Quantum Annealing

Parameters for the auto embedding.
Default: Embedding(chain_strength=None, chain_break_fraction=True, embedding_parameters=EmbeddingParameters(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=()))

Parameters for the sampling. See https://docs.dwavesys.com/docs/latest/c_solver_parameters.html for more details
Default: SamplingParams(anneal_offsets=None, anneal_schedule=None, annealing_time=None, auto_scale=None, fast_anneal=False, 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=None)

Sets the coupling strength between qubits representing variables that form a chain. Mappings should specify the required chain strength for each variable. By default, chain_strength is calculated with ~dwave.embedding.chain_strength.uniform_torque_compensation.
Default: None

Add a chain_break_fraction field to the unembedded response with the fraction of chains broken before unembedding.
Default: True

Embedding parameters for the embedding.
Default: EmbeddingParameters(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=())

Anneal offsets for the sampling.
Default: None

Anneal schedule for the sampling.
Default: None

Annealing time for the sampling.
Default: None

Whether to auto scale for the sampling.
Default: None

Use the fast-anneal protocol instead of the standard anneal.
Default: False

Flux biases for the sampling.
Default: None

Whether to use flux drift compensation for the sampling.
Default: True

H gain schedule for the sampling.
Default: None

Initial state for the sampling.
Default: None

Maximum number of answers for the sampling. Min: 1
Default: None

Number of reads for the sampling. Min: 1
Default: 1

Programming thermalization for the sampling. Has to be positive.
Default: None

Readout thermalization for the sampling. Has to be positive.
Default: None

Whether to reduce intersample correlation for the sampling.
Default: False

Whether to reinitialize state for the sampling. Should be None if initial_state is None.
Default: None

Maximum number of failed iterations to improve the current solution, where each iteration attempts to find an embedding for each variable of S such that it is adjacent to all its neighbours.
Default: 10

Seed for the random number generator. If None, seed is set by os.urandom().
Default: None

Algorithm gives up after timeout seconds.
Default: 1000

Qubits are assigned weight according to a formula (beta^n) where n is the number of chains containing that qubit. This value should never be less than or equal to 1. If None, max_beta is effectively infinite.
Default: None

Number of restart attempts before the algorithm stops. On D-WAVE 2000Q, a typical restart takes between 1 and 60 seconds.
Default: 10

The algorithm takes at most this many iterations between restart attempts; restart attempts are typically terminated due to max_no_improvement. If None, inner_rounds is effectively infinite.
Default: None

Maximum number of failed iterations to improve chain lengths in the current solution, where each iteration attempts to find an embedding for each variable of S such that it is adjacent to all its neighbours.
Default: 10

Restricts the number of chains that can simultaneously incorporate the same qubit during the search. Values above 63 are treated as 63. If None, max_fill is effectively infinite.
Default: None

Maximum number of threads to use. Note that the parallelization is only advantageous where the expected degree of variables is significantly greater than the number of threads. Min: 1.
Default: 1

This function returns an embedding, regardless of whether or not qubits are used by multiple variables. return_overlap determines the function's return value. If True, a 2-tuple is returned, in which the first element is the embedding and the second element is a bool representing the embedding validity. If False, only an embedding is returned.
Default: False

Skip the initialization pass. Note that this only works if the chains passed in through initial_chains and fixed_chains are semi-valid. A semi-valid embedding is a collection of chains such that every adjacent pair of variables (u,v) has a coupler (p,q) in the hardware graph where p is in chain(u) and q is in chain(v). This can be used on a valid embedding to immediately skip to the chain length improvement phase. Another good source of semi-valid embeddings is the output of this function with the return_overlap parameter enabled.
Default: False

Initial chains inserted into an embedding before fixed_chains are placed, which occurs before the initialization pass. These can be used to restart the algorithm in a similar state to a previous embedding; for example, to improve chain length of a valid embedding or to reduce overlap in a semi-valid embedding (see skip_initialization) previously returned by the algorithm. Missing or empty entries are ignored. Each value in the dictionary is a list of qubit labels.
Default: ()

Fixed chains inserted into an embedding before the initialization pass. As the algorithm proceeds, these chains are not allowed to change, and the qubits used by these chains are not used by other chains. Missing or empty entries are ignored. Each value in the dictionary is a list of qubit labels.
Default: ()

Throughout the algorithm, it is guaranteed that chain[i] is a subset of restrict_chains[i] for each i, except those with missing or empty entries. Each value in the dictionary is a list of qubit labels.
Default: ()

This is a metafeature that is only implemented in the Python interface. suspend_chains[i] is an iterable of iterables; for example, suspend_chains[i] = [blob_1, blob_2], with each blob_j an iterable of target node labels. This enforces the following: for each suspended variable i, for each blob_j in the suspension of i, at least one qubit from blob_j will be contained in the chain for iminorminer accomplishes this through the following problem transformation for each iterable blob_j in suspend_chains[i], Add an auxiliary node Zij to both source and target graphs Set fixed_chains[Zij] = [Zij] Add the edge (i,Zij) to the source graph Add the edges (q,Zij) to the target graph for each q in blob_j
Default: ()

The time limit for the solver.
Default: None

The time limit for the solver.
Default: None

The list of spin variables.
Default: None

Parameters for the RRQuantumAnnealingSamplingParams. See https://docs.dwavesys.com/docs/latest/c_solver_parameters.html for more details.
Default: RRQuantumAnnealingSamplingParams(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 for the solver. For each list element state, one call to the sampler with the parameter initial_state=state will be made in the first iteration.
Default: None

Number of initial states to create when initial_states is None. If initial_states is not None, this parameter will be ignored. Min: 1
Default: 1

How many samples to create per state in each iteration after the first. Min: 1
Default: 1

Beta schedule for the solver.
Default: [0.5, 3]

Timeout for the solver.
Default: 300

Maximum number of iterations for the solver.
Default: 10

The target energy for the solving process.
Default: None

Whether to check for trivial variables. Checking for trivial variables means an overhead. On the other hand, when set to False, trivial variables, i.e., variables without interactions, will lead to a runtime error.
Default: True

Anneal offsets for the sampling.
Default: None

Annealing time for the sampling.
Default: None

Whether to auto scale for the sampling.
Default: None

Flux biases for the sampling.
Default: None

Whether to use flux drift compensation.
Default: True

H gain schedule for the sampling.
Default: None

Maximum number of answers for the sampling. Min: 1
Default: None

Programming thermalization for the sampling. Has to be positive.
Default: None

Readout thermalization for the sampling. Has to be positive.
Default: None

Whether to reduce intersample correlation for the sampling.
Default: False

Parameters for the decomposer, which selects variables for the graph search to stop.
Default: Decomposer(size=10, min_gain=None, rolling=True, rolling_history=1.0, silent_rewind=True, traversal='energy')

Parameters for the Quantum Processing Unit (QPU).
Default: Qpu(num_reads=100, num_retries=0, sampling_params=SamplingParams(anneal_offsets=None, anneal_schedule=None, annealing_time=None, auto_scale=None, fast_anneal=False, 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=None), auto_embedding_params=AutoEmbeddingParams(embedding_parameters=EmbeddingParameters(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=())))

Parameters for the fixed temperature sampler.
Default: FixedTemperatureSampler(num_sweeps=10000, num_reads=None)

Maximum number of iterations.
Default: 20

Maximum time in seconds that the algorithm is allowed to run.
Default: 2

Nominal number of variables in the subproblem. Actual subproblem can be smaller, depending on other parameters (e.g. min_gain).
Default: 10

Minimum reduction required to BQM energy, given the current sample. A variable is included in the subproblem only if inverting its sample value reduces energy by at least this amount.
Default: None

If True, successive calls for the same problem (with possibly different samples) produce subproblems on different variables, selected by rolling down the list of all variables sorted by decreasing impact.
Default: True

Fraction of the problem size, as a float in range 0.0 to 1.0, that should participate in the rolling selection. Once reached, subproblem unrolling is reset. Min: 0.0, Max: 1.0
Default: 1.0

If False, raises :exc:EndOfStream when resetting/rewinding the subproblem generator upon the reset condition for unrolling.
Default: True

Traversal algorithm used to pick a subproblem of size variables. Options are: energy: Use the next size variables in the list of variables ordered by descending energy impact. bfs: Breadth-first traversal seeded by the next variable in the energy impact list. pfs: Priority-first traversal seeded by variables from the energy impact list, proceeding with the variable on the search boundary that has the highest energy impact.
Default: "energy"

Number of states (output solutions) to read from the sampler.
Default: 100

Number of times the sampler will retry to embed if a failure occurs.
Default: 0

Sampling parameters for the QPU. See https://docs.dwavesys.com/docs/latest/c_solver_parameters.html#anneal-offsets for more details
Default: SamplingParams(anneal_offsets=None, anneal_schedule=None, annealing_time=None, auto_scale=None, fast_anneal=False, 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=None)

Auto embedding parameters for the QPU. See https://docs.ocean.dwavesys.com/projects/system/en/stable/reference/generated/minorminer.find_embedding.html for more details.
Default: AutoEmbeddingParams(embedding_parameters=EmbeddingParameters(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=()))

Number of sweeps for the sampler.
Default: 10000

Number of reads for the sampler.
Default: None

Anneal offsets for the sampling.
Default: None

Anneal schedule for the sampling.
Default: None

Annealing time for the sampling.
Default: None

Whether to auto scale for the sampling.
Default: None

Use the fast-anneal protocol instead of the standard anneal.
Default: False

Flux biases for the sampling.
Default: None

Whether to use flux drift compensation for the sampling.
Default: True

H gain schedule for the sampling.
Default: None

Initial state for the sampling.
Default: None

Maximum number of answers for the sampling. Min: 1
Default: None

Number of reads for the sampling. Min: 1
Default: 1

Programming thermalization for the sampling. Has to be positive.
Default: None

Readout thermalization for the sampling. Has to be positive.
Default: None

Whether to reduce intersample correlation for the sampling.
Default: False

Whether to reinitialize state for the sampling. Should be None if initial_state is None.
Default: None

Embedding parameters for the auto embedding.
Default: EmbeddingParameters(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=())

Maximum number of failed iterations to improve the current solution, where each iteration attempts to find an embedding for each variable of S such that it is adjacent to all its neighbours.
Default: 10

Seed for the random number generator. If None, seed is set by os.urandom().
Default: None

Algorithm gives up after timeout seconds.
Default: 1000

Qubits are assigned weight according to a formula (beta^n) where n is the number of chains containing that qubit. This value should never be less than or equal to 1. If None, max_beta is effectively infinite.
Default: None

Number of restart attempts before the algorithm stops. On D-WAVE 2000Q, a typical restart takes between 1 and 60 seconds.
Default: 10

The algorithm takes at most this many iterations between restart attempts; restart attempts are typically terminated due to max_no_improvement. If None, inner_rounds is effectively infinite.
Default: None

Maximum number of failed iterations to improve chain lengths in the current solution, where each iteration attempts to find an embedding for each variable of S such that it is adjacent to all its neighbours.
Default: 10

Restricts the number of chains that can simultaneously incorporate the same qubit during the search. Values above 63 are treated as 63. If None, max_fill is effectively infinite.
Default: None

Maximum number of threads to use. Note that the parallelization is only advantageous where the expected degree of variables is significantly greater than the number of threads. Min: 1.
Default: 1

This function returns an embedding, regardless of whether or not qubits are used by multiple variables. return_overlap determines the function's return value. If True, a 2-tuple is returned, in which the first element is the embedding and the second element is a bool representing the embedding validity. If False, only an embedding is returned.
Default: False

Skip the initialization pass. Note that this only works if the chains passed in through initial_chains and fixed_chains are semi-valid. A semi-valid embedding is a collection of chains such that every adjacent pair of variables (u,v) has a coupler (p,q) in the hardware graph where p is in chain(u) and q is in chain(v). This can be used on a valid embedding to immediately skip to the chain length improvement phase. Another good source of semi-valid embeddings is the output of this function with the return_overlap parameter enabled.
Default: False

Initial chains inserted into an embedding before fixed_chains are placed, which occurs before the initialization pass. These can be used to restart the algorithm in a similar state to a previous embedding; for example, to improve chain length of a valid embedding or to reduce overlap in a semi-valid embedding (see skip_initialization) previously returned by the algorithm. Missing or empty entries are ignored. Each value in the dictionary is a list of qubit labels.
Default: ()

Fixed chains inserted into an embedding before the initialization pass. As the algorithm proceeds, these chains are not allowed to change, and the qubits used by these chains are not used by other chains. Missing or empty entries are ignored. Each value in the dictionary is a list of qubit labels.
Default: ()

Throughout the algorithm, it is guaranteed that chain[i] is a subset of restrict_chains[i] for each i, except those with missing or empty entries. Each value in the dictionary is a list of qubit labels.
Default: ()

This is a metafeature that is only implemented in the Python interface. suspend_chains[i] is an iterable of iterables; for example, suspend_chains[i] = [blob_1, blob_2], with each blob_j an iterable of target node labels. This enforces the following: for each suspended variable i, for each blob_j in the suspension of i, at least one qubit from blob_j will be contained in the chain for iminorminer accomplishes this through the following problem transformation for each iterable blob_j in suspend_chains[i], Add an auxiliary node Zij to both source and target graphs Set fixed_chains[Zij] = [Zij] Add the edge (i,Zij) to the source graph Add the edges (q,Zij) to the target graph for each q in blob_j
Default: ()

Number of replicas for the parallel tempering.
Default: 4

Parameters for the decomposer.
Default: Decomposer(size=10, min_gain=None, rolling=True, rolling_history=1.0, silent_rewind=True, traversal='energy')

Parameters for the QPU.
Default: Qpu(num_reads=100, num_retries=0, sampling_params=SamplingParams(anneal_offsets=None, anneal_schedule=None, annealing_time=None, auto_scale=None, fast_anneal=False, 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=None), auto_embedding_params=AutoEmbeddingParams(embedding_parameters=EmbeddingParameters(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=())))

Parameters for the fixed temperature sampler.
Default: FixedTemperatureSampler(num_sweeps=10000, num_reads=None)

Multiplier for the CPU count.
Default: 5

Parameters for the main loop of the algorithm.
Default: Loop(max_iter=100, max_time=5, convergence=3, target=None, rtol=1e-05, atol=1e-08)

Nominal number of variables in the subproblem. Actual subproblem can be smaller, depending on other parameters (e.g. min_gain).
Default: 10

Minimum reduction required to BQM energy, given the current sample. A variable is included in the subproblem only if inverting its sample value reduces energy by at least this amount.
Default: None

If True, successive calls for the same problem (with possibly different samples) produce subproblems on different variables, selected by rolling down the list of all variables sorted by decreasing impact.
Default: True

Fraction of the problem size, as a float in range 0.0 to 1.0, that should participate in the rolling selection. Once reached, subproblem unrolling is reset. Min: 0.0, Max: 1.0
Default: 1.0

If False, raises :exc:EndOfStream when resetting/rewinding the subproblem generator upon the reset condition for unrolling.
Default: True

Traversal algorithm used to pick a subproblem of size variables. Options are: energy: Use the next size variables in the list of variables ordered by descending energy impact. bfs: Breadth-first traversal seeded by the next variable in the energy impact list. pfs: Priority-first traversal seeded by variables from the energy impact list, proceeding with the variable on the search boundary that has the highest energy impact.
Default: "energy"

Number of states (output solutions) to read from the sampler.
Default: 100

Number of times the sampler will retry to embed if a failure occurs.
Default: 0

Sampling parameters for the QPU. See https://docs.dwavesys.com/docs/latest/c_solver_parameters.html#anneal-offsets for more details
Default: SamplingParams(anneal_offsets=None, anneal_schedule=None, annealing_time=None, auto_scale=None, fast_anneal=False, 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=None)

Auto embedding parameters for the QPU. See https://docs.ocean.dwavesys.com/projects/system/en/stable/reference/generated/minorminer.find_embedding.html for more details.
Default: AutoEmbeddingParams(embedding_parameters=EmbeddingParameters(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=()))

Number of sweeps for the sampler.
Default: 10000

Number of reads for the sampler.
Default: None

Maximum number of iterations.
Default: 100

Time in seconds after which the algorithm will stop.
Default: 5

Number of iterations with unchanged output to terminate algorithm.
Default: 3

Energy level that the algorithm tries to reach.
Default: None

Relative tolerance for convergence.
Default: 1e-05

Absolute tolerance for convergence.
Default: 1e-08

Anneal offsets for the sampling.
Default: None

Anneal schedule for the sampling.
Default: None

Annealing time for the sampling.
Default: None

Whether to auto scale for the sampling.
Default: None

Use the fast-anneal protocol instead of the standard anneal.
Default: False

Flux biases for the sampling.
Default: None

Whether to use flux drift compensation for the sampling.
Default: True

H gain schedule for the sampling.
Default: None

Initial state for the sampling.
Default: None

Maximum number of answers for the sampling. Min: 1
Default: None

Number of reads for the sampling. Min: 1
Default: 1

Programming thermalization for the sampling. Has to be positive.
Default: None

Readout thermalization for the sampling. Has to be positive.
Default: None

Whether to reduce intersample correlation for the sampling.
Default: False

Whether to reinitialize state for the sampling. Should be None if initial_state is None.
Default: None

Embedding parameters for the auto embedding.
Default: EmbeddingParameters(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=())

Maximum number of failed iterations to improve the current solution, where each iteration attempts to find an embedding for each variable of S such that it is adjacent to all its neighbours.
Default: 10

Seed for the random number generator. If None, seed is set by os.urandom().
Default: None

Algorithm gives up after timeout seconds.
Default: 1000

Qubits are assigned weight according to a formula (beta^n) where n is the number of chains containing that qubit. This value should never be less than or equal to 1. If None, max_beta is effectively infinite.
Default: None

Number of restart attempts before the algorithm stops. On D-WAVE 2000Q, a typical restart takes between 1 and 60 seconds.
Default: 10

The algorithm takes at most this many iterations between restart attempts; restart attempts are typically terminated due to max_no_improvement. If None, inner_rounds is effectively infinite.
Default: None

Maximum number of failed iterations to improve chain lengths in the current solution, where each iteration attempts to find an embedding for each variable of S such that it is adjacent to all its neighbours.
Default: 10

Restricts the number of chains that can simultaneously incorporate the same qubit during the search. Values above 63 are treated as 63. If None, max_fill is effectively infinite.
Default: None

Maximum number of threads to use. Note that the parallelization is only advantageous where the expected degree of variables is significantly greater than the number of threads. Min: 1.
Default: 1

This function returns an embedding, regardless of whether or not qubits are used by multiple variables. return_overlap determines the function's return value. If True, a 2-tuple is returned, in which the first element is the embedding and the second element is a bool representing the embedding validity. If False, only an embedding is returned.
Default: False

Skip the initialization pass. Note that this only works if the chains passed in through initial_chains and fixed_chains are semi-valid. A semi-valid embedding is a collection of chains such that every adjacent pair of variables (u,v) has a coupler (p,q) in the hardware graph where p is in chain(u) and q is in chain(v). This can be used on a valid embedding to immediately skip to the chain length improvement phase. Another good source of semi-valid embeddings is the output of this function with the return_overlap parameter enabled.
Default: False

Initial chains inserted into an embedding before fixed_chains are placed, which occurs before the initialization pass. These can be used to restart the algorithm in a similar state to a previous embedding; for example, to improve chain length of a valid embedding or to reduce overlap in a semi-valid embedding (see skip_initialization) previously returned by the algorithm. Missing or empty entries are ignored. Each value in the dictionary is a list of qubit labels.
Default: ()

Fixed chains inserted into an embedding before the initialization pass. As the algorithm proceeds, these chains are not allowed to change, and the qubits used by these chains are not used by other chains. Missing or empty entries are ignored. Each value in the dictionary is a list of qubit labels.
Default: ()

Throughout the algorithm, it is guaranteed that chain[i] is a subset of restrict_chains[i] for each i, except those with missing or empty entries. Each value in the dictionary is a list of qubit labels.
Default: ()

This is a metafeature that is only implemented in the Python interface. suspend_chains[i] is an iterable of iterables; for example, suspend_chains[i] = [blob_1, blob_2], with each blob_j an iterable of target node labels. This enforces the following: for each suspended variable i, for each blob_j in the suspension of i, at least one qubit from blob_j will be contained in the chain for iminorminer accomplishes this through the following problem transformation for each iterable blob_j in suspend_chains[i], Add an auxiliary node Zij to both source and target graphs Set fixed_chains[Zij] = [Zij] Add the edge (i,Zij) to the source graph Add the edges (q,Zij) to the target graph for each q in blob_j
Default: ()

Parameters for the QBSOLV-like solver.
Default: QBSOLVLike(decomposer_size=50, rolling=True, rolling_history=0.15, cpu_count_multiplier=1, loop=Loop(max_iter=100, max_time=5, convergence=3, target=None, rtol=1e-05, atol=1e-08))

QPU parameters
Default: Qpu(num_reads=100, num_retries=0, sampling_params=SamplingParams(anneal_offsets=None, anneal_schedule=None, annealing_time=None, auto_scale=None, fast_anneal=False, 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=None), auto_embedding_params=AutoEmbeddingParams(embedding_parameters=EmbeddingParameters(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=())))

Size for the decomposer.
Default: 50

Whether to use rolling for the solver.
Default: True

Rolling history for the solver.
Default: 0.15

CPU count multiplier for the solver.
Default: 1

Parameters for the main loop of the algorithm.
Default: Loop(max_iter=100, max_time=5, convergence=3, target=None, rtol=1e-05, atol=1e-08)

Number of states (output solutions) to read from the sampler.
Default: 100

Number of times the sampler will retry to embed if a failure occurs.
Default: 0

Sampling parameters for the QPU. See https://docs.dwavesys.com/docs/latest/c_solver_parameters.html#anneal-offsets for more details
Default: SamplingParams(anneal_offsets=None, anneal_schedule=None, annealing_time=None, auto_scale=None, fast_anneal=False, 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=None)

Auto embedding parameters for the QPU. See https://docs.ocean.dwavesys.com/projects/system/en/stable/reference/generated/minorminer.find_embedding.html for more details.
Default: AutoEmbeddingParams(embedding_parameters=EmbeddingParameters(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=()))

Maximum number of iterations.
Default: 100

Time in seconds after which the algorithm will stop.
Default: 5

Number of iterations with unchanged output to terminate algorithm.
Default: 3

Energy level that the algorithm tries to reach.
Default: None

Relative tolerance for convergence.
Default: 1e-05

Absolute tolerance for convergence.
Default: 1e-08

Anneal offsets for the sampling.
Default: None

Anneal schedule for the sampling.
Default: None

Annealing time for the sampling.
Default: None

Whether to auto scale for the sampling.
Default: None

Use the fast-anneal protocol instead of the standard anneal.
Default: False

Flux biases for the sampling.
Default: None

Whether to use flux drift compensation for the sampling.
Default: True

H gain schedule for the sampling.
Default: None

Initial state for the sampling.
Default: None

Maximum number of answers for the sampling. Min: 1
Default: None

Number of reads for the sampling. Min: 1
Default: 1

Programming thermalization for the sampling. Has to be positive.
Default: None

Readout thermalization for the sampling. Has to be positive.
Default: None

Whether to reduce intersample correlation for the sampling.
Default: False

Whether to reinitialize state for the sampling. Should be None if initial_state is None.
Default: None

Embedding parameters for the auto embedding.
Default: EmbeddingParameters(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=())

Maximum number of failed iterations to improve the current solution, where each iteration attempts to find an embedding for each variable of S such that it is adjacent to all its neighbours.
Default: 10

Seed for the random number generator. If None, seed is set by os.urandom().
Default: None

Algorithm gives up after timeout seconds.
Default: 1000

Qubits are assigned weight according to a formula (beta^n) where n is the number of chains containing that qubit. This value should never be less than or equal to 1. If None, max_beta is effectively infinite.
Default: None

Number of restart attempts before the algorithm stops. On D-WAVE 2000Q, a typical restart takes between 1 and 60 seconds.
Default: 10

The algorithm takes at most this many iterations between restart attempts; restart attempts are typically terminated due to max_no_improvement. If None, inner_rounds is effectively infinite.
Default: None

Maximum number of failed iterations to improve chain lengths in the current solution, where each iteration attempts to find an embedding for each variable of S such that it is adjacent to all its neighbours.
Default: 10

Restricts the number of chains that can simultaneously incorporate the same qubit during the search. Values above 63 are treated as 63. If None, max_fill is effectively infinite.
Default: None

Maximum number of threads to use. Note that the parallelization is only advantageous where the expected degree of variables is significantly greater than the number of threads. Min: 1.
Default: 1

This function returns an embedding, regardless of whether or not qubits are used by multiple variables. return_overlap determines the function's return value. If True, a 2-tuple is returned, in which the first element is the embedding and the second element is a bool representing the embedding validity. If False, only an embedding is returned.
Default: False

Skip the initialization pass. Note that this only works if the chains passed in through initial_chains and fixed_chains are semi-valid. A semi-valid embedding is a collection of chains such that every adjacent pair of variables (u,v) has a coupler (p,q) in the hardware graph where p is in chain(u) and q is in chain(v). This can be used on a valid embedding to immediately skip to the chain length improvement phase. Another good source of semi-valid embeddings is the output of this function with the return_overlap parameter enabled.
Default: False

Initial chains inserted into an embedding before fixed_chains are placed, which occurs before the initialization pass. These can be used to restart the algorithm in a similar state to a previous embedding; for example, to improve chain length of a valid embedding or to reduce overlap in a semi-valid embedding (see skip_initialization) previously returned by the algorithm. Missing or empty entries are ignored. Each value in the dictionary is a list of qubit labels.
Default: ()

Fixed chains inserted into an embedding before the initialization pass. As the algorithm proceeds, these chains are not allowed to change, and the qubits used by these chains are not used by other chains. Missing or empty entries are ignored. Each value in the dictionary is a list of qubit labels.
Default: ()

Throughout the algorithm, it is guaranteed that chain[i] is a subset of restrict_chains[i] for each i, except those with missing or empty entries. Each value in the dictionary is a list of qubit labels.
Default: ()

This is a metafeature that is only implemented in the Python interface. suspend_chains[i] is an iterable of iterables; for example, suspend_chains[i] = [blob_1, blob_2], with each blob_j an iterable of target node labels. This enforces the following: for each suspended variable i, for each blob_j in the suspension of i, at least one qubit from blob_j will be contained in the chain for iminorminer accomplishes this through the following problem transformation for each iterable blob_j in suspend_chains[i], Add an auxiliary node Zij to both source and target graphs Set fixed_chains[Zij] = [Zij] Add the edge (i,Zij) to the source graph Add the edges (q,Zij) to the target graph for each q in blob_j
Default: ()

Tabu parameters for Kerberos algorithm.
Default: TabuKerberos(num_reads=None, tenure=None, timeout=20, initial_states_generator='random', max_time=None)

Simulated annealing parameters for Kerberos algorithm.
Default: SimulatedAnnealing(num_reads=None, num_sweeps=1000, beta_range=None, beta_schedule_type='geometric', initial_states_generator='random')

Decomposer parameters for Kerberos algorithm.
Default: Decomposer(size=10, min_gain=None, rolling=True, rolling_history=1.0, silent_rewind=True, traversal='energy')

QPU parameters for Kerberos algorithm.
Default: Qpu(num_reads=100, num_retries=0, sampling_params=SamplingParams(anneal_offsets=None, anneal_schedule=None, annealing_time=None, auto_scale=None, fast_anneal=False, 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=None), auto_embedding_params=AutoEmbeddingParams(embedding_parameters=EmbeddingParameters(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=())))

Parameters for the main loop of the algorithm.
Default: Loop(max_iter=100, max_time=5, convergence=3, target=None, rtol=1e-05, atol=1e-08)

Multiplier for the CPU count.
Default: 1

Number of reads. Each read is generated by one run of the tabu algorithm. If num_reads is not explicitly given, it is selected to match the number of initial states given. If initial states are not provided, only one read is performed.
Default: None

Tabu tenure, which is the length of the tabu list, or number of recently explored solutions kept in memory. Default is a quarter of the number of problem variables up to a maximum value of 20.
Default: None

Timeout for non-interruptable operation of tabu search.
Default: 20

Defines the expansion of initial_states if fewer than num_reads are specified: 'none': if the number of initial states specified is smaller than num_reads, raises ValueError. 'tile': reuses the specified initial states if fewer than num_reads or truncates if greater. 'random': expands the specified initial states with randomly generated states if fewer than num_reads or truncates if greater.
Default: "random"

Timeout for tabu search.
Default: None

Number of reads. Each read is generated by one run of the simulated annealing algorithm. If num_reads is not explicitly given, it is selected to match the number of initial states given. If initial states are not provided, only one read is performed.
Default: None

Number of sweeps used in annealing.
Default: 1000

A 2-tuple defining the beginning and end of the beta schedule, where beta is the inverse temperature. The schedule is applied linearly in beta. Default range is set based on the total bias associated with each node.
Default: None

Beta schedule type, or how the beta values are interpolated between the given 'beta_range'.
Default: "geometric"

Defines the expansion of initial_states if fewer than num_reads are specified: 'none:' if the number of initial states specified is smaller than num_reads, raises an error. 'tile': reuses the specified initial states if fewer than num_reads or truncates if greater. 'random': expands the specified initial states with randomly generated states if fewer than num_reads or truncates if greater.
Default: "random"

Nominal number of variables in the subproblem. Actual subproblem can be smaller, depending on other parameters (e.g. min_gain).
Default: 10

Minimum reduction required to BQM energy, given the current sample. A variable is included in the subproblem only if inverting its sample value reduces energy by at least this amount.
Default: None

If True, successive calls for the same problem (with possibly different samples) produce subproblems on different variables, selected by rolling down the list of all variables sorted by decreasing impact.
Default: True

Fraction of the problem size, as a float in range 0.0 to 1.0, that should participate in the rolling selection. Once reached, subproblem unrolling is reset. Min: 0.0, Max: 1.0
Default: 1.0

If False, raises :exc:EndOfStream when resetting/rewinding the subproblem generator upon the reset condition for unrolling.
Default: True

Traversal algorithm used to pick a subproblem of size variables. Options are: energy: Use the next size variables in the list of variables ordered by descending energy impact. bfs: Breadth-first traversal seeded by the next variable in the energy impact list. pfs: Priority-first traversal seeded by variables from the energy impact list, proceeding with the variable on the search boundary that has the highest energy impact.
Default: "energy"

Number of states (output solutions) to read from the sampler.
Default: 100

Number of times the sampler will retry to embed if a failure occurs.
Default: 0

Sampling parameters for the QPU. See https://docs.dwavesys.com/docs/latest/c_solver_parameters.html#anneal-offsets for more details
Default: SamplingParams(anneal_offsets=None, anneal_schedule=None, annealing_time=None, auto_scale=None, fast_anneal=False, 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=None)

Auto embedding parameters for the QPU. See https://docs.ocean.dwavesys.com/projects/system/en/stable/reference/generated/minorminer.find_embedding.html for more details.
Default: AutoEmbeddingParams(embedding_parameters=EmbeddingParameters(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=()))

Maximum number of iterations.
Default: 100

Time in seconds after which the algorithm will stop.
Default: 5

Number of iterations with unchanged output to terminate algorithm.
Default: 3

Energy level that the algorithm tries to reach.
Default: None

Relative tolerance for convergence.
Default: 1e-05

Absolute tolerance for convergence.
Default: 1e-08

Anneal offsets for the sampling.
Default: None

Anneal schedule for the sampling.
Default: None

Annealing time for the sampling.
Default: None

Whether to auto scale for the sampling.
Default: None

Use the fast-anneal protocol instead of the standard anneal.
Default: False

Flux biases for the sampling.
Default: None

Whether to use flux drift compensation for the sampling.
Default: True

H gain schedule for the sampling.
Default: None

Initial state for the sampling.
Default: None

Maximum number of answers for the sampling. Min: 1
Default: None

Number of reads for the sampling. Min: 1
Default: 1

Programming thermalization for the sampling. Has to be positive.
Default: None

Readout thermalization for the sampling. Has to be positive.
Default: None

Whether to reduce intersample correlation for the sampling.
Default: False

Whether to reinitialize state for the sampling. Should be None if initial_state is None.
Default: None

Embedding parameters for the auto embedding.
Default: EmbeddingParameters(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=())

Maximum number of failed iterations to improve the current solution, where each iteration attempts to find an embedding for each variable of S such that it is adjacent to all its neighbours.
Default: 10

Seed for the random number generator. If None, seed is set by os.urandom().
Default: None

Algorithm gives up after timeout seconds.
Default: 1000

Qubits are assigned weight according to a formula (beta^n) where n is the number of chains containing that qubit. This value should never be less than or equal to 1. If None, max_beta is effectively infinite.
Default: None

Number of restart attempts before the algorithm stops. On D-WAVE 2000Q, a typical restart takes between 1 and 60 seconds.
Default: 10

The algorithm takes at most this many iterations between restart attempts; restart attempts are typically terminated due to max_no_improvement. If None, inner_rounds is effectively infinite.
Default: None

Maximum number of failed iterations to improve chain lengths in the current solution, where each iteration attempts to find an embedding for each variable of S such that it is adjacent to all its neighbours.
Default: 10

Restricts the number of chains that can simultaneously incorporate the same qubit during the search. Values above 63 are treated as 63. If None, max_fill is effectively infinite.
Default: None

Maximum number of threads to use. Note that the parallelization is only advantageous where the expected degree of variables is significantly greater than the number of threads. Min: 1.
Default: 1

This function returns an embedding, regardless of whether or not qubits are used by multiple variables. return_overlap determines the function's return value. If True, a 2-tuple is returned, in which the first element is the embedding and the second element is a bool representing the embedding validity. If False, only an embedding is returned.
Default: False

Skip the initialization pass. Note that this only works if the chains passed in through initial_chains and fixed_chains are semi-valid. A semi-valid embedding is a collection of chains such that every adjacent pair of variables (u,v) has a coupler (p,q) in the hardware graph where p is in chain(u) and q is in chain(v). This can be used on a valid embedding to immediately skip to the chain length improvement phase. Another good source of semi-valid embeddings is the output of this function with the return_overlap parameter enabled.
Default: False

Initial chains inserted into an embedding before fixed_chains are placed, which occurs before the initialization pass. These can be used to restart the algorithm in a similar state to a previous embedding; for example, to improve chain length of a valid embedding or to reduce overlap in a semi-valid embedding (see skip_initialization) previously returned by the algorithm. Missing or empty entries are ignored. Each value in the dictionary is a list of qubit labels.
Default: ()

Fixed chains inserted into an embedding before the initialization pass. As the algorithm proceeds, these chains are not allowed to change, and the qubits used by these chains are not used by other chains. Missing or empty entries are ignored. Each value in the dictionary is a list of qubit labels.
Default: ()

Throughout the algorithm, it is guaranteed that chain[i] is a subset of restrict_chains[i] for each i, except those with missing or empty entries. Each value in the dictionary is a list of qubit labels.
Default: ()

This is a metafeature that is only implemented in the Python interface. suspend_chains[i] is an iterable of iterables; for example, suspend_chains[i] = [blob_1, blob_2], with each blob_j an iterable of target node labels. This enforces the following: for each suspended variable i, for each blob_j in the suspension of i, at least one qubit from blob_j will be contained in the chain for iminorminer accomplishes this through the following problem transformation for each iterable blob_j in suspend_chains[i], Add an auxiliary node Zij to both source and target graphs Set fixed_chains[Zij] = [Zij] Add the edge (i,Zij) to the source graph Add the edges (q,Zij) to the target graph for each q in blob_j
Default: ()