Noise remains one of the primary obstacles to practical quantum computing. Systematic coherent errors, calibration drift, and hardware constraints degrade the fidelity of quantum operations, limiting the performance of both near-term quantum devices and future fault-tolerant systems. Achieving reliable quantum computation therefore requires robust quantum control and error suppression that are co-designed with the capabilities and constraints of quantum hardware.

In this preliminary exam, I will present two complementary frameworks that illustrate a unified approach to robust algorithm-hardware co-design.  First, I will introduce Ansatz-ResPID, a closed-loop quantum control framework that adapts proportional-integral-derivative (PID) control to drifting quantum systems. By combining online parameter tracking with an offline-learned controller, Ansatz-ResPID stabilizes multiple control parameters while significantly reducing calibration overhead. Second, I will present a robust analog Hamiltonian simulation framework that suppresses coherent calibration errors using only native two-local interactions. Building on this framework, I will introduce Error-Protected Simulation (EPS), which extends the framework using hardware-efficient qLDPC codes optimized for superconducting architectures. By combining geometrically local stabilizer Hamiltonians with modular-based logical operators, EPS continuously suppresses dominant local noise throughout quantum evolution while preserving hardware locality and scalability.

Together, these frameworks demonstrate how co-designing quantum control and error-suppression strategies with quantum hardware can improve the robustness, efficiency, and scalability of quantum computing.