diff --git a/.github/workflows/quality.yml b/.github/workflows/quality.yml index bee7907..7a421ad 100644 --- a/.github/workflows/quality.yml +++ b/.github/workflows/quality.yml @@ -11,7 +11,7 @@ jobs: steps: - uses: actions/checkout@v4 with: - submodules: 'true' + submodules: 'recursive' - name: Install poetry run: pipx install poetry - name: Set up Python 3.11 diff --git a/.gitmodules b/.gitmodules index c05c4fb..00979a4 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,6 +1,3 @@ -[submodule "trackml-library"] - path = trackml-library - url = git@github.com:stroblme/trackml-library.git [submodule "hepqpr-qallse"] path = hepqpr-qallse url = git@github.com:lfd/hepqpr-qallse.git diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 9391e4f..0d6a5ff 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -12,10 +12,11 @@ Start of by.. ## Setup Contributing to this project requires some more dependencies besides the "standard" packages. -Those are specified in the groups `dev` and `docs`. +Those are specified in the groups `dev`. ``` -poetry install --with dev,docs +poetry install --with dev ``` +These dependencies are essentially flake8, black, pre-commit and licensecheck which we use for quality assurance and code formatting. Additionally, we have pre-commit hooks in place, which can be installed as follows: ``` @@ -24,25 +25,4 @@ poetry run pre-commit install ``` Currently the only purpose of the hook is to run Black on commit which will do some code formatting for you. -However be aware, that this might reject your commit and you have to re-do the commit. - -## Testing - -We do our testing with Pytest. Corresponding tests can be triggered as follows: -``` -poetry run pytest -``` -There are Github action pipelines in place, that will do linting and testing once you open a pull request. -However, it's a good idea to run tests and linting (either Black or Flake8) locally before pushing. - -## Documentation - -We use MkDocs for our documentation. To run a server locally, run: -``` -poetry run mkdocs serve -``` -This will automatically trigger a rebuild each time you make changes. -See the [MkDocs Documentation](https://cirkiters.github.io/qml-essentials/usage/) for more details. - -Publishing (and building) the documentation is done automagically using Github actions. -This action is triggered when a new release is made. \ No newline at end of file +However be aware, that this might reject your commit and you have to re-do the commit. \ No newline at end of file diff --git a/README.md b/README.md index 64e448b..6e4cb28 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,16 @@ # From Hope To Heuristics **Realistic Runtime Estimates for Quantum Optimisation in NHEP** -## Approach +## :book: Project Description -For the QUBO formulation we build on the [HEPQPR.Qallse](https://github.com/derlin/hepqpr-qallse) project. +This is the repository for our contribution to [CHEP24](https://indico.cern.ch/event/1338689/contributions/6010081/) consisting of two key aspects: +Firstly, we estimate runtimes and scalability for common NHEP problems addressed via QUBO formulations by identifying minimum energy solutions of intermediate Hamiltonian operators encountered during the annealing process. +Secondly, we investigate how the classical parameter space in the QAOA, together with approximation techniques such as a Fourier-analysis based heuristic, proposed by Zhou et al. (2018), can help to achieve (future) quantum advantage, considering a trade-off between computational complexity and solution quality. +Those approaches are evaluated on two benchmark problems: the Maxcut problem and the track reconstruction problem. - +## Approach +For the QUBO formulation of the track reconstruction problem, we build on the [HEPQPR.Qallse](https://github.com/derlin/hepqpr-qallse) project. To work with smaller sized QUBOs, we only focus on hit-triplets present in a specified angle, similar to the approach presented by [Schwägerl et al.](https://arxiv.org/pdf/2303.13249). ## :rocket: Getting Started @@ -17,20 +21,13 @@ When cloning, make sure to get the submodule: ``` git clone --recurse-submodules git@github.com:lfd/spectral_gap_nhep.git ``` +This will clone [our fork of hepqr-qallse](https://github.com/lfd/hepqpr-qallse) and [our fork of trackml](https://github.com/lfd/trackml) recursively. +The latter is needed by hepqr-qallse but is not shipped via pip. If you have poetry installed, run `poetry install`. -With pip, make sure to include the dependencies in the submodule `trackml-library` (pandas and numpy) and `hepqpr-qallse` (pandas, numpy, plotly). +With pip, make sure to include the dependencies in the submodule `hepqpr-qallse` (pandas, numpy, plotly) and `trackml` (pandas, numpy). - - ### Quickstart After installing all the dependencies, simply execute @@ -39,6 +36,10 @@ kedro run ``` This will run the default pipeline which consists of all individual pipelines described in the following section. +You can get an interactive overview of the pipeline in your browser by running +``` +kedro viz +``` ### Pipelines @@ -79,16 +80,22 @@ The following list gives a brief explanation of the most important locations in Besides that, we make use of two submodules: - `hepqpr-qallse`: Currently, all the data loading and QUBO formulation is done using this submodule -- `trackml-library`: Not in use currently ## Hyperparameter Optimization This project uses Optuna for hyperparameter optimization. -You can take a look at the experiments by running +There is a dedicated kedro pipeline that takes care of the hyperparameter optimization and submission of jobs to a SLURM cluster. +``` +kedro run --pipeline hyperparameter_study +``` + +If you don't have a SLURM cluster available, head to `pipelines/hyperparameter_study/nodes.py` switch the subprocess command such that it spawns a single kedro job instead of a submission to the cluster. + +Supposing everything goes well, you can take a look at the experiments by running ``` optuna-dashboard sqlite:///studies/fhth.db ``` -supposing that the path to the sqlite database where Optuna stores its results is `storage/fhth.db`. +supposing that the path to the sqlite database where Optuna stores its results is `studies/fhth.db`. ## 🚧 Contributing diff --git a/doc/kedro-pipeline.svg b/doc/kedro-pipeline.svg index d874ceb..2ed90f6 100644 --- a/doc/kedro-pipeline.svg +++ b/doc/kedro-pipeline.svg @@ -1,4 +1,4 @@ -Event HitsEvent ParticlesEvent TruthParams: Data FractionParams: Num Angle PartsParams: Geometric IndexParams: SeedParams: Num Anneal FractionsParams: OptimiserParams: QParams: Max PParams: Maxcut Graph DensityParams: Maxcut N QubitsExport ParametersExport ParametersTrackrecon ParametersMaxcut ParametersCreate MetadataParticlesHitsTruthDoubletsCreate Qallse DatawrapperData WrapperBuild QubosQubosRun Track Reconstruction AnnealingRun Maxcut AnnealingAdiabatic Track Reconstruction ResultsAdiabatic Maxcut ResultsParams: Hyperhyper Trial IdParams: InitialisationParams: Apply BoundsParams: MaxiterParams: OptionsParams: ToleranceRun Track Reconstruction QaoaRun Maxcut QaoaQaoa Track Reconstruction ResultsQaoa Maxcut ResultsMetadataSolve QubosCreate Anneal ScheduleCreate Anneal ScheduleResponsesAnneal Schedule Track Reconstruction ResultsAnneal Schedule Maxcut ResultsVisualizeTrackrecon Figures \ No newline at end of file diff --git a/hepqpr-qallse 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object wrapper for zip files" optional = false -python-versions = ">=3.8" +python-versions = ">=3.9" files = [ - {file = "zipp-3.20.2-py3-none-any.whl", hash = "sha256:a817ac80d6cf4b23bf7f2828b7cabf326f15a001bea8b1f9b49631780ba28350"}, - {file = "zipp-3.20.2.tar.gz", hash = "sha256:bc9eb26f4506fda01b81bcde0ca78103b6e62f991b381fec825435c836edbc29"}, + {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"}, + {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"}, ] [package.extras] @@ -3638,4 +3625,4 @@ type = ["pytest-mypy"] [metadata] lock-version = "2.0" python-versions = "^3.11" -content-hash = "8da20430dea6b432476808033f9c5b05d1db787357be6a75b468a89c49af3f0f" +content-hash = "24abeffe45daa9a1645f0d51f31050ed1a9f01b59037d2ef3dbca41aeb634b33" diff --git a/pyproject.toml b/pyproject.toml index 4b9c2b8..9957238 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -38,7 +38,6 @@ qiskit = "^1.2.4" qiskit-algorithms = "^0.3.0" networkx = "^3.3" scipy = "^1.14.1" -trackml = { path = "./trackml-library" } qallse = { path = "./hepqpr-qallse" } kedro = "^0.19.8" kedro-viz = "^10.0.0" @@ -54,7 +53,4 @@ licensecheck = "^2024.3" [build-system] requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" - -[tool.kedro_telemetry] -project_id = "02bc8c61d7e64f47b985e83579e35049" +build-backend = "poetry.core.masonry.api" \ No newline at end of file diff --git a/slurm.sh b/slurm.sh index f9773ce..9e21c68 100755 --- a/slurm.sh +++ b/slurm.sh @@ -24,7 +24,7 @@ # output path #SBATCH --output="logs/slurm/slurm-%j-%x.out" -module load devel/python/3.11.7_intel_2021.4.0 +module load devel/python/3.11.7 # Space for two arguments: pipeline and params ~/spectral_gap_nhep/.venv/bin/python -m kedro run $1 $2 diff --git a/src/fromhopetoheuristics/pipelines/hyperparameter_study/nodes.py b/src/fromhopetoheuristics/pipelines/hyperparameter_study/nodes.py index 5e3d47e..a1ed044 100644 --- a/src/fromhopetoheuristics/pipelines/hyperparameter_study/nodes.py +++ b/src/fromhopetoheuristics/pipelines/hyperparameter_study/nodes.py @@ -1,7 +1,8 @@ -from typing import Dict, List +from typing import Dict, List, Optional, Callable import subprocess import pandas as pd import os +import optuna from fromhopetoheuristics.utils.hyperparam_optimizer import Hyperparam_Optimizer @@ -11,10 +12,10 @@ def create_hyperparam_optimizer( - n_trials: str, + n_trials: int, timeout: int, - enabled_hyperparameters: List, - optimization_metric: List, + enabled_hyperparameters: List[str], + optimization_metric: List[str], path: str, sampler: str, sampler_seed: int, @@ -26,8 +27,8 @@ def create_hyperparam_optimizer( resume_study: bool, n_jobs: int, run_id: str, - hyperparameters: Dict, -) -> Hyperparam_Optimizer: + hyperparameters: Dict[str, List[float]], +) -> Dict[str, Hyperparam_Optimizer]: hyperparam_optimizer = Hyperparam_Optimizer( name=run_id, @@ -51,7 +52,10 @@ def create_hyperparam_optimizer( hyperparam_optimizer.set_fixed_parameters({}) def objective( - trial, parameters, report_callback=None, early_stop_callback=None + trial: optuna.trial.Trial, + parameters: Dict[str, float], + report_callback: Optional[Callable[[Dict[str, float], int], None]] = None, + early_stop_callback: Optional[Callable[[], bool]] = None, ) -> float: """This function is the optimization target that is called by Optuna for each trial. It runs the experiment with the given parameters @@ -102,7 +106,7 @@ def objective( ] ) - def get_objective_for_trial(trial_id) -> float: + def get_objective_for_trial(trial_id: int) -> float: tmp_file_name = f".hyperhyper{trial_id}.json" results = pd.read_json(tmp_file_name) os.remove(tmp_file_name) @@ -125,17 +129,16 @@ def get_objective_for_trial(trial_id) -> float: return {"hyperparam_optimizer": hyperparam_optimizer} -def run_optuna( - hyperparam_optimizer: Hyperparam_Optimizer, -): - hyperparam_optimizer.minimize(idx=0) +def run_optuna(hyperparam_optimizer: Hyperparam_Optimizer) -> None: + """ + Run the hyperparameter optimization study. + + Args: + hyperparam_optimizer: The hyperparameter optimizer to run. - # try: - # hyperparam_optimizer.log_study( - # selected_parallel_params=optuna_selected_parallel_params, - # selected_slice_params=optuna_selected_slice_params, - # ) - # except Exception as e: - # log.exception("Error while logging study") + Returns: + None + """ + hyperparam_optimizer.minimize(idx=0) return {} diff --git a/src/fromhopetoheuristics/utils/data_structures.py b/src/fromhopetoheuristics/utils/data_structures.py index 3c58f06..2793ff2 100644 --- a/src/fromhopetoheuristics/utils/data_structures.py +++ b/src/fromhopetoheuristics/utils/data_structures.py @@ -8,6 +8,21 @@ class ExtendedDoublet(Doublet): in the XY Plane""" def __init__(self, hit_start: Hit, hit_end: Hit): + """ + Create an extended doublet. + + Parameters + ---------- + hit_start : Hit + The hit at the start of the doublet. + hit_end : Hit + The hit at the end of the doublet. + + Notes + ----- + The angle in the XY plane between this doublet and the Y axis + is stored in `xy_angle`. + """ super().__init__(hit_start, hit_end) #: The angle in the XY plane between this doublet and the Y axis. @@ -19,6 +34,18 @@ class ExtendedTriplet(Triplet): in the XY Plane for the first doublet""" def __init__(self, d1: ExtendedDoublet, d2: ExtendedDoublet): + """ + Create an extended triplet. + + Parameters + ---------- + d1, d2 : ExtendedDoublet + The two doublets composing this triplet. + + Notes + ----- + The angle in the XY plane of the first hit is stored in `xy_angle`. + """ super().__init__(d1, d2) # Angle in the XY-Plane of the first hit diff --git a/src/fromhopetoheuristics/utils/design.py b/src/fromhopetoheuristics/utils/design.py deleted file mode 100644 index 0b9b7e8..0000000 --- a/src/fromhopetoheuristics/utils/design.py +++ /dev/null @@ -1,23 +0,0 @@ -class design: - print_figure_title = True - - title_font_size = 18 - legend_font_size = 16 - - legend_x_pos = 0 - legend_y_pos = 1 - - scatter_legend = dict( - x=legend_x_pos, - y=legend_y_pos, - orientation="h", - traceorder="normal", - font=dict( - size=legend_font_size, - ), - ) - base_theme = "simple_white" - - showgrid = False - - scatter_markers = "lines" diff --git a/src/fromhopetoheuristics/utils/hyperparam_optimizer.py b/src/fromhopetoheuristics/utils/hyperparam_optimizer.py index 10f8aa8..d95397e 100644 --- a/src/fromhopetoheuristics/utils/hyperparam_optimizer.py +++ b/src/fromhopetoheuristics/utils/hyperparam_optimizer.py @@ -1,10 +1,6 @@ import optuna as o from typing import List, Dict, Optional, Any -import plotly.graph_objects as go - -from fromhopetoheuristics.utils.design import design - class Hyperparam_Optimizer: def __init__( @@ -106,6 +102,22 @@ def objective(): raise NotImplementedError("Objective method must be set!") def select_hyperparams(self, pair): + """ + Given a hyperparameter pair, this function returns True if the + hyperparameter is enabled for optimization and False otherwise. + + If enabled_hyperparameters is empty, then all hyperparameters are enabled. + + Parameters + ---------- + pair: + A tuple consisting of the hyperparameter name and its value. + + Returns + ------- + bool + True if the hyperparameter is enabled, False otherwise. + """ key, _ = pair return ( len(self.enabled_hyperparameters) == 0 @@ -113,11 +125,45 @@ def select_hyperparams(self, pair): ) def set_variable_parameters(self, parameters: Dict): + """ + Set the variable hyperparameters for optimization. + + This method is used to set the hyperparameters that should be optimized. + The hyperparameters are filtered based on the enabled_hyperparameters + set in the constructor. If enabled_hyperparameters is empty, then all + hyperparameters are enabled. + + Parameters + ---------- + parameters : Dict + A dictionary of hyperparameters to optimize. + + Returns + ------- + None + """ self.variable_parameters = dict( filter(self.select_hyperparams, parameters.items()) ) def set_fixed_parameters(self, parameters: Dict): + """ + Set the fixed hyperparameters for optimization. + + This method sets the hyperparameters that are fixed and not subject to + optimization. The hyperparameters are filtered based on the + enabled_hyperparameters set in the constructor. If enabled_hyperparameters + is empty, then all hyperparameters are considered fixed. + + Parameters + ---------- + parameters : Dict + A dictionary of hyperparameters to be fixed. + + Returns + ------- + None + """ self.fixed_parameters = dict( filter(self.select_hyperparams, parameters.items()) ) @@ -270,12 +316,36 @@ def update_variable_parameters( return updated_variable_parameters def minimize(self, idx: int): + """ + Run the optimization of a study. + + Parameters + ---------- + idx : int + The index of the study to run. + + Returns + ------- + None + """ result = self.studies[idx].optimize( self.run_trial, n_trials=self.n_trials, n_jobs=self.n_jobs ) self.studies[idx].tell(result) def run_trial(self, trial): + """ + This function is the optimization target that is called by Optuna + for the given trial using the variable parameters and fixed parameters + set before. + It calls the objective function and thus requires it being implemented. + + Args: + trial: The Optuna trial object. + + Returns: + The result of the experiment. + """ updated_variable_parameters = self.update_variable_parameters( trial, self.variable_parameters ) diff --git a/src/fromhopetoheuristics/utils/model.py b/src/fromhopetoheuristics/utils/model.py index 0370119..f1ed8da 100644 --- a/src/fromhopetoheuristics/utils/model.py +++ b/src/fromhopetoheuristics/utils/model.py @@ -7,8 +7,7 @@ ExtendedTriplet, ) -from typing import Tuple -import json +from typing import Tuple, List import logging log = logging.getLogger(__name__) @@ -20,12 +19,52 @@ class SplitConfig(D0Config): class QallseSplit(QallseD0): + """ + A QallseSplit is a QallseD0 model that is split into multiple parts based on + the angle in the XY plane of the first hit of each doublet. This is done by + overriding the `_create_doublets` and `_create_triplets` methods to apply + early cuts to the doublets and triplets based on the angle part of the first + hit. The angle part is determined by the `geometric_index` parameter of + the `config` attribute. + + Attributes + ---------- + config : SplitConfig + The configuration for the QallseSplit model. + + Methods + ------- + _create_doublets(initial_doublets) + Generate Doublet structures from the initial doublets, calling + `_is_invalid_doublet` to apply early cuts. + _create_triplets() + Generate Triplet structures from Doublets, calling + `_is_invalid_triplet` to apply early cuts. + _is_invalid_triplet(triplet) + Check if a Triplet is invalid based on the angle part of the first hit. + _get_base_config() + Return the base configuration for the QallseSplit model. + serialize() + Serialize the model and their associated xplets. + """ + config = SplitConfig() - def _create_doublets(self, initial_doublets): - # Generate Doublet structures from the initial doublets, - # calling _is_invalid_doublet to apply early cuts - doublets = [] + def _create_doublets(self, initial_doublets: List[Tuple[int, int]]) -> None: + """ + Generate Doublet structures from the initial doublets, calling + _is_invalid_doublet to apply early cuts. + + Parameters + ---------- + initial_doublets : List[Tuple[int, int]] + A list of tuples containing start and end hit IDs. + + Returns + ------- + None + """ + doublets: List[ExtendedDoublet] = [] for start_id, end_id in initial_doublets: start, end = self.hits[start_id], self.hits[end_id] d = ExtendedDoublet(start, end) @@ -37,10 +76,16 @@ def _create_doublets(self, initial_doublets): self.logger.info(f"created {len(doublets)} doublets.") self.doublets = doublets - def _create_triplets(self): - # Generate Triplet structures from Doublets, - # calling _is_invalid_triplet to apply early cuts - triplets = [] + def _create_triplets(self) -> None: + """ + Generate Triplet structures from Doublets, calling + _is_invalid_triplet to apply early cuts. + + Returns + ------- + None + """ + triplets: List[ExtendedTriplet] = [] for d1 in self.doublets: for d2 in d1.h2.outer: t = ExtendedTriplet(d1, d2) @@ -51,7 +96,20 @@ def _create_triplets(self): self.logger.info(f"created {len(triplets)} triplets.") self.triplets = triplets - def _is_invalid_triplet(self, triplet: ExtendedTriplet): + def _is_invalid_triplet(self, triplet: ExtendedTriplet) -> bool: + """ + Check if a triplet is invalid with respect to the geometric index. + + Parameters + ---------- + triplet : ExtendedTriplet + The triplet to check + + Returns + ------- + bool + True if invalid, False otherwise. + """ if super()._is_invalid_triplet(triplet): return True @@ -92,24 +150,28 @@ def serialize(self) -> Tuple: with use_markers(self, **qubo_kwargs) as altered_model: qubo = altered_model.to_qubo() - # class NumpyTypeEncoder(json.JSONEncoder): - # def default(self, obj): - # if isinstance(obj, np.generic): - # return obj.item() - # elif isinstance(obj, np.ndarray): - # return obj.tolist() - # return json.JSONEncoder.default(self, obj) - - # # This is ugly.. serioulsy, don't look at it too long - # qubo = json.loads(json.dumps(qubo, cls=NumpyTypeEncoder)) - # xplet = json.loads(json.dumps(xplet, cls=NumpyTypeEncoder)) - return qubo, xplet -def build_model(doublets, model, add_missing): - - # prepare doublets +def build_model( + doublets: List[ExtendedDoublet], model: QallseSplit, add_missing: bool +) -> None: + """ + Prepares and builds the QUBO model from the given doublets. + + Parameters + ---------- + doublets : List[ExtendedDoublet] + A list of extended doublets to be used in the model. + model : QallseSplit + The QallseSplit model to build the QUBO. + add_missing : bool + Flag indicating whether to add missing doublets. + + Returns + ------- + None + """ if add_missing: log.info("Cheat on, adding missing doublets.") doublets = model.dataw.add_missing_doublets(doublets) @@ -119,5 +181,4 @@ def build_model(doublets, model, add_missing): f"Precision: {p * 100:.4f}%, Recall:{r * 100:.4f}%, Missing: {len(ms)}" ) - # build the qubo model.build_model(doublets=doublets) diff --git a/src/fromhopetoheuristics/utils/spectral_gap_calculator.py b/src/fromhopetoheuristics/utils/spectral_gap_calculator.py index ec15371..0aedf00 100644 --- a/src/fromhopetoheuristics/utils/spectral_gap_calculator.py +++ b/src/fromhopetoheuristics/utils/spectral_gap_calculator.py @@ -1,5 +1,5 @@ import numpy as np -from typing import List, Iterable +from typing import List, Iterable, Tuple, Dict, Union from qiskit.quantum_info import SparsePauliOp, Pauli from qiskit_algorithms.eigensolvers import NumPyEigensolver @@ -7,10 +7,19 @@ from fromhopetoheuristics.utils.qaoa_utils import hamiltonian_from_qubo -def build_mixing_hamiltonian(num_qubits): - # As in - # https://qiskit.org/documentation/_modules/qiskit/circuit/library/ - # n_local/qaoa_ansatz.html#QAOAAnsatz +def build_mixing_hamiltonian(num_qubits: int) -> SparsePauliOp: + """ + Builds a mixing hamiltonian for the given number of qubits. + This is built according to the QAOAAnsatz implementation in Qiskit. + See https://qiskit.org/documentation/_modules/qiskit/circuit/library/ + n_local/qaoa_ansatz.html#QAOAAnsatz + + Args: + num_qubits: The number of qubits in the hamiltonian to build + + Returns: + A SparsePauliOp representing the mixing hamiltonian + """ mixer_terms = [ ("I" * left + "X" + "I" * (num_qubits - left - 1), 1) for left in range(num_qubits) @@ -19,9 +28,21 @@ def build_mixing_hamiltonian(num_qubits): return mixer_hamiltonian -# Builds the interpolation hamiltonian for the given problem hamiltonian, -# fraction s and amount of qubits -def build_hamiltonian(qubo, fraction, num_qubits): +def build_hamiltonian( + qubo: np.ndarray, fraction: float, num_qubits: int +) -> SparsePauliOp: + """ + Builds the interpolation hamiltonian for the given problem hamiltonian, + fraction s and amount of qubits + + Args: + qubo: The QUBO matrix + fraction: The interpolation fraction s, s in [0,1] + num_qubits: The number of qubits in the hamiltonian to build + + Returns: + A SparsePauliOp representing the interpolation hamiltonian + """ cost_hamiltonian, offset = hamiltonian_from_qubo(qubo) mixer_hamiltonian = build_mixing_hamiltonian(num_qubits) offset_hamiltonian = SparsePauliOp(["I"]) @@ -35,18 +56,31 @@ def build_hamiltonian(qubo, fraction, num_qubits): return H -# Derives the spectral gap for the given problem hamiltonian at the given fraction s -def calculate_spectral_gap(fraction: float, qubo: np.ndarray, num_dec_pos: int = 4): +def calculate_spectral_gap( + fraction: float, qubo: np.ndarray, num_dec_pos: int = 4 +) -> Tuple[float, float, float]: + """ + Calculates the spectral gap for a given problem hamiltonian at a given fraction s. + + Args: + fraction: The interpolation fraction s, s in [0,1] + qubo: The QUBO matrix + num_dec_pos: The number of decimal positions to round the eigenvalues to + + Returns: + A tuple of three floats, the ground state energy, + the first excited state energy, and the spectral gap + """ num_qubits = len(qubo) H = build_hamiltonian(qubo, fraction, num_qubits) counter = 0 - eigenvalues = [] - # eigenstates = [] + eigenvalues: List[float] = [] + # eigenstates: List[np.ndarray] = [] while len(set(eigenvalues)) < 2 and counter != num_qubits: # Increase the counter in every iteration, such that the number # of searched eigenvalues exponentially increases # as long as no two unique eigenvalues are found - counter = counter + 1 + counter += 1 eigensolver = NumPyEigensolver(k=pow(2, counter)) eigensolver_result = eigensolver.compute_eigenvalues(H) # eigenstates = eigensolver_result.eigenstates @@ -61,11 +95,23 @@ def calculate_spectral_gap(fraction: float, qubo: np.ndarray, num_dec_pos: int = def annealing( qubo: np.ndarray, fractions: Iterable[float], -) -> List[dict]: - results = [] +) -> List[Dict[str, Union[float, Tuple[float, float, float]]]]: + """ + Calculates the spectral gap for a given problem hamiltonian at + a given interpolation fraction s. + + Args: + qubo: The QUBO matrix + fractions: The interpolation fractions s, s in [0,1] + + Returns: + A list of dictionaries, where each dictionary contains the + fraction, ground state energy, first excited state energy, + and the spectral gap + """ + results: List[Dict[str, Union[float, Tuple[float, float, float]]]] = [] for fraction in fractions: - res = {"fraction": fraction} res = {"fraction": fraction} res["gs"], res["fes"], res["gap"] = calculate_spectral_gap(fraction, qubo) results.append(res) diff --git a/trackml-library b/trackml-library deleted file mode 160000 index 6bcf062..0000000 --- a/trackml-library +++ /dev/null @@ -1 +0,0 @@ -Subproject commit 6bcf062a609fbd88a1fadf63f8818128e4fb62b8