complexity target for Grover, quantum $\epsilon_1, \epsilon_2$2 versus classical $\epsilon_1, \epsilon_2$3 (Akbar et al., 2022).

The acceptance examples are correspondingly concrete. A hardware/resource QSR can require execution on an IBM backend with $\epsilon_1, \epsilon_2$4 available qubits, nearest-neighbor two-qubit connectivity, and daily calibration, with acceptance tied to backend metadata and calibration timestamp. Another can require $\epsilon_1, \epsilon_2$5, $\epsilon_1, \epsilon_2$6, $\epsilon_1, \epsilon_2$7, $\epsilon_1, \epsilon_2$8, and readout error $\epsilon_1, \epsilon_2$9. Portability can require compilation and execution on Qiskit and AWS Braket with simulator state fidelity $d$0. Hybrid orchestration can require end-to-end data exchange latency $d$1 ms. Verification can require circuit-equivalence checking with state fidelity $d$2 across random input seeds and property-based tests passing for 95% of generated cases. Reliability can require success probability to remain within $d$3 across two calibration cycles. Maintainability can require versioning of circuits, transpilation parameters, backend calibration snapshots, and noise model versions so that reruns reproduce results within statistical bounds (Akbar et al., 2022).

5. Domain-specific expansions of the QSR concept

Beyond software specification, the term and its surrounding ideas are used to express system-level resource inequalities, controller timing constraints, network SLAs, and industrial feasibility thresholds. In these settings, QSRs function as cross-layer constraints connecting software requirements, fault tolerance, communication, and runtime feasibility (Kelly et al., 2022, Kurman et al., 2024, Krol et al., 2024, Jacinto et al., 11 Apr 2025, Skrzypczyk et al., 2021).

Domain	Representative QSR	Source
Quantum communication and stabilizer QEC	$d$4 for nonzero channel capacity; $d$5 for target code distance	(Kelly et al., 2022)
Controller–decoder systems for surface-code circuits	Closed-loop latency in the “tens of microseconds” range; $d$6; near-term hardware with $d$7 and about 1000 qubits sufficient for the Shor(21) scale	(Kurman et al., 2024)
Industry-relevant scheduling (QISS)	Quantum speedup requires error rates $d$8 or better and measurement time below $d$9 ns; measurement latency dominates runtime more strongly than error rates	(Krol et al., 2024)
Distributed fault-tolerant computation	70 Bell pairs per cycle time between each processor with fidelity exceeding 98.4 percent for RSA-2048 over 379 processors	(Jacinto et al., 11 Apr 2025)
Multi-user quantum networks	QoS requirements for entanglement fidelity, throughput, latency, jitter, and availability, satisfied through centralized periodic scheduling and RCPSP formulations	(Skrzypczyk et al., 2021)

In the coherence-based communication setting, QSRs are explicitly resource-theoretic. They tie relative entropy of coherence to channel capacity, volume-law entanglement, and stabilizer code distance. For random monitored dynamics, the transition criterion $T$0 separates a quantum phase with nonzero capacity from classical area-law regimes, and the code-distance bound $T$1 turns coherence into a design requirement for stabilizer codes (Kelly et al., 2022).

In infrastructural settings, the same logic becomes operational. The controller–decoder paper derives latency, throughput, bandwidth, and physical error-rate requirements from an end-to-end surface-code implementation of Shor’s algorithm; the distributed-architecture paper derives Bell-pair rate and fidelity requirements from surface-code lattice surgery across modules; and the QISS study derives measurement-time and error-rate thresholds from quantum resource estimation for manufacturing shift scheduling. This suggests that QSRs can denote not only software-level requirement statements but also end-to-end architectural envelopes under which a quantum application remains executable or advantageous (Kurman et al., 2024, Jacinto et al., 11 Apr 2025, Krol et al., 2024).

6. Traceability, verification, and tool support

QSRs are typically managed through explicit traceability structures. One detailed proposal uses a QSR-oriented traceability matrix with columns for QSR ID, category, design artifact, implementation artifact, test artifact, maintenance hooks, and status. The associated verification and validation strategies include unit testing of quantum kernels, integration testing of hybrid pipelines, metamorphic testing, property-based testing, equivalence checking, statistical validation, and hardware-in-the-loop runs. The metrics recorded for these activities include gate counts, depth, qubit utilization, transpilation cost, state or process fidelity, success probability, runtime, queue latency, shot counts, and QBER for QKD scenarios (Akbar et al., 2022).

The modeling side is similarly heterogeneous. QSRE work introduces SysML requirements diagrams and use-case models with stereotypes «qReq», «cReq», and «hReq». QCaaS work extends this with UML component, sequence, and deployment diagrams, workflow/BPMN, process models, graph-based specifications, circuit diagrams, and QADL. Reusable patterns include Classic–Quantum Split, API Gateway, Service Composition, Service Façade, and ESB/layered architectures, all used to make QSR-driven choices traceable from requirement to model, to code, and to deployment descriptors (Yue et al., 2023, Ahmad et al., 6 Oct 2025).

Systematic-review work adds a methodological layer for synthesizing QSR evidence. The protocol for reviewing requirements engineering in quantum computing proposes coding literature against challenge, opportunity, and future-direction schemas; visualizing results with “a weighted cloud tag,” “a Sankey diagram,” and concept-author maps using “VOS-viewer, Sankeymatic, Termine, etc.”; and applying a five-question quality-assessment checklist covering aim, methodology, context, threats to validity, and clarity of findings. Threats to validity are categorized as theoretical, descriptive, interpretative, generalizability, and reliability, and PRISMA is used to validate report structure (Sepúlveda et al., 2023).

7. Limitations, misconceptions, and future directions

QSRs are not limited to claims of quantum advantage or raw performance. Quality-attribute analyses of quantum computing platforms show that, except for performance and scalability, most other quality attributes are adversely affected by current platform characteristics. The adversely affected set includes maintainability, testability, reliability, availability, interoperability or portability, security, manageability, and usability. This is why the QSR literature consistently includes portability, reliability, maintainability, monitorability, security, and operational constraints alongside qubit counts, circuit depth, and speedup targets (Sodhi, 2018, Sodhi et al., 2021).

The term itself is also not yet standardized. Several central papers on quantum software requirements engineering, quantum development life cycles, and requirements-engineering reviews articulate the substance of QSRs without adopting the label. At the same time, the literature repeatedly identifies the same unresolved problems: requirements verification for quantum software is under-explored, formal methods are in an early stage, testing still faces major challenges, tool support and modeling notations need extensions, comprehensive toolchains remain “in its infancy,” and standardized processes, taxonomies, and templates are still needed. Future-direction categories already named in the literature include “QC Requirement Models, Simulation and Visualization Tools,” “Hybrid Q-Classical RE Methodologies,” “Requirement Visualization for QC, Automated Tools for Requirement Analysis,” “Interdisciplinary Collaboration Models,” “Stakeholder Engagement Strategies,” and “RE Training Programs for QC” (Yue et al., 2023, Sepúlveda et al., 2023, Ahmad et al., 6 Oct 2025).

A plausible implication is that QSRs should be understood less as a fixed vocabulary than as an evolving requirements-engineering framework for expressing where quantum mechanics, software architecture, toolchains, controller latency, communication fidelity, and industrial deployment constraints materially alter what it means for a requirement to be specific, measurable, and actionable.