Maximum gate density per layer assumption.
Loading page content.
Hardware-constrained learning for quantum computing and artificial intelligence
Simulation studio
Learners adjust circuit depth, gate error rate, qubit count, and backend assumptions while a live fidelity curve collapses from 1.0 toward 0.0.
Controls include circuit depth, epsilon, qubit count, and backend options for IBM Heron, IonQ Forte, and a simulated ideal baseline.
F(G, epsilon) = (1 - epsilon)^G
Corrected as a conservative upper-bound model that uses total gate count G rather than depth times qubits. The 0.85 threshold is labeled as an illustrative teaching threshold, not a universal literature standard.
Recommended first build because it is the fastest high-value public demo and the clearest expression of the course thesis.
Module context
Module 1 simulations make the NISQ-era constraints visible and clarify where the quantum subroutine actually sits inside a hybrid workflow.
Live lab
This studio route isolates a single simulation so the learner can focus on one model, one control surface, and one explanatory framing at a time.
Browser-playable lab
Conservative upper-bound model with total noisy gate count G per layer, not depth times qubits.
Controls
Outputs
Maximum gate density per layer assumption.
Backend-adjusted epsilon.
Illustrative teaching threshold at 0.85 fidelity.
Final modeled fidelity is 0.3248 after 140 total noisy gates on IBM Heron.
Why this lab matters
The NISQ Fidelity Cliff sits inside Module 1to reinforce the module's core teaching objective through direct manipulation rather than summary-only reading.
Module 1 simulations make the NISQ-era constraints visible and clarify where the quantum subroutine actually sits inside a hybrid workflow.
Keep exploring