Microsoft QIO¶
JijQIOBaseSampler ¶
Bases: JijZeptBaseSampler, typ.Generic[P]
sample_hubo(J, parameters=None, timeout=None, sync=True, queue_name=None, **kwargs) ¶
Microsoft QIO simulated annealing solver is performed for binary polynomial models.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
J | Dict | Polynomial interactions. | required |
vartype | str | The variable type. "SPIN" or "BINARY". | required |
num_sweeps | Optional[int] | The number of Monte-Carlo steps. If | required |
beta_min | Optional[float] | Minimum (initial) inverse temperature. If | required |
beta_max | Optional[float] | Maximum (final) inverse temperature. If | required |
num_reads | Optional[int] | The number of samples. If | required |
seed | Optional[int] | Seed for Monte Carlo algorithm. If | required |
platform | Optional[str] | "CPU" or "FPGA". If | required |
timeout | Optional[int] | The number of timeout [sec] for post request. If | None |
sync | bool | Synchronous mode. | True |
Returns:
| Type | Description |
|---|---|
JijModelingResponse | dimod.SampleSet: Stores minimum energy samples and other information. |
Examples:
import jijzept as jz
sampler = jz.JijQIOSASampler(config='config.toml')
response = sampler.sample_hubo(J={(0,1,2,3,4): -1}, vartype="SPIN")
sample_model(model, feed_dict, multipliers={}, fixed_variables={}, search=False, num_search=15, algorithm=None, parameters=None, timeout=None, sync=True, queue_name=None, **kwargs) ¶
Sample using JijModeling by means of Microsoft QIO Simulated Annealing solver.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
model | jijmodeling.Problem | Mathematical model is constracted by JijModeling. | required |
feed_dict | Dict[str, Union[Number, list, np.ndarray]] | The actual values to be assigned to the placeholders. | required |
multipliers | Dict[str, Number] | The actual multipliers for penalty terms, derived from constraint conditions. | {} |
fixed_variables | Dict[str, Dict[Tuple[int, ...], Union[int, float]]] | dictionary of variables to fix. | {} |
search | bool | If | False |
num_search | int | The number of parameter search iteration. Defaults to set 15. This option works if | 15 |
algorithm | Optional[str] | Algorithm for parameter search. Defaults to None. | None |
parameters | P | None | (P | None, optional): parameters for QIO. Defaults to None. | None |
timeout | Optional[int] | The number of timeout [sec] for post request. If | None |
sync | bool | Synchronous mode. | True |
Returns:
| Name | Type | Description |
|---|---|---|
JijModelingResponse | JijModelingResponse | Stores minimum energy samples and other information. |
Examples:
import jijzept as jz
import jijmodeling as jm
n = jm.Placeholder('n')
x = jm.Binary('x', shape=n)
d = jm.Placeholder('d', shape=n)
i = jm.Element("i", n)
problem = jm.Problem('problem')
problem += jm.Sum(i, d[i] * x[i])
sampler = jz.JijQIOSASampler(config='config.toml')
response = sampler.sample_model(problem, feed_dict={'n': 5, 'd': [1,2,3,4,5]})
sample_qubo(Q, parameters=None, timeout=None, sync=True, queue_name=None, **kwargs) ¶
Microsoft QIO solver is performed for binary quadratic models.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
Q | Dict | The linear or quadratic terms. | required |
num_sweeps | Optional[int] | The number of Monte-Carlo steps. If | required |
beta_min | Optional[float] | Minimum (initial) inverse temperature. If | required |
beta_max | Optional[float] | Maximum (final) inverse temperature. If | required |
num_reads | Optional[int] | The number of samples. If | required |
seed | Optional[int] | Seed for Monte Carlo algorithm. If | required |
platform | Optional[str] | "CPU" or "FPGA". If | required |
timeout | Optional[int] | The number of timeout [sec] for post request. If | None |
sync | bool | Synchronous mode. | True |
Returns:
| Type | Description |
|---|---|
JijModelingResponse | dimod.SampleSet: Stores minimum energy samples and other information. |
Examples:
import jijzept as jz
sampler = jz.JijQIOSASampler(config='config.toml')
response = sampler.sample_qubo(Q={(0,0): -0.5, (0,1): -1, (0,2): -1, (2,3): -1})