Quantum Portfolio Optimizer API reference

Type: `json`

Dictionary with the asset prices. The data must be structured as a JSON object that stores information about the closing prices of financial assets on specific dates. The format is as follows:

• Primary key (string): The name or ticker symbol of the financial asset (for example, "8801.T").
• Secondary key (string): The date in YYYY-MM-DD format.
• Value (number): The closing price of the asset on the specified date. Prices can be entered either normalized or non-normalized.

Note that all dictionaries must have the same secondary key (dates). If a specific asset lacks a date that others have, the data must be filled to ensure consistency. For example, this can be done by using the last tracked closing price of that asset.

• Required: Yes
• Example:

{
    "8801.T": {
        "2023-01-01": 2374.0,
        "2023-01-02": 2374.0,
        "2023-01-03": 2374.0,
        "2023-01-04": 2356.5,
        ...
    },
    "AAPL": {
        "2023-01-01": 145.2,
        "2023-01-02": 146.5,
        "2023-01-03": 147.3,
        "2023-01-04": 148.1,
        ...
    },
    ...
}

{
    "asset_name": {
        "date": closing_value,
        ...
    },
    ...
}

Type: `json`

Settings of the QUBO. The following table describes the keys of the qubo_settings dictionary. Build the dictionary specifying the number of time steps nt, the number of resolution qubits nq, and the max_investment - or change other default values.

• Required: Yes

Type: `json`

Default value: `None`

Settings of the ansatz. To modify the default options, create a dictionary for the ansatz_settings parameter with the following keys. By default, the ansatz is set to "real_amplitudes", and both extra options (see the following table) are set to False.

* Available ansatzes

• real_amplitudes
• cyclic
• optimized_real_amplitudes
• tailored (Only for ibm_torino backend, 7 assets, 4 time steps, and 4 resolution qubits)

** If multiple_passmanager is set to False, the function uses the default Qiskit pass manager with optimization_level=3. If set to True, the multiple_passmanager subroutine compares three pass managers: the previous default Qiskit pass manager, a pass manager mapping qubits over the QPU first neighbors chain, and the AI transpiler services. Then, the pass manager with the estimated lower cumulative error is selected.

• Required: No

Type: `json`

Default value: `None`

Settings of the optimizer. This parameter is a dictionary with some tunable options of the optimizing process.

Under primitive_options and optimizer_options keys we set dictionaries with the following parameters:

primitive_options

optimizer_options

• Required: No

Type: `str`

The QPU backend name

• Required: No
• Example: ibm_torino

Type: `list` of `str`

Default value: Empty list

List of session IDs to retrieve data from previous runs. To resume an execution or retrieve jobs that were processed in one or more previous sessions, the list of session IDs must be passed in the previous_session_id parameter. This is particularly useful in cases where an optimization task failed to complete due to any error in the process, and execution needs to finish. To achieve this, you must provide the same arguments used in the initial execution, along with the previous_session_id list as described.

• Required: No
• Example: ["session_id_1", "session_id_2"]

Type: `bool`

Default value: `True`

Apply noise-aware SQD post-processing.

• Required: No
• Example: True

Type: `list` of `str`

Default value: Empty list

List of tags to identify the experiment.

• Required: No
• Example: ["optimization", "quantum_computing"]

Type: str

Default value: "M"

Size the job runs at, which is "S", "M", or "L". Pass it to dpo_solver.run(...) with the other inputs. The required size depends on the number of variables of the problem, which is the number of assets times nt times nq, and on the number of generations, num_generations: up to 50 variables is "S", more than 50 variables with up to three generations is "M", and more than 50 variables with four or more generations is "L". To calculate the size before you submit a job, see Function sizes. If the size is too small, the job fails with error 4600. If the size is larger than the job needs, the job runs normally and records a 4601 advisory event.

• Required: No
• Example: "L"

Type: dict[str, dict[str, float]]

Contains the investment strategy over time, with each time-stamp mapping to asset-specific investment weights (each weight is the investment amount normalized by the total investment amount).

• Example: {'time_1': {'asset_1': 0.2, 'asset_2': 0.3, ...}, ...}

Type: dict[str, Any]

Data generated during the analysis, including solutions, costs, and metrics.

Description of the all_samples_metrics dictionary