> ## Documentation Index
> Fetch the complete documentation index at: https://docs.classiq.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Hardware Benchmarking

Benchmarking quantum hardware is essential on the path toward practical quantum
computing. As hardware advances, benchmarking must go beyond standard device-level
tests such as randomized benchmarking or Quantum Volume to include **algorithmic- and
application-level** evaluation, which measures what ultimately matters: the
correctness and usefulness of the final computational outcome.

## Functional-level benchmarks

Classiq provides a functional-level benchmarking suite for this purpose. Each
benchmark is defined by a quantum model, a **score**, and a problem-size parameter,
so you can study how performance scales with the task. Hardware constraints such as
circuit width are handled at the synthesis stage rather than hard-coded into the
benchmark, keeping the functional definition of a task separate from the capabilities
of the target device.

## Running across backends

Classiq exposes a wide variety of backends — QPUs, hardware emulators, and simulators
across many providers — and lets you switch smoothly between them. The platform also
handles the operational side of a run — managing execution
budget (including running jobs against your Classiq-allocated budget, or emulating a
device's noise model without consuming QPU time or credits) and tracking the time
each job takes to complete, so backends are directly comparable on both accuracy and
turnaround.

## Next steps

Run a set of predefined benchmarks covering canonical algorithmic and
application-level tasks, or define your own. See
[Predefined Benchmarks](./predefined-benchmarks) to get started.