Deep Bayesian Experimental Design for Quantum Many-body Systems

This repository provides the research code developed for the experiments shown in the paper "Deep Bayesian Experimental Design for Quantum Many-body Systems" by Leopoldo Sarra and Florian Marquardt.

Installation

• git clone this repository
• install the requirements
  • numpy, scipy, tensorflow, matplotlib, tqdm
  • for example, run from this folder: pip install -r requirements.txt
• install the package with pip install -e .

Usage

Please refer to:

• notebook.ipynb for running experiments
• output-analysis.ipynb for analyzing previously run experiments

In particular, to run a new experiment:

• load the configuration, if desired, or define it manually:

    cfg.merge_from_file(config_path)

• define a system

    system = al.systems.get_system_from_name(cfg.SYSTEM.TYPE)(
                dim_lambda=cfg.SYSTEM.DIM_LAMBDA,
                type_lambda=cfg.SYSTEM.TYPE_LAMBDA,
                real_lambda=cfg.SYSTEM.REAL_LAMBDA,
                sigma_noise=cfg.SYSTEM.SIGMA_NOISE,
                x_range=cfg.SYSTEM.X_RANGE,
                j_coupling=not cfg.SYSTEM.COUPLING_RANDOM and cfg.SYSTEM.COUPLING,
                n_counts=cfg.SYSTEM.BINOMIAL.N_COUNTS)

• define the learner

learner = al.learners.BayesLearner.from_default(system)

• define the advisor

    advisor = al.advisors.get_advisor_from_name(cfg.ADVISOR.NAME)(
        learner=learner,
        starting_x=0,
        n_train=cfg.ADVISOR.N_TRAIN,
        batch_size=cfg.ADVISOR.BATCH_SIZE,
        parallel=parallel)

• define initial label (where to save the checkpoints)

    label = f"{cfg.PATH.SUBLABEL}-{len(learner.history):03d}"

• save the first checkpoint for the initial state

    al.utils.save_all(al.utils.h5_get_model_path(label), system, learner, advisor)

The training loop consists in:

• suggesting a new measurement
• (evaluating the metrics)
• applying the measurement and updating the prior

for i in range(cfg.TRAINING.N_MEASUREMENTS):
    label = f"{cfg.PATH.SUBLABEL}-{len(learner.history)+1:03d}"
    al.logger.info(f"Starting measurement {len(learner.history)+1:03d}")

    tf_measure_x = advisor.suggest_next_measurement()
    tf_measure_y = tf.convert_to_tensor(system.measure(tf_measure_x), K.backend.floatx())
    learner.metrics.evaluate(tf_new_y_measure=tf_measure_y)

    learner.apply_measurement(tf_measure_x, tf_measure_y)
    al.utils.save_all(al.utils.h5_get_model_path(label), system, learner, advisor)

Implementation details

Each instance of a model is composed by three elements:

• the system, which represents the physical system about which we want to learn the parameters
• the learner, which implements the Bayesian formalism and keeps track of the parameter distribution and of the measurement history
• the advisor, which implements the measurement strategy (e.g. active, random, etc.) and handles the posterior optimization

All the most important parameters of a run are stored in a config file inside the configs folder. To run a new experiment, it is enough to specify a configuration file. Results are stored in the outputs folder in a new folder with the chosen label. Each run saves checkpoints after each measurement in the models folder and logs in the logs folder.

We used to run the experiments using the SLURM job scheduler. We used the run.ipynb notebook as a helper for this purpose. This is also the reason why we employed ipyparallel to facilitate parallelization among cluster nodes (indeed, for the active strategy, it is helpful to run the optimization multiple times from different initial values of x to avoid getting trapped in local minima). This setting can be disabled with the flag config.PARALLEL.ENABLED = False. The included sbatch files are examples of engine and controller configuration settings for the ipyparallel profile. We decided to employ papermill to run notebooks from command line, but this is definitely not a requirement if you wrap the code in notebook.ipynb into a script.

An experiment on a new physical system can be created by implementing a new instance of the System class.

The code has been tested on Python 3.9.

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Citation

If you find this code useful in your work, please cite our article "Deep Bayesian Experimental Design for Quantum Many-body Systems", Leopoldo Sarra, Florian Marquardt, arXiv:2306.14510

available on

https://arxiv.org/abs/2306.14510

This work is licensed under a MIT License.