Use cases
Three killer applications, developed against real hardware constraints and validated on real quantum computers.
The project’s software stack is not built in the abstract. Three high-impact applications drive its requirements and prove its worth: quantum cryptanalysis, simulation of strongly correlated systems, and Quantum AI for 6G networks.
All three are executed on IQM’s superconducting quantum hardware through the cloud (T4.5) and assessed against performance, reliability and scalability criteria (T4.6), creating a feedback loop between algorithm design, hardware constraints and application needs.
Strategic KPI 3 — develop and validate at least three killer applications for quantum computing.
UC1 Quantum cryptanalysis of post-quantum cryptography
Cybersecurity
The challenge
Post-quantum cryptographic schemes are being standardised now, but their resilience against a real quantum adversary is still largely estimated rather than measured. Reliable quantum cost estimates are needed to choose parameters that will hold up over the coming decades.
Our approach
Two complementary strands. For lattice-based schemes, Grover-based and quantum walk techniques are applied to the hardness of Learning With Errors (LWE) and the Shortest Vector Problem (SVP). For code-based schemes, classical Information Set Decoding is adapted to quantum amplitude amplification and quantum sieving, targeting the Syndrome Decoding Problem and the Codeword Finding Problem — including candidates such as HQC, being standardised as HQC-KEM.
What it produces
Validated quantum circuits, fault-tolerant benchmarks, security assessment reports and concrete recommendations on parameter selection, feeding into standardisation.
UC2 Quantum simulation of strongly correlated systems
Materials science and chemistry
The challenge
Strongly correlated quantum systems are intractable for classical methods at the scales that matter for energy and chemistry applications, yet current quantum simulations rarely leave the simulator for real hardware.
Our approach
Quantum Phase Estimation, the Variational Quantum Eigensolver and Quantum Imaginary Time Evolution are tailored to model Hubbard and Heisenberg Hamiltonians, with co-design against real hardware constraints such as qubit count and connectivity.
What it produces
High-fidelity simulation results on IQM hardware, algorithmic benchmarks, and feedback that flows back into hardware optimisation.
UC3 Quantum AI for 6G networks
Telecommunications
The challenge
AI-native Radio Access Networks for 6G must classify high-dimensional signal data in real time to detect threats such as false base stations — a workload where quantum machine learning may offer an advantage.
Our approach
Quantum machine learning models are designed and implemented for classification over high-dimensional signal data, tested on real quantum hardware and simulated within AI-RAN frameworks.
What it produces
Validated quantum AI algorithms and demonstrators achieving at least 90% classification accuracy under realistic conditions.