or4k2l/adc-benchmark
Benchmark suite to compare error‑mitigation strategies (Baseline, DD, ADC, Hybrid) for superconducting qubits — includes temperature sweep, bootstrap CI and automatic γ optimization.
What's novel
Adaptive Dissipation Control — Comprehensive benchmark suite for superconducting qubits. In Cooperation whith AI.
Code Analysis
11 files read · 4 roundsSimulates open quantum system dynamics of a 4-qubit superconducting circuit using Lindblad master equations (via QuTiP) to benchmark four error-mitigation strategies (baseline, XY8 dynamical decoupling, adaptive dissipation control, and hybrid) across temperatures with bootstrap confidence intervals
Strengths
The physics is genuinely non-trivial — proper Lindblad dissipators with thermal occupation, correct XY8 pulse sequences, temperature-dependent noise scaling, and a well-structured factory pattern for strategies. Tests are meaningful, covering operator counts, temperature scaling, CI ordering, and end-to-end execution.
Weaknesses
The entire implementation lives in a single 21KB file with no separation between the hardware model, mitigation strategies, benchmark runner, and plotting/CLI — making it hard to extend or reuse individual components. The 'noise' in the Hamiltonian is deterministic (seeded per time step) rather than truly stochastic, and the grid-search optimizer is computationally expensive without any caching or parallelization.
Score Breakdown
Signal breakdown
Innovation
Craft
Traction
Scope
Evidence
Commits
16
Contributors
2
Files
14
Active weeks
3
Repository
Language
Python
Stars
0
Forks
0
License
NOASSERTION