Applications / AI / Quantum for AI
Quantum RL and control policies
Choosing an action from an observed state
A variational policy is a circuit that takes the observed state as input angles and returns action probabilities as measurement outcomes. Training is ordinary REINFORCE: sample an action, collect the reward, and push the parameters toward actions that scored above the batch mean. The circuit replaces the policy network and nothing else about the loop changes.
Because the state enters as input angles rather than as trainable parameters, the data parameters have to be held fixed while the weights move. That split is what separates a policy from a Born machine, which trains every angle it has.
Run on our engines
A two-qubit policy with four trainable weights, trained by REINFORCE over 60 decisions, then probed at two states on hardware. Submitted to each kind of compute we offer, on 18 September 2026. Every figure below is a real job on the service, priced as any customer would be priced.
| Device | Engine | Kind | Qubits | Result | Cost |
|---|---|---|---|---|---|
 | exact.cpu | CPU | 2 | mean return 0.611 over the last three batches, against 0.533 for a random policy | $0.0540 |
 | qpu.rigetti | QPU | 2 | P(action 1) 0.199 and 0.650 at two probe states, against 0.049 and 0.708 noiseless | $1.4500 |
The same problem is yours to run: every instance here is seeded, so it rebuilds exactly. Open the console and a cost estimate is free before anything executes.
540 circuits over 60 decisions. The random baseline returns 0.533 on this task rather than 0.5, which is the sampling spread at that number of decisions.
On hardware both probes moved toward 0.5, by 0.15 and 0.06, which is the readout floor. The same weights on a noiseless statevector separate the two states the same way, so the device reproduced the policy it was given.
Where this stops
- Every decision is one circuit and one job, so an episode length is a job count
- 60 decisions is a small sample: a margin of 0.611 against 0.533 sits inside the spread of that sample size, and separating them needs hundreds of episodes across several seeds
- Gradients here are central differences, which cost two evaluations per parameter
The classical baseline for this case is a random policy, at 0.533 mean return. The structural limit, which does not move when a benchmark is re-run, is One circuit per decision: an episode of 1,000 steps is 1,000 jobs.
Run it yourself
Every figure above is from a job billed on the production service. Load the same circuits into the console, change the instance to your own data, and export a certificate for your own run rather than citing ours.
The argument behind these numbers, at length: Quantum machine learning on simulators and real quantum hardware.
Other AI use cases
QCBM and quantum generative models
Sampling from a distribution learned from scratch
QCBM against its own untrained start
QGAN and synthetic data
Adversarial generation against a learning discriminator
QGAN with a classical discriminator
Quantum AI image generation
Writing an image into a circuit and reading it back
Patch GAN and NTQIP, both on Rigetti