Welcome to the CUDA-Q Libraries repository

GQE Usage

GQE class usage: gqe(cost, pool, config=None, **kwargs) can take a config object and\or additional **kwargs

The config object is of type ConfigDict imported from ml_collections.

Example usage:

from ml_collections import ConfigDict
cfg = ConfigDict()
cfg.seed = 3047

The available config options are provided in the table below:

Parameter	Default Value	Description
`cfg.num_samples`	`5`	Number of circuits to generate during each epoch/batch
`cfg.max_iters`	`100`	Number of epochs to run
`cfg.ngates`	`20`	Number of gates that make up each generated circuit
`cfg.seed`	`3047`	Random seed
`cfg.lr`	`5e-7`	Learning rate used by the optimizer
`cfg.energy_offset`	`0.0`	Offset added to expectation value of the cirucit (Energy) for numerical stability, see K. Nakaji et al. (2024) Sec. 3
`cfg.grad_norm_clip`	`1.0`	max_norm for clipping gradients, see Ligthning docs
`cfg.temperature`	`5.0`	Starting inverse temperature $\beta$ as described in K. Nakaji et al. (2024) Sec. 2.2
`cfg.del_temperature`	`0.05`	Temperature increase after each epoch
`cfg.resid_pdrop`	`0.0`	The dropout probability for all fully connected layers in the embeddings, encoder, and pooler, see GPT2Config
`cfg.embd_pdrop`	`0.0`	The dropout ratio for the embeddings, see GPT2Config
`cfg.attn_pdrop`	`0.0`	The dropout ratio for the attention, see GPT2Config
`cfg.small`	`False`	True: Uses a small transfomer (6 hidden layers and 6 attention heads as opposed to the default transformer of 12 of each)
`cfg.save_dir`	`"./output/"`	Path to save files

The **kwargs takes the following args:

arg	Description
`model`	Can pass in an already constructed transformer
`optimizer`	Can pass in an already constructed optimizer

In addition, **kwargs can be used to overwrite any of the default configs if you don't pass in a full config object, i.e:

arg	Description
`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