gqe(cost, pool, config=None, **kwargs)

Run the Generative Quantum Eigensolver algorithm.

GQE uses a transformer model to learn which quantum operators from a pool should be applied to minimize a given cost function. Python-only implementation.

The GQE implementation in CUDA-Q Solvers is based on this paper: K. Nakaji et al. (2024).

Parameters:

  • cost – Cost function that evaluates operator sequences

  • pool – List of quantum operators to select from

  • config – Optional configuration object. If None, uses kwargs to override defaults

  • kwargs –

    Optional keyword arguments to override default configuration. The following special arguments are supported:

    • model: Can pass in an already constructed transformer

    • optimizer: Can pass in an already constructed optimizer

    Additionally, any default config parameter can be overridden via kwargs if no config object is provided, for example:

    • max_iters: Overrides cfg.max_iters for total number of epochs to run

    • energy_offset: Overrides cfg.energy_offset for offset to add to expectation value

Returns:

tuple: Minimum energy found, corresponding operator indices

cudaq_solvers.gqe_algorithm.gqe.get_default_config()

Create a default configuration for GQE.

Returns:

Default configuration for GQE with the following parameters:

  • num_samples (int): Number of circuits to generate during each epoch/batch. Default=5

  • max_iters (int): Number of epochs to run. Default=100

  • ngates (int): Number of gates that make up each generated circuit. Default=20

  • seed (int): Random seed. Default=3047

  • lr (float): Learning rate used by the optimizer. Default=5e-7

  • energy_offset (float): Offset added to expectation value of the circuit (Energy) for numerical

    stability, see K. Nakaji et al. (2024) Sec. 3. Default=0.0

  • grad_norm_clip (float): max_norm for clipping gradients, see Lightning docs. Default=1.0

  • temperature (float): Starting inverse temperature β as described in K. Nakaji et al. (2024)

    Sec. 2.2. Default=5.0

  • del_temperature (float): Temperature increase after each epoch. Default=0.05

  • resid_pdrop (float): The dropout probability for all fully connected layers in the embeddings,

    encoder, and pooler, see GPT2Config. Default=0.0

  • embd_pdrop (float): The dropout ratio for the embeddings, see GPT2Config. Default=0.0

  • attn_pdrop (float): The dropout ratio for the attention, see GPT2Config. Default=0.0

  • small (bool): Uses a small transformer (6 hidden layers and 6 attention heads as opposed to

    the default transformer of 12 of each). Default=False

  • use_fabric_logging (bool): Whether to enable fabric logging. Default=False

  • fabric_logger (object): Fabric logger to use for logging. If None, no logging will be done. Default=None

  • save_trajectory (bool): Whether to save the trajectory data to a file. Default=False

  • trajectory_file_path (str): Path to save the trajectory data file. Default=”gqe_logs/gqe_trajectory.json”

  • verbose (bool): Enable verbose output to the console. Output includes the epoch, loss,

    model.train_step time, and minimum energy. Default=False