UREQA: Leveraging Operation-Aware Error Rates for Effective Quantum Circuit Mapping
summary
UREQA builds operation-aware k-NN models (single-qubit, two-qubit, readout) trained from thousands of microbenchmarks plus calibration features (T1, T2, frequency) to predict per-operation error rates on IBM NISQ machines. Evaluated on four devices with six benchmarks including QPE and Simon, operation-aware models cut one-qubit prediction deviation from 2.9% to ~1% and two-qubit from 1.4% to ~1%, and reduces QPE output error by ~15 percentage points (about 25% → 10%), enabling better circuit-map selection.
AI-written summary · about
chapters (10)
- 0:00UREQA: Leveraging Operation-Aware
- 4:42Multi-qubit Gate Operations
- 7:08Engineering a Quantum Computing Device
- 8:13NISQ Devices are Highly Erroneous!
- 9:08Execution Flow on a Quantum Computer
- 11:29Quantum Circuit Map Selection
- 14:04Quantum Phase Estimation (QPE)
- 20:59UREQA: A Machine Learning-based Approach to
- 23:17UREQA Evaluation Methodology
- 24:08Operation-Aware UREQA++ Achieves the
related talks
28mThe Problem with Qubits
37mResource-Efficient Quantum Computing by Breaking Abstractions
18mSAQR-QC: A Logic for Scalable but Approximate Quantitative Reasoning about Quantum(…)
15mAccelerQ: Accelerating Quantum Eigensolvers With Machine Learning on Quantum Simulators
19mQuarl: A Learning-Based Quantum Circuit Optimizer
23mMake Quantum Computing Useful with Performance Enhancing Infrastructure Software
50mQuantum Benchmarking
49mEvidence for the utility of quantum computing before fault tolerance