OptCRLQAS
Cross-source consensus on OptCRLQAS from 1 sources and 6 claims.
1 sources · 6 claims
Uses
How it works
Benefits
Highlighted claims
- OptCRLQAS accumulates multiple architecture edits before running full variational optimization and energy evaluation. — Replay-buffer engineering for noise-robust quantum circuit optimization
- OptCRLQAS reduces expensive evaluations from every step to roughly one evaluation per m edits. — Replay-buffer engineering for noise-robust quantum circuit optimization
- For 12-qubit H2O, OptCRLQAS reduced average wall-clock time per episode without degrading final quality. — Replay-buffer engineering for noise-robust quantum circuit optimization
- OptCRLQAS reduced quantum energy evaluation time and classical optimization time in runtime benchmarks. — Replay-buffer engineering for noise-robust quantum circuit optimization
- OptCRLQAS is proposed as a way to make curriculum RL-based QAS more feasible at larger qubit counts. — Replay-buffer engineering for noise-robust quantum circuit optimization
- OptCRLQAS can improve learning signals by evaluating local gate combinations rather than isolated single-gate edits. — Replay-buffer engineering for noise-robust quantum circuit optimization