Quantum computing is no longer just the domain of theoretical physicists, it’s a tangible, rapidly growing field where Canadian innovators are leading the charge. Companies likeXanadu and D-Wave are redefining what’s computationally possible, while universities and startups are pushing the boundaries of quantum hardware, algorithms, and software infrastructure.
But here’s the question many R&D teams in this space are asking:
The answer is often yes, but only when the work is structured and documented properly. In this post, we break down where quantum R&D intersects with SR&ED eligibility and how to avoid missing valuable credits in this high-potential space.
What Is Quantum Computing and Why It Matters Now
Unlike classical computers, which operate on bits (0s and 1s), quantum computers use qubits that can exist in multiple states at once due to superposition. Qubits can also be entangled, allowing for interactions that are exponentially more powerful than those in traditional systems.
Canada’s quantum scene is robust and growing:
Xanadu is building photonic quantum hardware and the open-source PennyLane quantum software platform.
D-Wave focuses on quantum annealing and commercial applications in logistics and finance.
Major research hubs like the University of Waterloo’s Institute for Quantum Computing drive academic breakthroughs.
As research moves from theory to commercial application, the line between academic discovery and business-driven innovation blurs, making this space increasingly relevant for SR&ED.
Where the SR&ED Opportunities Lie in Quantum Work
Despite its complexity, the CRA doesn’t shy away from quantum computing. In fact, quantum projects often contain the very elements that define SR&ED eligibility: technological uncertainty, experimental development, and systematic investigation.
Here are a few common R&D categories that may qualify:
Quantum Algorithm Development
Creating or improving quantum algorithms (e.g., for optimization or cryptography)
Adapting classical models to run on quantum architectures
Benchmarking performance under uncertain qubit conditions
Hardware Experimentation
Advancing qubit stability and coherence times
Engineering new qubit types (e.g., topological or photonic)
Developing error correction techniques under physical and technical constraints
Software Infrastructure
Building quantum compilers or simulators
Creating hybrid quantum-classical systems
Integrating quantum APIs into legacy infrastructure under non-trivial conditions
These areas often push into unknown technical territory, where established methods don’t exist or fail to perform, key markers of eligible SR&ED activity.
Experimental Development in Quantum: Practical Examples
Let’s ground this with some examples of potentially SR&ED-eligible work in the quantum domain:
Tackling decoherence: Trying to reduce qubit errors through novel shielding or cooling methods—especially when results are unpredictable.
Designing new quantum gates: Building custom operations for specific quantum chips and testing for logical fidelity.
Simulating real-world systems: Developing quantum models for materials science or financial systems, where classical simulations fail to scale.
The common thread? These activities involve iteration, failure, refinement, and ultimately, a deeper understanding of underlying technologies, not just applying known methods.
Routine Engineering vs. SR&ED in Quantum Computing
Quantum teams often struggle to distinguish routine engineering from eligible R&D. Here’s a simplified comparison:
Activity
SR&ED-Eligible?
Why / Why Not
Implementing a known quantum algorithm in a simulator
❌ No
Routine application of existing knowledge
Developing a new algorithm to solve a novel problem where scalability is uncertain
✅ Yes
Involves technological uncertainty
Assembling a prebuilt quantum SDK or API into an app
❌ No
Integration work, not experimental development
Extending a quantum SDK with new functionality due to performance limitations
✅ Yes
Requires investigation and overcomes technical limitations
Configuring standard cryogenic cooling for qubits
❌ No
Routine engineering
Experimenting with alternative cooling methods to extend coherence time
✅ Yes
Investigative, iterative, and uncertain
In SR&ED, the key is to focus on “how” something was developed, not just “what” was delivered.
What CRA Looks For in Quantum R&D
Despite being a complex domain, the CRA applies the same core SR&ED criteria to quantum projects as it does to more conventional technologies. Here’s what they expect:
Technological Uncertainty
Is there uncertainty that can’t be resolved using standard procedures or publicly available knowledge?
Are there fundamental limits in current methods (e.g., qubit fidelity, simulation limits)?
Systematic Investigation
Was a structured approach taken to address the uncertainty?
Were hypotheses tested, data collected, and iterations performed?
Documentation
Is there evidence of experimental design, code versions, test logs, or technical roadblocks?
Can you show failed iterations that helped advance understanding?
Conclusion: The Future Is Quantum and SR&ED Can Help Fund It
Quantum computing is a moonshot but moonshots need fuel. If your team is tackling tough problems in quantum development, SR&ED can be a vital funding tool to offset risk and accelerate progress.
Too often, brilliant technical teams miss out on SR&ED simply because they don’t frame or document their work properly. Don’t let that be you.
