Applications / AI
AI quantum computing use cases
Quantum AI and QML, measured on QPUs and classical baselines
Quantum machine learning (QML) puts a parameterised circuit where a model layer would sit, and the rotation angles are the weights: a quantum circuit Born machine (QCBM), a QGAN, quantum image generation, a variational policy for quantum reinforcement learning, quantum kernels and variational classifiers, and quantum reservoir computing.
AI for quantum points the other way, with neural network quantum states (NNQS) finding a ground state and neural network tomography reconstructing the state a device prepared. Every case below is a billed run against the production API with its criterion fixed in advance, and five of the eight were then submitted to a quantum processor.
Every run below is reproducible from the console, or from a phone. Get the Android app
One worked example before the cases below. This is the neural wavefunction from NNQS for materials and chemistry, and it is the same table that page carries.
Run on our engines
A spin Hamiltonian solved on the TPU tier, and the H2 molecule solved on both neural engines for comparison. Submitted to each kind of compute we offer, on 16 and 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 |
|---|---|---|---|---|---|
 | neural.tpu | TPU | 12 | energy -6.918861, ceiling -6.886729, against an exact ground state of -7.048804 | $0.0395 |
 | neural.cpu | CPU | 2 | H2 at -1.116981 Ha, 0.0203 Ha above exact certificate | $0.0001 |
| neural.tpu | TPU | 2 | the same H2 molecule, -1.116981 Ha certificate | $0.0740 |
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.
Run on a quantum processor
Five cases where the trained circuit was submitted to real hardware rather than to a simulator. Training stays on a simulator because every gradient evaluation is its own task; the converged circuit is one task.
QCBM and quantum generative models
Sampling from a distribution learned from scratch
QCBM against its own untrained start
Generative modelling research, synthetic data programmes
QGAN and synthetic data
Adversarial generation against a learning discriminator
QGAN with a classical discriminator
Teams whose training data is scarce, regulated or expensive to collect
Quantum AI image generation
Writing an image into a circuit and reading it back
Patch GAN and NTQIP, both on Rigetti
Imaging research, quantum image processing
Quantum RL and control policies
Choosing an action from an observed state
Variational quantum policy trained by REINFORCE
Control systems, scheduling, operations research
NNQS for materials and chemistry
Ground states where tensor networks stop
Neural network quantum states on a Google TPU
Condensed matter, quantum chemistry, materials research
Run on the production simulators
Three cases measured on the same service and the same billing, on exact and neural engines. Each runs unchanged on the gate QPUs by naming a different engine.
QML and classification
Labelling data through a quantum feature map
Quantum kernel and variational classifier against classical baselines
Applied ML teams evaluating quantum feature maps
QRC, quantum reservoir computing
Features from a circuit that trains nothing
Quantum reservoir computing with a classical readout
Time-series and signal processing teams
NNQS tomography
Reconstructing the state a device prepared
Neural network tomography from measurement records
Hardware teams, metrology, anyone certifying a device
Where each method stops
Every figure below follows from the encoding or from the shot budget rather than from a timing, so unlike an accuracy it does not move when a benchmark is re-run. The measured results sit on each case page.
| Use case | Direction | Classical baseline | Where the method runs out |
|---|---|---|---|
| QCBM and quantum generative models | Quantum for AI | The untrained circuit, at TVD 0.459 | 2^n outcomes against a fixed shot budget: at 20 qubits most outcomes are never sampled once |
| QGAN and synthetic data | Quantum for AI | The discriminator gap, which starts at 0.0069 | Mode collapse onto a subset of outcomes, the same failure mode as a classical GAN |
| Quantum AI image generation | Quantum for AI | The target image itself, pixel by pixel | One qubit per pixel: a 108-qubit processor holds about a 10x10 image in a single job |
| Quantum RL and control policies | Quantum for AI | A random policy, at 0.533 mean return | One circuit per decision: an episode of 1,000 steps is 1,000 jobs |
| NNQS for materials and chemistry | AI for quantum | Exact diagonalisation, where the system still fits in memory | 40 spins on the neural tier; exact diagonalisation stops near 30 |
| QML and classification | Quantum for AI | Logistic regression, k-NN, an MLP and an RBF kernel | A kernel needs one circuit per pair: 1,111 circuits for 62 points |
| QRC, quantum reservoir computing | Quantum for AI | The raw coordinates, at 0.850 | A bitstring readout needs 2^n features from a fixed shot budget; local observables need n^2 |
| NNQS tomography | AI for quantum | Held-out measurement records the fit never saw | Refused outright when the measured bases cannot determine a state |
Every criterion was fixed before the run
Each case above had its success condition written down before it was submitted, and the result is published against that condition whichever way it came out. Where a run on hardware returned a worse figure than its simulator, the hardware figure is the one printed.
Every number is from a job billed on the production service at the price any customer pays, and the approximate ones carry a certified error bound. You can load the same circuits into the console, change the instance to your own data, and export a certificate for your own run.
Working on a model shape not covered here? Tell us the problem and we will benchmark it.