DWave
BF - Brute Force Solver
Description
The Brute Force Solver utilizes a conventional brute force approach to search through all possible solutions. The solver becomes very slow for problems with more than 17 variables.
Available
Parameters
QPU Provider
DS - Dialectic Search Solver
Description
The Dialectic Search Solver uses a path search between two states representing the thesis and antithesis. A greedy search is used to reduce the energy by applying bit flips in an attempt to find the solution.
Parameters
decomposer
size: int
Number of selected variables for the graph search to stop. Default is 10.
min_gain: int, optional
Minimum gain to consider. If None, no minimum gain is enforced.
rolling: bool
Whether to use a rolling window for the decomposer. Default is True.
rolling_history: int
The size of the rolling window. Default is 1.
silent_rewind: bool
Whether to silently rewind the decomposer. Default is True.
traversal: str
The traversal strategy for the decomposer. Default is "energy".
tabu_antithesis
num_reads: int, optional
Number of reads for the tabu search. If None, no limit is enforced.
tenure: int, optional
Tenure for the tabu search. If None, no tenure is enforced.
timeout: int
Timeout for the tabu search in seconds. Default is 20.
initial_states_generator: str
The initial states generator for the tabu search. Default is "random".
tabu_synthesis
num_reads: int, optional
Number of reads for the tabu search. If None, no limit is enforced.
tenure: int, optional
Tenure for the tabu search. If None, no tenure is enforced.
timeout: int
Timeout for the tabu search in seconds. Default is 20.
initial_states_generator: str
The initial states generator for the tabu search. Default is "random".
update: dict
max_iter: int
Maximum number of iterations. Default is 100.
max_tries: int
Maximum number of iterations with unchanged output to terminate algorithm. Default is 100.
max_time: int
Time in seconds after which the algorithm will stop. Default is 5.
target: float, optional
Energy level that the algorithm tries to reach. If None (default), the algorithm will run until any other stopping criterion kicks in.
Available
Parameters
{
"decomposer": {
"size": 10,
"min_gain": None,
"rolling": True,
"rolling_history": 1,
"silent_rewind": True,
"traversal": "energy"
},
"tabu_antithesis": {
"num_reads": None,
"tenure": None,
"timeout": 20,
"initial_states_generator": "random"
},
"tabu_synthesis": {
"num_reads": None,
"tenure": None,
"timeout": 20,
"initial_states_generator": "random"
},
"update": {
"max_iter": 100,
"max_tries": 100,
"max_time": 5
},
"target": None
}
QPU Provider
K - Kerberos
Description
Kerberos divides the problem into subproblems and solves them using Tabu Search, Simulated Annealing and QPU Subproblem Sampling. These algorithms are executed in parallel and afterwards the best solutions are combined. This procedure is applied iteratively until the best solution is found or a termination criterion is met.
Parameters
TABU
num_reads: int, optional
Number of reads for the tabu search. If None, no limit is enforced. Default is 1.
max_time: int, optional
Maximum time for the tabu search. If None, no limit is enforced.
simulated_annealing
num_reads: int, optional
Number of reads for the simulated annealing. If None, no limit is enforced.
num_sweeps: int
Number of sweeps for the simulated annealing. Default is 1000.
beta_range: Any, optional
Beta range for the simulated annealing. If None, no specific range is enforced.
beta_schedule_type: str
Beta schedule type for the simulated annealing. Default is "geometric".
initial_states_generator: str
The initial states generator for the simulated annealing. Default is "random".
decomposer
size: int
Number of selected variables for the graph search to stop. Default is 10.
min_gain: int, optional
Minimum gain to consider. If None, no minimum gain is enforced.
rolling: bool
Whether to use a rolling window for the decomposer. Default is True.
rolling_history: int
The size of the rolling window. Default is 1.
silent_rewind: bool
Whether to silently rewind the decomposer. Default is True.
traversal: str
The traversal strategy for the decomposer. Default is "energy".
qpu
num_reads: int
Number of reads for the QPU. Default is 100.
num_retries: int
Number of retries for the QPU. Default is 0.
auto_embedding_params: AutoEmbeddingParamsDict
Parameters for the auto embedding in the QPU.
sampling_params
Parameters for the sampling in the QPU.
max_iter: int
Maximum number of iterations. Default is 100.
max_time: int
Time in seconds after which the algorithm will stop. Default is 5.
convergence: int
Number of iterations with unchanged output to terminate algorithm. Default is 3.
target: float, optional
Energy level that the algorithm tries to reach. If None (default), the algorithm will run until any other stopping criterion kicks in.
cpu_count_multiplier: int
Multiplier for the CPU count. Default is 1.
rtol: float
Relative tolerance for convergence.
atol: float
Absolute tolerance for convergence.
Available
Parameters
{
"TABU": {
"num_reads": 1,
"tenure": None,
"timeout": 20,
"initial_states_generator": "random",
"max_time": None
},
"simulated_annealing": {
"num_reads": None,
"num_sweeps": 1000,
"beta_range": None,
"beta_schedule_type": "geometric",
"initial_states_generator": "random"
},
"decomposer": {
"size": 10,
"min_gain": None,
"rolling": True,
"rolling_history": 1,
"silent_rewind": True,
"traversal": "energy"
},
"qpu": {
"num_reads": 100,
"num_retries": 0,
"auto_embedding_params": {
"embedding_parameters": {
"max_no_improvement": 10,
"random_seed": None,
"timeout": 1000,
"max_beta": None,
"tries": 10,
"inner_rounds": None,
"chainlength_patience": 10,
"max_fill": None,
"threads": 1,
"return_overlap": False,
"skip_initialization": False,
"initial_chains": [],
"fixed_chains": [],
"restrict_chains": [],
"suspend_chains": []
}
},
"sampling_params": {
"anneal_offsets": None,
"anneal_schedule": None,
"annealing_time": None,
"auto_scale": None,
"flux_biases": None,
"flux_drift_compensation": True,
"h_gain_schedule": None,
"initial_state": None,
"max_answers": None,
"num_reads": 1,
"programming_thermalization": None,
"readout_thermalization": None,
"reduce_intersample_correlation": False,
"reinitialize_state": True
}
},
"max_iter": 100,
"max_time": 5,
"convergence": 3,
"target": None,
"cpu_count_multiplier": 1,
"rtol": 1e-05,
"atol": 1e-08
}
QPU Provider
dwave
LBQM - Leap Hybrid BQM
Description
Leap's quantum-classical hybrid solvers are intended to solve arbitrary application problems formulated as quadratic models. This solver accepts arbitrarily structured, unconstrained problems formulated as BQMs, with any constraints typically represented through penalty models.
Parameters
time_limit: Any, optional
The time limit for the solver.
Available
Parameters
{
"time_limit": None
}
QPU Provider
dwave
LCQM - Leap Hybrid CQM
Description
Leap's quantum-classical hybrid solvers are intended to solve arbitrary application problems formulated as quadratic models. This solver accepts arbitrarily structured problems formulated as CQMs, with any constraints represented natively.
Parameters
time_limit: Any, optional
The time limit for the solver.
Available
Parameters
{
"time_limit": None
}
QPU Provider
dwave
PTQ - Parallel Tempering QPU
Description
Parallel Tempering uses multiple optimization procedures per temperature. During the cooling process, an exchange of replicas can take place between the parallel procedures, thus enabling higher energy mountains to be overcome.
Parameters
n_replicas: int
Number of replicas for the parallel tempering. Default is 4.
decomposer
size: int
Number of selected variables for the graph search to stop. Default is 10.
min_gain: int, optional
Minimum gain to consider. If None, no minimum gain is enforced.
rolling: bool
Whether to use a rolling window for the decomposer. Default is False.
qpu
anneal_offsets: Any, optional
Anneal offsets for the QPU. If None, no offsets are applied.
anneal_schedule: Any, optional
Anneal schedule for the QPU. If None, a default schedule is used.
annealing_time: Any, optional
Annealing time for the QPU. If None, a default time is used.
auto_scale: Any, optional
Whether to auto scale the QPU. If None, auto scaling is not used.
flux_biases: Any, optional
Flux biases for the QPU. If None, no biases are applied.
flux_drift_compensation: bool
Whether to use flux drift compensation. Default is True.
h_gain_schedule: Any, optional
H gain schedule for the QPU. If None, a default schedule is used.
initial_state: Any, optional
Initial state for the QPU. If None, a default state is used.
max_answers: Any, optional
Maximum number of answers for the QPU. If None, no limit is enforced.
num_reads: int
Number of reads for the QPU. Default is 1.
programming_thermalization: Any, optional
Programming thermalization for the QPU. If None, a default thermalization is used.
readout_thermalization: Any, optional
Readout thermalization for the QPU. If None, a default thermalization is used.
reduce_intersample_correlation: bool
Whether to reduce intersample correlation. Default is False.
reinitialize_state: bool
Whether to reinitialize the state of the QPU. Default is True.
fixed_temperature_sampler
num_sweeps: int
Number of sweeps for the fixed temperature sampler. Default is 10,000.
num_reads: int, optional
Number of reads for the fixed temperature sampler. If None, no limit is enforced.
max_iter: int
Maximum number of iterations. Default is 5.
max_time: int
Time in seconds after which the algorithm will stop. Default is 5.
target: Any, optional
Energy level that the algorithm tries to reach. If None (default), the algorithm will run until any other stopping criterion kicks in.
cpu_count_multiplier: int
Multiplier for the CPU count. Default is 5.
rtol: float
Relative tolerance for convergence. Default is 1.0e-5.
atol: float
Absolute tolerance for convergence. Default is 1.0e-8.
Available
Parameters
{
"n_replicas": 4,
"decomposer": {
"size": 10,
"min_gain": None,
"rolling": True,
"rolling_history": 1,
"silent_rewind": True,
"traversal": "energy"
},
"qpu": {
"num_reads": 100,
"num_retries": 0,
"auto_embedding_params": {
"embedding_parameters": {
"max_no_improvement": 10,
"random_seed": None,
"timeout": 1000,
"max_beta": None,
"tries": 10,
"inner_rounds": None,
"chainlength_patience": 10,
"max_fill": None,
"threads": 1,
"return_overlap": False,
"skip_initialization": False,
"initial_chains": [],
"fixed_chains": [],
"restrict_chains": [],
"suspend_chains": []
}
},
"sampling_params": {
"anneal_offsets": None,
"anneal_schedule": None,
"annealing_time": None,
"auto_scale": None,
"flux_biases": None,
"flux_drift_compensation": True,
"h_gain_schedule": None,
"initial_state": None,
"max_answers": None,
"num_reads": 1,
"programming_thermalization": None,
"readout_thermalization": None,
"reduce_intersample_correlation": False,
"reinitialize_state": True
}
},
"fixed_temperature_sampler": {
"num_sweeps": 10000,
"num_reads": None
},
"max_iter": 5,
"max_time": 5,
"target": None,
"cpu_count_multiplier": 5,
"rtol": 1e-05,
"atol": 1e-08
}
QPU Provider
dwave
PT - Parallel Tempering
Description
Parallel Tempering uses multiple optimization procedures per temperature. During the cooling process, an exchange of replicas can take place between the parallel procedures, thus enabling higher energy mountains to be overcome.
Parameters
n_replicas: int
Number of replicas for the parallel tempering. Default is 2.
fixed_temperature_sampler
Parameters for the fixed temperature sampler.
num_sweeps: int
Number of sweeps for the fixed temperature sampler. Default is 10,000.
num_reads: int, optional
Number of reads for the fixed temperature sampler. If None, no limit is enforced.
max_iter: int
Maximum number of iterations. Default is 3.
max_time: int
Time in seconds after which the algorithm will stop. Default is 5.
target: Any, optional
Energy level that the algorithm tries to reach. If None (default), the algorithm will run until any other stopping criterion kicks in.
cpu_count_multiplier: int
Multiplier for the CPU count. Default is 5.
rtol: float
Relative tolerance for convergence. Default is 1e-05.
atol: float
Absolute tolerance for convergence. Default is 1e-08.
random_swaps_factor: int
Factor for random swaps. Default is 1.
Available
Parameters
{
"n_replicas": 2,
"fixed_temperature_sampler": {
"num_sweeps": 10000,
"num_reads": None
},
"max_iter": 3,
"max_time": 5,
"target": None,
"cpu_count_multiplier": 5,
"rtol": 1e-05,
"atol": 1e-08,
"random_swaps_factor": 1
}
QPU Provider
PAQ - Population Annealing QPU
Description
Population Annealing uses a sequential Monte Carlo method to minimize the energy of a population. The population consists of walkers that can explore their neighborhood during the cooling process. Afterwards, walkers are removed and duplicated using bias to lower energy. Eventually, a population collapse occurs where all walkers are in the lowest energy state.
Parameters
decomposer
Parameters for the decomposer, which selects variables for the graph search to stop.
size: int
Number of selected variables for the graph search to stop. Default is 10.
min_gain: int, optional
Minimum gain to consider. If None, no minimum gain is enforced.
rolling: bool
Whether to use a rolling window for the decomposer. Default is True.
rolling_history: int
History size for the rolling window. Default is 1.
silent_rewind: bool
Whether to silently rewind the decomposer. Default is True.
traversal: str
Traversal method for the decomposer. Default is "energy".
qpu
Parameters for the Quantum Processing Unit (QPU).
num_reads: int
Number of reads for the QPU. Default is 100.
num_retries: int
Number of retries for the QPU. Default is 0.
auto_embedding_params: AutoEmbeddingParamsDict
Parameters for the auto embedding.
embedding_parameters
Parameters for the embedding.
max_no_improvement: int
Maximum number of iterations without improvement. Default is 10.
random_seed: int, optional
Random seed for the embedding. If None, a random seed is not used.
timeout: int
Timeout for the embedding. Default is 1000.
max_beta: int, optional
Maximum beta for the embedding. If None, a maximum beta is not used.
tries: int
Number of tries for the embedding. Default is 10.
inner_rounds: int, optional
Number of inner rounds for the embedding. If None, a default number is used.
chainlength_patience: int
Patience for chain length in the embedding. Default is 10.
max_fill: int, optional
Maximum fill for the embedding. If None, a maximum fill is not used.
threads: int
Number of threads for the embedding. Default is 1.
return_overlap: bool
Whether to return overlap in the embedding. Default is False.
skip_initialization: bool
Whether to skip initialization in the embedding. Default is False.
initial_chains: Any, optional
Initial chains for the embedding. If None, initial chains are not used.
fixed_chains: Any, optional
Fixed chains for the embedding. If None, fixed chains are not used.
restrict_chains: Any, optional
Restricted chains for the embedding. If None, restricted chains are not used.
suspend_chains: Any, optional
Suspended chains for the embedding. If None, suspended chains are not used.
sampling_params
Parameters for the sampling.
anneal_offsets: Any, optional
Anneal offsets for the QPU. If None, no offsets are applied.
anneal_schedule: Any, optional
Anneal schedule for the QPU. If None, a default schedule is used.
annealing_time: Any, optional
Annealing time for the QPU. If None, a default time is used.
auto_scale: Any, optional
Whether to auto scale the QPU. If None, auto scaling is not used.
flux_biases: Any, optional
Flux biases for the QPU. If None, no biases are applied.
flux_drift_compensation: bool
Whether to use flux drift compensation. Default is True.
h_gain_schedule: Any, optional
H gain schedule for the QPU. If None, a default schedule is used.
initial_state: Any, optional
Initial state for the QPU. If None, a default state is used.
max_answers: Any, optional
Maximum number of answers for the QPU. If None, no limit is enforced.
num_reads: int
Number of reads for the QPU. Default is 1.
programming_thermalization: Any, optional
Programming thermalization for the QPU. If None, a default thermalization is used.
readout_thermalization: Any, optional
Readout thermalization for the QPU. If None, a default thermalization is used.
reduce_intersample_correlation: bool
Whether to reduce intersample correlation. Default is False.
reinitialize_state: bool
Whether to reinitialize the state of the QPU. Default is True.
fixed_temperature_sampler
Parameters for the fixed temperature sampler.
num_sweeps: int
Number of sweeps for the fixed temperature sampler. Default is 10,000.
num_reads: int, optional
Number of reads for the fixed temperature sampler. If None, no limit is enforced.
max_iter: int
Maximum number of iterations. Default is 20.
target: Any, optional
Energy level that the algorithm tries to reach. If None (default), the algorithm will run until any other stopping criterion kicks in.
timeout: int
Time in seconds after which the algorithm will stop if it hasn't already. Default is 5.
max_time: int
Maximum time in seconds that the algorithm is allowed to run. Default is 2.
Available
Parameters
{
"decomposer": {
"size": 10,
"min_gain": None,
"rolling": True,
"rolling_history": 1,
"silent_rewind": True,
"traversal": "energy"
},
"qpu": {
"num_reads": 100,
"num_retries": 0,
"auto_embedding_params": {
"embedding_parameters": {
"max_no_improvement": 10,
"random_seed": None,
"timeout": 1000,
"max_beta": None,
"tries": 10,
"inner_rounds": None,
"chainlength_patience": 10,
"max_fill": None,
"threads": 1,
"return_overlap": False,
"skip_initialization": False,
"initial_chains": [],
"fixed_chains": [],
"restrict_chains": [],
"suspend_chains": []
}
},
"sampling_params": {
"anneal_offsets": None,
"anneal_schedule": None,
"annealing_time": None,
"auto_scale": None,
"flux_biases": None,
"flux_drift_compensation": True,
"h_gain_schedule": None,
"initial_state": None,
"max_answers": None,
"num_reads": 1,
"programming_thermalization": None,
"readout_thermalization": None,
"reduce_intersample_correlation": False,
"reinitialize_state": True
}
},
"fixed_temperature_sampler": {
"num_sweeps": 10000,
"num_reads": None
},
"max_iter": 20,
"target": None,
"timeout": 5,
"max_time": 2
}
QPU Provider
dwave
PA - Population Annealing
Description
Population Annealing uses a sequential Monte Carlo method to minimize the energy of a population. The population consists of walkers that can explore their neighborhood during the cooling process. Afterwards, walkers are removed and duplicated using bias to lower energy. Eventually, a population collapse occurs where all walkers are in the lowest energy state.
Parameters
fixed_temperature_sampler
num_sweeps: int
Number of sweeps for the fixed temperature sampler. Default is 10,000.
num_reads: int, optional
Number of reads for the fixed temperature sampler. If None, no limit is enforced.
max_iter: int
Maximum number of iterations. Default is 20.
target: Any, optional
Energy level that the algorithm tries to reach. If None (default), the algorithm will run until any other stopping criterion kicks in.
max_time: int
Maximum time in seconds that the algorithm is allowed to run. Default is 2.
Available
Parameters
{
"fixed_temperature_sampler": {
"num_sweeps": 10000,
"num_reads": None
},
"max_iter": 20,
"target": None,
"max_time": 2
}
QPU Provider
QLQ - QBSolv Like Simulated Annealing QPU
Description
QBSolv Like Simulated Annealing breaks down the problem and solves the parts individually using a classic solver that uses Simulated Annealing. This particular implementation uses hybrid.SimulatedAnnealingSubproblemSampler (https://docs.ocean.dwavesys.com/projects/hybrid/en/stable/reference/ samplers.html#simulatedannealingsubproblemsampler) as a sampler for the subproblems to achieve a QBSolv like behaviour.
Parameters
qbsolv_like
Parameters for the QBSOLV-like solver.
decomposer_size: int
Size of the decomposer. Default is 50.
rolling: bool
Whether to use a rolling history. Default is True.
rolling_history: float
The size of the rolling history. Default is 0.15.
max_iter: int
Maximum number of iterations. Default is 10.
max_time: int
Maximum time for the solver. Default is 5.
convergence: int
Convergence criteria. Default is 3.
target: Any, optional
The target for the solver.
cpu_count_multiplier: int
Multiplier for the CPU count. Default is 1.
rtol: float
Relative tolerance. Default is 1.0e-5.
atol: float
Absolute tolerance. Default is 1.0e-8.
qpu
Parameters for the QPU.
num_reads: int
Number of reads for the QPU. Default is 100.
num_retries: int
Number of retries for the QPU. Default is 0.
auto_embedding_params
Parameters for the auto embedding.
sampling_params
Parameters for the sampling.
Available
Parameters
{
"qbsolv_like": {
"decomposer_size": 50,
"rolling": True,
"rolling_history": 0.15,
"max_iter": 10,
"max_time": 5,
"convergence": 3,
"target": None,
"cpu_count_multiplier": 1,
"rtol": 1e-05,
"atol": 1e-08
},
"qpu": {
"num_reads": 100,
"num_retries": 0,
"auto_embedding_params": {
"embedding_parameters": {
"max_no_improvement": 10,
"random_seed": None,
"timeout": 1000,
"max_beta": None,
"tries": 10,
"inner_rounds": None,
"chainlength_patience": 10,
"max_fill": None,
"threads": 1,
"return_overlap": False,
"skip_initialization": False,
"initial_chains": [],
"fixed_chains": [],
"restrict_chains": [],
"suspend_chains": []
}
},
"sampling_params": {
"anneal_offsets": None,
"anneal_schedule": None,
"annealing_time": None,
"auto_scale": None,
"flux_biases": None,
"flux_drift_compensation": True,
"h_gain_schedule": None,
"initial_state": None,
"max_answers": None,
"num_reads": 1,
"programming_thermalization": None,
"readout_thermalization": None,
"reduce_intersample_correlation": False,
"reinitialize_state": True
}
}
}
QPU Provider
dwave
QLSA - QBSolv Like Simulated Annealing
Description
QBSolv Like Simulated Annealing breaks down the problem and solves the parts individually using a classic solver that uses Simulated Annealing. This particular implementation uses hybrid.SimulatedAnnealingSubproblemSampler (https://docs.ocean.dwavesys.com/projects/hybrid/en/stable/reference/ samplers.html#simulatedannealingsubproblemsampler) as a sampler for the subproblems to achieve a QBSolv like behaviour.
Parameters
qbsolv_like
Parameters for the QBSOLV-like solver.
decomposer_size: int
Size of the decomposer. Default is 50.
rolling: bool
Whether to use a rolling history. Default is True.
rolling_history: float
The size of the rolling history. Default is 0.15.
max_iter: int
Maximum number of iterations. Default is 10.
max_time: int
Maximum time for the solver. Default is 5.
convergence: int
Convergence criteria. Default is 3.
target: Any, optional
The target for the solver.
cpu_count_multiplier: int
Multiplier for the CPU count. Default is 1.
rtol: float
Relative tolerance. Default is DEFAULT_RTOL.
atol: float
Absolute tolerance. Default is DEFAULT_ATOL.
simulated_annealing
Parameters for the Simulated Annealing.
num_reads: int, optional
Number of reads for the Simulated Annealing.
num_sweeps: int
Number of sweeps for the Simulated Annealing. Default is 1000.
beta_range: Any, optional
Beta range for the Simulated Annealing.
beta_schedule_type: str
Beta schedule type for the Simulated Annealing. Default is "geometric".
initial_states_generator: str
Initial states generator for the Simulated Annealing. Default is "random".
Available
Parameters
{
"qbsolv_like": {
"decomposer_size": 50,
"rolling": True,
"rolling_history": 0.15,
"max_iter": 10,
"max_time": 5,
"convergence": 3,
"target": None,
"cpu_count_multiplier": 1,
"rtol": 1e-05,
"atol": 1e-08
},
"simulated_annealing": {
"num_reads": None,
"num_sweeps": 1000,
"beta_range": None,
"beta_schedule_type": "geometric",
"initial_states_generator": "random"
}
}
QPU Provider
QLTS - QBSolv Like Tabu Search
Description
QBSolv Like Tabu Search breaks down the problem and solves the parts individually using a classic solver that uses Tabu Search. This particular implementation uses hybrid.TabuSubproblemSampler (https://docs.ocean.dwavesys.com/projects/hybrid/en/stable/reference/ samplers.html#tabusubproblemsampler) as a sampler for the subproblems to achieve a QBSolv like behaviour.
Parameters
qbsolv_like Parameters for the QBSOLV-like solver.
decomposer_size: int
Size of the decomposer. Default is 50.
rolling: bool
Whether to use a rolling history. Default is True.
rolling_history: float
The size of the rolling history. Default is 0.15.
max_iter: int
Maximum number of iterations. Default is 10.
max_time: int
Maximum time for the solver. Default is 5.
convergence: int
Convergence criteria. Default is 3.
target: Any, optional
The target for the solver.
cpu_count_multiplier: int
Multiplier for the CPU count. Default is 1.
rtol: float
Relative tolerance. Default is DEFAULT_RTOL.
atol: float
Absolute tolerance. Default is DEFAULT_ATOL.
tabu_search Parameters for the Tabu Search.
timeout: int
Timeout for the Tabu Search. Default is 100.
Available
Parameters
{
"qbsolv_like": {
"decomposer_size": 50,
"rolling": True,
"rolling_history": 0.15,
"max_iter": 10,
"max_time": 5,
"convergence": 3,
"target": None,
"cpu_count_multiplier": 1,
"rtol": 1e-05,
"atol": 1e-08
},
"tabu_search": {
"num_reads": None,
"tenure": None,
"timeout": 100,
"initial_states_generator": "random"
}
}
QPU Provider
QA - QuantumAnnealing
Description
The QuantumAnnealing uses a minor embedding to map problems onto a specified D-Wave sampler. The sampler solves the problem with the specified sampler method. With each call of the sampling method, the minor embedding is recalculated.
Parameters
auto_embedding_params Parameters for the auto embedding.
chain_strength: Any, optional
The chain strength for the embedding.
chain_break_method: Any, optional
The method to use for chain breaks.
chain_break_fraction: bool
Whether to use chain break fraction. Default is True.
embedding_parameters
Parameters for the embedding.
max_no_improvement: int
Maximum number of iterations without improvement. Default is 10.
random_seed: int, optional
Random seed for the embedding.
timeout: int
Timeout for the embedding. Default is 1000.
max_beta: int, optional
Maximum beta for the embedding.
tries: int
Number of tries for the embedding. Default is 10.
inner_rounds: int, optional
Number of inner rounds for the embedding.
chainlength_patience: int
Patience for chain length in the embedding. Default is 10.
max_fill: int, optional
Maximum fill for the embedding.
threads: int
Number of threads for the embedding. Default is 1.
return_overlap: bool
Whether to return overlap. Default is False.
skip_initialization: bool
Whether to skip initialization. Default is False.
initial_chains: Any
Initial chains for the embedding.
fixed_chains: Any
Fixed chains for the embedding.
restrict_chains: Any
Restricted chains for the embedding.
suspend_chains: Any
Suspended chains for the embedding.
return_embedding: Any, optional
Whether to return the embedding.
sampling_params_dict
Parameters for the sampling.
anneal_offsets: Any, optional
Anneal offsets for the sampling.
anneal_schedule: Any, optional
Anneal schedule for the sampling.
annealing_time: Any, optional
Annealing time for the sampling.
auto_scale: Any, optional
Whether to auto scale for the sampling.
flux_biases: Any, optional
Flux biases for the sampling.
flux_drift_compensation: bool
Whether to use flux drift compensation. Default is True.
h_gain_schedule: Any, optional
H gain schedule for the sampling.
initial_state: Any, optional
Initial state for the sampling.
max_answers: Any, optional
Maximum number of answers for the sampling.
num_reads: int
Number of reads for the sampling. Default is 1.
programming_thermalization: Any, optional
Programming thermalization for the sampling.
readout_thermalization: Any, optional
Readout thermalization for the sampling.
reduce_intersample_correlation: bool
Whether to reduce intersample correlation. Default is False.
reinitialize_state: bool
Whether to reinitialize state. Default is True.
Available
Parameters
{
"embedding": {
"chain_strength": None,
"chain_break_method": None,
"chain_break_fraction": True,
"embedding_parameters": {
"max_no_improvement": 10,
"random_seed": None,
"timeout": 1000,
"max_beta": None,
"tries": 10,
"inner_rounds": None,
"chainlength_patience": 10,
"max_fill": None,
"threads": 1,
"return_overlap": False,
"skip_initialization": False,
"initial_chains": [],
"fixed_chains": [],
"restrict_chains": [],
"suspend_chains": []
},
"return_embedding": None
},
"sampling_params": {
"anneal_offsets": None,
"anneal_schedule": None,
"annealing_time": None,
"auto_scale": None,
"flux_biases": None,
"flux_drift_compensation": True,
"h_gain_schedule": None,
"initial_state": None,
"max_answers": None,
"num_reads": 1,
"programming_thermalization": None,
"readout_thermalization": None,
"reduce_intersample_correlation": False,
"reinitialize_state": True
}
}
QPU Provider
dwave
RRQA - Repeated Reverse Quantum Annealing
Description
Repeated Reverse Quantum Annealing begins the annealing process from a previously initialized state and increases the temperature from there. Afterwards, the temperature is decreased again until the solution is found. This procedure is repeated several times with this particular solver. (for additional information see: D-Wave Reverse Annealing)
Parameters
anneal_offsets: Any, optional
Anneal offsets for the sampling.
annealing_time: Any, optional
Annealing time for the sampling.
auto_scale: Any, optional
Whether to auto scale for the sampling.
flux_biases: Any, optional
Flux biases for the sampling.
flux_drift_compensation: bool
Whether to use flux drift compensation. Default is True.
h_gain_schedule: Any, optional
H gain schedule for the sampling.
max_answers: Any, optional
Maximum number of answers for the sampling.
programming_thermalization: Any, optional
Programming thermalization for the sampling.
readout_thermalization: Any, optional
Readout thermalization for the sampling.
reduce_intersample_correlation: bool
Whether to reduce intersample correlation. Default is False.
Available
Parameters
{
"sampling_params": {
"anneal_offsets": None,
"annealing_time": None,
"auto_scale": None,
"flux_biases": None,
"flux_drift_compensation": True,
"h_gain_schedule": None,
"max_answers": None,
"programming_thermalization": None,
"readout_thermalization": None,
"reduce_intersample_correlation": False
},
"initial_states": None,
"num_reads": 1,
"beta_schedule": [
0.5,
3
],
"timeout": 5,
"max_iter": 10,
"target": None,
"check_trivial": True
}
QPU Provider
dwave
RRSA - Repeated Reverse Simulated Annealing
Description
Repeated Reverse Simulated Annealing finds the solution to a problem using an annealing process. Initially, random states are chosen in the solution landscape. Afterwards, as the temperature decreases, states are chosen that are more energetically favorable. At the end of the complete annealing process, the resulting states make up the solution.
Parameters
simulated_annealing
num_sweeps: int, optional Number of sweeps for the simulated annealing.
num_sweeps_per_beta: int
Number of sweeps per beta for the simulated annealing. Default is 1.
interrupt_function: Any, optional
Interrupt function for the simulated annealing.
beta_schedule: Any, optional
Beta schedule for the simulated annealing.
randomize_order: bool
Whether to randomize order for the simulated annealing. Default is False.
proposal_acceptance_criteria: str
Proposal acceptance criteria for the simulated annealing. Default is "Metropolis".
initial_states : Any, optional
Initial states for the solver.
timeout : int
Timeout for the solver. Default is 5.
max_iter : int
Maximum number of iterations for the solver. Default is 10.
target : Any, optional
The target for the solver.
Available
Parameters
{
"simulated_annealing": {
"num_reads": None,
"num_sweeps": None,
"beta_range": None,
"beta_schedule_type": "geometric",
"initial_states_generator": "random",
"num_sweeps_per_beta": 1,
"interrupt_function": None,
"beta_schedule": None,
"randomize_order": False,
"proposal_acceptance_criteria": "Metropolis"
},
"initial_states": None,
"timeout": 5,
"max_iter": 10,
"target": None
}
QPU Provider
dwave
SA - Simulated Annealing
Description
Simulated Annealing is a probabilistic technique for approximating the global optimum of a given function. It performs well on problems where approximate global optima are more desirable than exact local optima. For more details, check the DWave website.
Parameters
num_sweeps_per_beta: int
Number of sweeps per beta for the simulated annealing. Default is 1.
seed: Any, optional
Seed for the simulated annealing.
interrupt_function: Any, optional
Interrupt function for the simulated annealing.
beta_schedule: Any, optional
Beta schedule for the simulated annealing.
initial_states: Any, optional
Initial states for the simulated annealing.
randomize_order: bool
Whether to randomize order for the simulated annealing. Default is False.
proposal_acceptance_criteria: str
Proposal acceptance criteria for the simulated annealing. Default is "Metropolis".
Available
Parameters
{
"num_reads": None,
"num_sweeps": 1000,
"beta_range": None,
"beta_schedule_type": "geometric",
"initial_states_generator": "random",
"num_sweeps_per_beta": 1,
"seed": None,
"interrupt_function": None,
"beta_schedule": None,
"initial_states": None,
"randomize_order": False,
"proposal_acceptance_criteria": "Metropolis"
}
QPU Provider
TS - Tabu Search
Description
Tabu Search is a heuristic optimization method that works with the help of a tabu list. Initially, random states are chosen in the solution landscape. Afterwards, an iterative search for energetically better states in the neighborhood is started from these states. According to a tabu strategy, states are added to the tabu list that are not allowed to be selected as successor states for a tabu duration. The tabu search ends as soon as there are no better successor states in the neighborhood. The resulting state is therefore the solution to the problem.
Parameters
initial_states: Any, optional
Initial states for the solver.
initial_states_generator: str
Initial states generator for the solver. Default is "random".
num_reads: Any, optional
Number of reads for the solver.
seed: Any, optional
Seed for the solver.
tenure: Any, optional
Tenure for the solver.
timeout: int
Timeout for the solver. Default is 20.
num_restarts: int
Number of restarts for the solver. Default is 1_000_000.
energy_threshold: Any, optional
Energy threshold for the solver.
coefficient_z_first: Any, optional
Coefficient z first for the solver.
coefficient_z_restart: Any, optional
Coefficient z restart for the solver.
lower_bound_z: Any, optional
Lower bound z for the solver.
Available
Parameters
{
"initial_states": None,
"initial_states_generator": "random",
"num_reads": None,
"seed": None,
"tenure": None,
"timeout": 20,
"num_restarts": 1000000,
"energy_threshold": None,
"coefficient_z_first": None,
"coefficient_z_restart": None,
"lower_bound_z": None
}