An Azure service that provides quantum computing and optimization solutions.
Hello Akita,
Azure Quantum Resource Estimator (QRE) is generally designed to provide deterministic estimates for the same circuit and configuration inputs. However, in some cases, users may observe small variations across repeated runs of the same QASM input.
These differences are typically related to internal compilation and optimization processes involved in fault-tolerant resource estimation, such as circuit rewriting, scheduling, layout optimization, and resource tradeoff analysis. Minor numerical precision effects or backend service updates may also contribute to slight differences in reported metrics.
At present, the standard estimation workflow is not expected to intentionally rely on randomized synthesis or stochastic routing algorithms that would produce significantly different outputs for identical inputs. Therefore, any observed variation is usually small and within expected estimation tolerance.
To improve reproducibility, we recommend:
- Using the same estimation parameters, error budget, and target profile across runs
- Comparing multiple consecutive executions to evaluate whether the variation remains within a small tolerance range
- Reviewing whether advanced optimization or Pareto estimation settings are enabled
The following documentation may also be helpful:
Quickstart: Run your first resource estimate
How to optimize run times for large programs with the resource estimator
Retrieve the output of the Microsoft Quantum resource estimator