# Choosing a workflow

> How to find the right Qsolve workflow for your problem and judge it before you spend credits: search, track record, problem limits and the classical baseline.

Source: https://qsolve.omlabs.co/docs/use/choosing-a-workflow
Updated: 2026-11-12

Match your problem to a workflow's input and size limits, then read its track record to see how it has performed against the classical baseline. The track record describes benchmark cases, not your data, so treat it as a guide to what to expect.

## Find candidates

Open **Run an experiment**. Type a problem into the search box, for example "portfolio" or "binding energy". Search covers each workflow's title, summary, name and tags. The chips below filter by family, such as optimisation or chemistry.

Each card shows a **By Qsolve** badge for workflows Qsolve maintains, a row of squares (one per benchmark case: green matches the best classical answer, amber is below it, red failed), how many cases were confirmed, and the median cost of the benchmark runs as **typical credits**.

## The workflows

| Workflow | Solves | Input | Largest problem |
|---|---|---|---|
| `generic-qubo` | Any QUBO, optionally with "choose exactly k" | A square matrix `Q`, optional labels | 24 variables |
| `max-cut` | Split a weighted graph into two groups to maximise the weight between them | Nodes and weighted edges | 24 nodes |
| `portfolio-optimization` | Pick k assets, trading return against risk | Asset names, expected returns, covariance, number to select | 24 assets |
| `molecular-ground-state` | Ground-state energy of a closed-shell molecule or ion | A built-in name such as H2 or H2O, or an XYZ geometry, plus an optional active space | 6 orbitals (12 qubits) |
| `interaction-energy` | Binding, solvation, adsorption or reaction energy as a signed sum of system energies | A set of species, each with a geometry or name | 6 orbitals per system |
| `electrolyte-solvent-screen` | Rank battery-electrolyte solvents by how strongly they bind Li+ | A list of solvents such as EC, ACN, water, or your own geometries | 8 solvents |
| `quantum-kernel-classifier` | Binary classification with a quantum-kernel support vector machine | A built-in dataset or your own table | 8 features, 60 training points |

The optimisation workflows use QAOA and are judged against an exhaustive search (simulated annealing above 22 variables). The chemistry workflows use VQE and are judged against the exact solution inside the chosen active space, with Hartree-Fock, MP2 and CCSD(T) shown alongside. The classifier is judged against a tuned RBF support vector machine, with logistic regression, a random forest and gradient boosting for context. The [Glossary](/docs/start/glossary) defines these terms.

## Read the workflow page

Five figures at the top summarise the track record:

| Figure | What it is |
|---|---|
| **Accuracy · simulator** | Median accuracy against the baseline over the latest simulator benchmark cases |
| **Fidelity** | Median fidelity of the quantum output on noisy runs, labelled "hardware" if the workflow has real-device results and "noise model" otherwise |
| **Shots kept** | Median share of shots that passed the error checks on those noisy runs |
| **Typical cost** | Median credits the benchmark runs cost |
| **Version** | The published version you will run |

"n/a" or "none yet" means there are no results of that kind yet.

The **Track record** section lists each benchmark case with its accuracy, error bar, cost and age. Open a row to see every method run on that input side by side, with a note on how each value was checked, the quantum answer, the error-mitigation figures and the circuit.

Up to three tables appear:

- **Track record** (simulator). These count towards the headline accuracy. Only the newest result per case is current; older ones sit in a collapsed "N earlier results" list.
- **Noise-model estimate.** Small cases simulated with an IBM processor's published noise. Real devices can be noisier.
- **On quantum hardware.** The same workflow on real processors. These measure the device as much as the workflow, so they do not count towards the headline accuracy.

**Limits**, at the bottom, gives the largest problem the workflow accepts, which machines it runs on, its tags, and a link to its source repository (or "Maintained by Qsolve").

## Judge a workflow before you commit

1. **Check that your problem fits.** Size limits and input format are fixed. Inputs over the limit are rejected before any credits are spent.
2. **Look at cases like yours.** A workflow that matches the baseline on 10-variable problems says little about 24-variable ones. Pick the benchmark cases closest to your size.
3. **Look for amber and red squares.** Amber means the quantum method found a worse answer than the baseline. Red means the run failed. Both are information.
4. **Read the baseline.** The methods table names the classical method the quantum result was compared with. An exhaustive search or exact solution makes a strong comparison. A heuristic (annealing above 22 variables) makes a "match" mean less.
5. **Run your own input on the CPU simulator.** It costs a few hundredths of a credit and shows how the workflow handles your data. Then decide whether a quantum computer run is worth its price.