# Solver Properties#

The properties that characterize behaviors and supported features of SAPI solvers[1] can be queried through SAPI. This chapter defines these properties[2].

The examples below use the following setup:

```
>>> from dwave.system import DWaveSampler
>>> from dwave.system import LeapHybridSampler, LeapHybridCQMSampler, LeapHybridDQMSampler, LeapHybridNLSampler
...
>>> qpu_advantage = DWaveSampler(solver={'topology__type': 'pegasus'})
>>> hybrid_bqm_sampler = LeapHybridSampler()
>>> hybrid_cqm_sampler = LeapHybridCQMSampler()
>>> hybrid_dqm_sampler = LeapHybridDQMSampler()
>>> hybrid_nl_sampler = LeapHybridNLSampler()
```

## anneal_offset_ranges#

Array of ranges of valid anneal offset values, in normalized offset units, for each qubit. The negative values represent the largest number of normalized offset units by which a qubit’s anneal path may be delayed. The positive values represent the largest number of normalized offset units by which a qubit’s anneal path may be advanced.

### Example#

For the solver configured in the example setup above,

```
>>> qpu_advantage.properties["anneal_offset_ranges"][50:55]
[[-0.6437569799443247, 0.5093937328282595],
[-0.6409899716199288, 0.5697835920492209],
[-0.6468663649068287, 0.5211452841036136],
[-0.6435316809914349, 0.5011584701861175],
[-0.6500326191157569, 0.5392206819650301]]
```

## anneal_offset_step#

Quantization step size of anneal offset values in normalized units.

### Example#

For the solver configured in the example setup above,

```
>>> qpu_advantage.properties["anneal_offset_step"]
-0.00017565852000668507
```

## anneal_offset_step_phi0#

Quantization step size in physical units (annealing flux-bias units): \(\Phi_0\).

### Example#

For the solver configured in the example setup above,

```
>>> qpu_advantage.properties["anneal_offset_step_phi0"]
1.486239425109832e-05
```

## annealing_time_range#

Range of time, in microseconds with a resolution of 0.01 \(\mu s\), possible for one anneal (read). The lower limit in this range is the fastest quench possible for this solver using the standard-anneal protocol. See also the fast_anneal_time_range.

Default annealing time is specified by the default_annealing_time property.

### Example#

For the solver configured in the example setup above,

```
>>> qpu_advantage.properties["annealing_time_range"]
[0.5, 2000.0]
```

## category#

Type of solver; for example,

`qpu`

: Quantum computers such as the Advantage.`hybrid`

: Quantum-classical hybrid; typically one or more classical algorithms run on the problem while outsourcing to a quantum processing unit (QPU) parts of the problem where it benefits most.

### Example#

For the solver configured in the example setup above,

```
>>> hybrid_bqm_sampler.properties["category"]
'hybrid'
```

## chip_id#

Name of the solver.

### Example#

For the solver configured in the example setup above,

```
>>> qpu_advantage.properties["chip_id"]
'Advantage_system1.1'
```

## couplers#

Couplers in the working graph. A coupler contains two elements [*q1*, *q2*],
where both *q1* and *q2* appear in the list of working qubits, in the range
[0, *num_qubits* - 1] and in ascending order (i.e., *q1* < *q2*). These are the
couplers that can be programmed with nonzero \(J\) values.

### Example#

For the solver configured in the example setup above,

```
>>> qpu_advantage.properties["couplers"][:5]
[[30, 31], [31, 32], [32, 33], [33, 34], [34, 35]]
```

## default_annealing_time#

Default time, in microseconds with a resolution of 0.01 \(\mu s\), for one anneal (read). You can change the annealing time for a given problem by using the annealing_time or anneal_schedule parameters, but do not exceed the upper limit given by the annealing_time_range or fast_anneal_time_range property.

### Example#

For the solver configured in the example setup above,

```
>>> qpu_advantage.properties["default_annealing_time"]
20.0
```

## default_programming_thermalization#

Default time, in microseconds with a resolution of 0.01 \(\mu s\), that the
system waits after programming the QPU for it to return to base temperature.
This value contributes to the total *qpu_programming_time*,
which is returned by SAPI with the problem solutions.

You can change this value using the programming_thermalization parameter.

### Example#

For the solver configured in the example setup above,

```
>>> qpu_advantage.properties["default_programming_thermalization"]
1000.0
```

## default_readout_thermalization#

Default time, in microseconds with a resolution of 0.01 \(\mu s\), that the system
waits after each state is read from the QPU for it to cool back to base temperature.
This value contributes to the *qpu_delay_time_per_sample* field, which is returned
by SAPI with the problem solutions.

### Example#

For the solver configured in the example setup above,

```
>>> qpu_advantage.properties["default_readout_thermalization"]
0.0
```

## extended_j_range#

Extended range of values possible for the coupling strengths (quadratic coefficients), \(J\), for this solver. Strong negative couplings may be necessary for some embeddings; however, such chains may require additional calibration through the flux_biases parameter to compensate for biases introduced by strong negative couplings. See also per_qubit_coupling_range and per_group_coupling_range.

The auto_scale parameter, which rescales \(h\) and \(J\) values in the problem to use as much of the range of \(h\) (h_range) and the range of \(J\) (extended_j_range) as possible, enables you to submit problems with values outside these ranges and have the system automatically scale them to fit.

### Example#

For the solver configured in the example setup above,

```
>>> qpu_advantage.properties["extended_j_range"]
[-2.0, 1.0]
```

## fast_anneal_time_range#

Range of time, in microseconds with a resolution of up to picoseconds,[3] possible for one anneal (read). The lower limit in this range is the fastest quench possible for this solver.

Default annealing time is specified by the default_annealing_time property.

The fast-anneal protocol supports a time granularity of about 0.05% of the anneal time. For annealing times of about 10 ns, the granularity is about 5 ps; for anneals of about 20 \(\mu s\), it is reduced to around 10 ns.

### Example#

For the solver configured in the example setup above,

```
>>> from dwave.system import DWaveSampler
>>> qpu_advantage2 = DWaveSampler(solver={"topology__type": "zephyr"})
>>> qpu_advantage2.properties["fast_anneal_time_range"]
[0.005, 2000.0]
```

## h_gain_schedule_range#

Range of the time-dependent gain applied to qubit biases for this solver.

### Example#

For the solver configured in the example setup above,

```
>>> qpu_advantage.properties["h_gain_schedule_range"]
[-4.0, 4.0]
```

## h_range#

Range of values possible for the qubit biases (linear coefficients), \(h\), for this solver.

The auto_scale parameter, which rescales \(h\) and \(J\) values in the problem to use as much of the range of \(h\) (h_range) and the range of \(J\) (extended_j_range) as possible, enables you to submit problems with values outside these ranges and have the system automatically scale them to fit.

### Example#

For the solver configured in the example setup above,

```
>>> qpu_advantage.properties["h_range"]
[-4.0, 4.0]
```

## j_range#

Range of values possible for the coupling strengths (quadratic coefficients), \(J\), for this solver.

See also the extended_j_range solver property.

### Example#

For the solver configured in the example setup above,

```
>>> qpu_advantage.properties["j_range"]
[-1.0, 1.0]
```

## max_anneal_schedule_points#

Maximum number of points permitted in a PWL waveform submitted to change the default anneal schedule.

For reverse annealing, the maximum number of points allowed is one more than the number given in the max_anneal_schedule_points property.

### Example#

For the solver configured in the example setup above,

```
>>> qpu_advantage.properties["max_anneal_schedule_points"]
12
```

## max_h_gain_schedule_points#

Maximum number of points permitted in a PWL waveform submitted to set a time-dependent gain on linear coefficients (qubit biases, see the h parameter) in the Hamiltonian.

### Example#

For the solver configured in the example setup above,

```
>>> qpu_advantage.properties["max_h_gain_schedule_points"]
20
```

## maximum_decision_state_size#

Maximum size, in bytes, of all decision-variable states,
`decision_state_size()`

, accepted by the solver.

### Example#

For the solver configured in the example setup above,

```
>>> hybrid_nl_sampler.properties["maximum_decision_state_size"]
52428800
```

## maximum_number_of_biases#

Maximum number of biases, both linear and quadratic in total, accepted by the solver.

### Example#

For the solver configured in the example setup above,

```
>>> hybrid_bqm_sampler.properties["maximum_number_of_biases"]
200000000
```

## maximum_number_of_cases#

Maximum number of cases accepted by the solver.

For more details about cases and their role in DQMs, see the section on the DQM concept in the Ocean documentation.

### Example#

For the solver configured in the example setup above,

```
>>> hybrid_dqm_sampler.properties["maximum_number_of_cases"]
500000
```

## maximum_number_of_constraints#

Maximum number of constraints accepted by the solver.

### Example#

For the solver configured in the example setup above,

```
>>> hybrid_cqm_sampler.properties["maximum_number_of_constraints"]
100000
```

## maximum_number_of_linear_biases_real#

Maximum number of linear biases of type floating-point/real numbers counted across all constraints and the model’s objective.

### Example#

For the solver configured in the example setup above,

```
>>> hybrid_cqm_sampler.properties["maximum_number_of_linear_biases_real"]
75000000
```

## maximum_number_of_nodes#

Maximum number of nodes, `num_nodes()`

,
accepted by the solver.

### Example#

For the solver configured in the example setup above,

```
>>> hybrid_nl_sampler.properties["maximum_number_of_nodes"]
2000000
```

## maximum_number_of_quadratic_variables#

Maximum number of variables that have interactions (quadratic biases)—counted across all constraints (meaning that the same variable can be counted multiple times if it appears in interactions in more than one constraint)—accepted by the solver.

### Example#

For the solver configured in the example setup above,

```
>>> hybrid_cqm_sampler.properties["maximum_number_of_quadratic_variables"]
15000000
```

## maximum_number_of_quadratic_variables_real#

Maximum number of variables that have interactions (quadratic biases) of type floating-point/real numbers—counted across all constraints (meaning that the same variable can be counted multiple times if it appears in interactions in more than one constraint)—accepted by the solver.

### Example#

For the solver configured in the example setup above,

```
>>> hybrid_cqm_sampler.properties["maximum_number_of_quadratic_variables_real"]
0
```

## maximum_number_of_states#

Maximum number of initialized states[4], `size()`

,
accepted by the solver.

Submitted models can include states, typically to provide initial decision-variable assignments that might accelerate solutions. Such included states, with values initialized or not, are counted by maximum_number_of_states.

### Example#

For the solver configured in the example setup above,

```
>>> hybrid_nl_sampler.properties["maximum_number_of_states"]
1
```

## maximum_number_of_variables#

Maximum number of problem variables accepted by the solver.

### Example#

For the solver configured in the example setup above,

```
>>> hybrid_bqm_sampler.properties["maximum_number_of_variables"]
1000000
```

## maximum_state_size#

Maximum size, in bytes, of all states, `state_size()`

,
accepted by the solver.

### Example#

For the solver configured in the example setup above,

```
>>> hybrid_nl_sampler.properties["maximum_state_size"]
786432000
```

## maximum_time_limit_hrs#

Maximum allowed run time, in hours, that can be specified for the solver.

### Example#

For the solver configured in the example setup above,

```
>>> hybrid_bqm_sampler.properties["maximum_time_limit_hrs"]
24.0
```

## minimum_time_limit#

Minimum required run time, in seconds, the solver must be allowed to work on the given problem. Specifies the minimum time as a piecewise-linear curve defined by a set of floating-point pairs. The second element is the minimum required time; the first element in each pair is some measure of the problem, dependent on the solver:

For hybrid BQM solvers, this is the number of variables.

For hybrid DQM solvers, this is a combination of the numbers of interactions, variables, and cases that reflects the “density” of connectivity between the problem’s variables.

The minimum time for any particular problem is a linear interpolation calculated
on two pairs that represent the relevant range for the given measure of the
problem. For example, if `minimum_time_limit`

for a hybrid BQM
solver were `[[1, 0.1], [100, 10.0], [1000, 20.0]]`

, then the minimum time
for a 50-variable problem would be 5 seconds, the linear interpolation of the
first two pairs that represent problems with between 1 to 100 variables.

For more details, see the Ocean samplers documentation for solver methods that calculate this parameter, and their descriptions.

### Example#

For the solver configured in the example setup above,

```
>>> hybrid_bqm_sampler.properties["minimum_time_limit"]
[[1, 3.0],
[1024, 3.0],
[4096, 10.0],
[10000, 40.0],
[30000, 200.0],
[100000, 600.0],
[1000000, 600.0]]
```

## minimum_time_limit_s#

Minimum required run time, in seconds, the solver must be allowed to work on any given problem.

For more details, see the Ocean samplers documentation for solver methods that calculate the default runtime of a given problem.

### Example#

For the solver configured in the example setup above,

```
>>> hybrid_cqm_sampler.properties["minimum_time_limit_s"]
5
```

## num_biases_multiplier#

Multiplier used in the internal calculation of the minimum run time, minimum_time_limit_s, the solver must be allowed to work on the given problem.

### Example#

For the solver configured in the example setup above,

```
>>> hybrid_cqm_sampler.properties["num_biases_multiplier"]
4.65705646e-06
```

## num_constraints_multiplier#

Multiplier used in the internal calculation of the minimum run time, minimum_time_limit_s, the solver must be allowed to work on the given problem.

### Example#

For the solver configured in the example setup above,

```
>>> hybrid_cqm_sampler.properties["num_constraints_multiplier"]
6.44385747e-09
```

## num_nodes_multiplier#

Multiplier applied to a problem’s total number of nodes, the product of which is used in the internal estimate of the problem’s minimum runtime.

### Example#

For the solver configured in the example setup above,

```
>>> hybrid_nl_sampler.properties["num_nodes_multiplier"]
0.00008306792043756981
```

## num_nodes_state_size_multiplier#

Multiplier applied to the totals of a problem’s state sizes and number of nodes, the product of which is used in the internal estimate of the problem’s minimum runtime.

### Example#

For the solver configured in the example setup above,

```
>>> hybrid_nl_sampler.properties["num_nodes_state_size_multiplier"]
2.1097317822863965e-12
```

## num_qubits#

Total number of qubits, both working and nonworking,[5] in the QPU.

The qubits property provides the indices of qubits in the QPU’s working graph, which are the qubits you can use for problem solving.

### Example#

For the solver configured in the example setup above,

```
>>> qpu_advantage.properties["num_qubits"]
5760
```

## num_reads_range#

Range of values possible for the number of reads that you can request for a problem.

### Example#

For the solver configured in the example setup above,

```
>>> qpu_advantage.properties["num_reads_range"]
[1, 10000]
```

## num_variables_multiplier#

Multiplier used in the internal calculation of the minimum run time, minimum_time_limit_s, the solver must be allowed to work on the given problem.

### Example#

For the solver configured in the example setup above,

```
>>> hybrid_cqm_sampler.properties["num_variables_multiplier"]
0.000157411458
```

## parameters#

List of the parameters supported for the solver.

### Example#

For the solver configured in the example setup above,

```
>>> hybrid_bqm_sampler.properties["parameters"]
{'time_limit': 'Maximum requested runtime in seconds.'}
```

## per_group_coupling_range#

Coupling range permitted per group of qubits for this solver. The couplers of every qubit are divided into two similar sized groups to which apply limitations on the total of all coupling strengths.

Strong negative couplings may be necessary for some embeddings, and can be enabled by using the extended_j_range range of \(J\) values. However, total \(J\) values of the couplers for a group of qubits must not exceed the range specified by this property. Also, chains may require additional calibration through the flux_biases parameter to compensate for biases introduced by strong negative couplings.

The auto_scale parameter scales a problem’s \(J\) values such that the total \(J\) values of the couplers for a group of qubits is within the range specified by this property.

For more information, see the Ocean documentation’s coupling_groups function.

### Example#

```
>>> from dwave.system import DWaveSampler
>>> qpu_advantage2 = DWaveSampler(solver={"topology__type": "zephyr"})
>>> qpu_advantage2.properties["per_group_coupling_range"]
[-13.000 10.000]
```

## per_qubit_coupling_range#

Coupling range permitted per qubit for this solver.

Strong negative couplings may be necessary for some embeddings, and can be enabled by using the extended_j_range range of \(J\) values. However, the total \(J\) values of the couplers for a qubit must not exceed the range specified by this property. Also, chains may require additional calibration through the flux_biases parameter to compensate for biases introduced by strong negative couplings.

The auto_scale parameter scales a problem’s \(J\) values such that the total \(J\) values of the couplers for a qubit is within the range specified by this property.

### Example#

For the solver configured in the example setup above,

```
>>> qpu_advantage.properties["per_qubit_coupling_range"]
[-18.0, 15.0]
```

## problem_run_duration_range#

Range of time, in microseconds with a resolution of 0.01 \(\mu s\), that a submitted problem is allowed to run.

For details about how the run-duration limit is calculated, see QMI Runtime Limit.

### Example#

For the solver configured in the example setup above,

```
>>> qpu_advantage.properties["problem_run_duration_range"]
[0.0, 1000000.0]
```

## problem_timing_data#

Key-value pairs that contain sample timing data and specify the version 1.0.x problem-timing model used to estimate problem runtimes for a QPU solver. For details about using the timing data to estimate the QPU access times for problem submissions, see the Estimating Access Time section.

The key-value pairs are the following:

`decorrelation_max_nominal_anneal_time`

: Parameter, in microseconds, used in the calculation of inter-sample correlation time. For more information, see the reduce_intersample_correlation solver property.`decorrelation_time_range`

: Range of inter-sample correlation time, in microseconds, to add to the per-sample delay time as specified in the`qpu_delay_time_per_sample`

field. Scales linearly with the anneal time from 0 to the amount returned in the`decorrelation_max_nominal_anneal_time`

field. For more details, see the reduce_intersample_correlation solver parameter.`default_annealing_time`

: See the default_annealing_time solver property.`default_programming_thermalization`

: See the default_programming_thermalization solver property.`default_readout_thermalization`

: See the default_readout_thermalization solver property.`qpu_delay_time_per_sample`

: Default for the per-sample delay time in microseconds.`readout_time_model`

: The readout-time model used to generate the set of data points; these data points define a piecewise-linear curve of typical readout times as a function of the number of qubits in a representative set of problems embedded on a QPU. The data points are provided in the`readout_time_model_parameters`

field. Supported values that specify the scale of the curve are:`pwl_log_log`

: The log scale is used for both the horizontal and vertical axes.`pwl_linear`

: The linear scale is used for both the horizontal and vertical axes.

`readout_time_model_parameters`

: A set of \(2N\) data points generated by the readout-time model specified in the`readout_time_model`

field.`reverse_annealing_with_reinit_delay_time_delta`

: Extra per-sample delay time, in microseconds, when reverse annealing is used and the reinitialize_state parameter is true.`reverse_annealing_with_reinit_prog_time_delta`

: Extra programming time, in microseconds, when reverse annealing is used and the reinitialize_state solver parameter is true.`reverse_annealing_without_reinit_delay_time_delta`

: Extra per-sample delay time, in microseconds, when reverse annealing is used and the reinitialize_state solver parameter is false.`reverse_annealing_without_reinit_prog_time_delta`

: Extra programming time, in microseconds, when reverse annealing is used and the reinitialize_state solver parameter is false.`typical_programming_time`

: Typical programming time in microseconds.`version`

: Version of the problem-timing model.

### Example#

For the solver configured in the example setup above,

```
>>> qpu_advantage.properties["problem_timing_data"]
{'version': '1.0.0', 'typical_programming_time': 14072.88,
'reverse_annealing_with_reinit_prog_time_delta': 0.0,
'reverse_annealing_without_reinit_prog_time_delta': 5.55,
'default_programming_thermalization': 1000.0, 'default_annealing_time': 20.0,
'readout_time_model': 'pwl_log_log',
'readout_time_model_parameters': [0.0, 0.7699665876947938, 1.7242758696010096,
2.711975459489206, 3.1639057672764026, 3.750276915153992, 1.539131714800995,
1.8726623164229292, 2.125631787097315, 2.332672340068556, 2.371606651233025,
2.3716219271760215], 'qpu_delay_time_per_sample': 20.54,
'reverse_annealing_with_reinit_delay_time_delta': -4.5,
'reverse_annealing_without_reinit_delay_time_delta': -1.5,
'default_readout_thermalization': 0.0, 'decorrelation_max_nominal_anneal_time': 2000.0,
'decorrelation_time_range': [500.0, 10000.0]}
```

## programming_thermalization_range#

Range of time, in microseconds with a resolution of 0.01 \(\mu s\), possible for
the system to wait after programming the QPU for it to cool back to base
temperature. This value contributes to the total *qpu_programming_time*, which
is returned by SAPI with the problem solutions.

You can change this value using the programming_thermalization parameter, but be aware that values lower than the default accelerate solving at the expense of solution quality.

Default value for a solver is given in the default_programming_thermalization property.

### Example#

For the solver configured in the example setup above,

```
>>> qpu_advantage.properties["programming_thermalization_range"]
[0.0, 10000.0]
```

## qubits#

Indices of the working graph.

In a D-Wave QPU, the set of qubits and couplers that are available for
computation is known as the working graph.
The working graph typically does not contain all the qubits and couplers that
are fabricated and physically present in the QPU. For example, an Advantage
with its 5760 qubits (the num_qubits property), of which a
maximum of 5640 are calibrated for problem solving (the programmable fabric,
or largest component of the full Pegasus graph), might have a working graph of
5627 qubits (a *yield* of 99.7%) due to 13 qubits that were not brought into a
consistent parametric regime in the calibration process. For such a system, the
value of the `qubits`

property is the sequence of integers between 30, the
first of the 5640 fabric qubits, and 5729, the last of those 5640 qubits, minus
indices of the 13 non-working qubits.[6]

You can see the qubit indexing scheme of such an Advantage QPU with Ocean’s
`dwave_networkx.pegasus_graph()`

function, which enables you to generate
Pegasus graphs.

### Example#

For the solver configured in the example setup above,

```
>>> qpu_advantage.properties["qubits"][:5]
[30, 31, 32, 33, 34]
```

## quota_conversion_rate#

Rate at which user or project quota is consumed for the solver as a ratio to QPU solver usage. Different solver types may consume quota at different rates.

Time is deducted from your quota according to:

See the QPU Solver Datasheet guide for more information.

### Example#

For the solver configured in the example setup above,

```
>>> hybrid_bqm_sampler.properties["quota_conversion_rate"]
1
```

## readout_thermalization_range#

Range of time, in microseconds with a resolution of 0.01 \(\mu s\), possible for
the system to wait after each state is read from the QPU for it to cool back to
base temperature. This value contributes to the *qpu_delay_time_per_sample*
field, which is returned by SAPI with the problem solutions.

Default readout thermalization time is specified in the default_readout_thermalization property.

### Example#

For the solver configured in the example setup above,

```
>>> qpu_advantage.properties["readout_thermalization_range"]
[0.0, 10000.0]
```

## state_size_multiplier#

Multiplier applied to a problem’s state-size total, the product of which is used in the internal estimate of the problem’s minimum runtime.

### Example#

For the solver configured in the example setup above,

```
>>> hybrid_nl_sampler.properties["state_size_multiplier"]
2.8379674360396316e-10
```

## supported_problem_types#

Indicates what problem types are supported for the solver.

QPU solvers support the following energy-minimization problem types:

`qubo`

: Quadratic unconstrained binary optimization (QUBO) problems; use \(0/1\)-valued variables.`ising`

: Ising model problems; use \(-1/1\)-valued variables.

Hybrid solvers support the following energy-minimization problem types:

`bqm`

: Binary quadratic model (BQM) problems; use \(0/1\)-valued variables and \(-1/1\)-valued variables; constraints are typically represented as penalty models.`cqm`

: Constrained quadratic model (CQM) problems; use both binary-valued variables (both \(0/1\)-valued variables and \(-1/1\)-valued variables), integer-valued variables, real variables (also known as*continuous variables*); support constraints natively.`dqm`

: Discrete quadratic model (DQM) problems; use variables that can represent a set of values such as`{red, green, blue, yellow}`

or`{3.2, 67}`

; constraints are typically represented as penalty models.`nl`

: Nonlinear-model problems; use with integer and binary variables; support constraints natively.

### Example#

For the solver configured in the example setup above,

```
>>> qpu_advantage.properties["supported_problem_types"]
['ising', 'qubo']
```

## time_constant#

Used to estimate the minimum runtime required for a problem.

### Example#

For the solver configured in the example setup above,

```
>>> hybrid_nl_sampler.properties["time_constant"]
0.012671678446550176
```

## topology#

Indicates the topology `type`

(`pegasus`

or `zephyr`

) and `shape`

of
the QPU graph.

### Example#

The topology seen in this example is a P16 Pegasus graph. See the D-Wave QPU Architecture: Topologies section of the Getting Started with D-Wave Solvers guide for a description of QPU topologies.

For the solver configured in the example setup above,

```
>>> qpu_advantage.properties["topology"]
{'type': 'pegasus', 'shape': [16]}
```

## version#

Version number of the solver.

### Example#

For the solver configured in the example setup above,

```
>>> hybrid_bqm_sampler.properties["version"]
'2.0'
```

## vfyc#

Obsolete property that is always `False`

.

### Example#

For the solver configured in the example setup above,

```
>>> qpu_advantage.properties["vfyc"]
False
```