See non-disenchantable armor and weapons on Wowhead database. Faction reputation vendor-purchased armor and weapons.Only (but not all) armor and weapons of only uncommon, rare or epic quality are disenchantable. Crystals come from epic equipment iLevel 56+ (100%) and extremely rarely from rare equipment (~0.5%).Shards come from rare equipment (~100%), epic equipment iLevel 55 or less (100%, 2-4x), and rarely from uncommon equipment (3-5%).Essences usually come from uncommon weapons (75%) and armor (20-22%).Dusts usually come from uncommon armor (75%) and weapons (20-22%).The type and quantity of materials produced from disenchanting varies by item type (armor or weapon), iLevel and quality. Reduce the depth of nested operators to the minimum.Epic items lower than iLevel 61 fall within the ranges for Uncommon/Rare itemsįor more detailed percentages, quantities, and iLevels associated with each material, see the disenchanting tables.
Returns the expected shape of the weights tensor. Z ( float) – exponent for the operator ReturnsĪppend the operator to the Operator queue. Wire_order ( Iterable) – global wire order, must contain all wire labels from the operator’s wires ReturnsĪ list of new operators equal to this one raised to the given power. Will be cast in the same autodifferentiation framework as the parameters.Ī MatrixUndefinedError is raised if the matrix representation has not been defined. If the matrix depends on trainable parameters, the result Otherwise, the wire order defaults to the Operator’s wires in the global wire order. If wire_order is provided, the numerical representation considers the position of the Representation of the operator as a matrix in the computational basis. Wire_map ( dict) – dictionary containing the old wires as keys and the new wires as values Returns Returns a copy of the current operator with its wires changed according to the given label ( cache = cache ) 'U(M1)' > cache, ], requires_grad=True), tensor(,, , ], requires_grad=True)] map_wires ( wire_map ) ¶ label ( cache = cache ) 'U(M0)' > cache, ], requires_grad=True)] > op3 = qml. Returns the frequencies for each operator parameter with respect to an expectation value of the form \(\langle \psi | U(\mathbf > op2. Number of trainable parameters that the operator depends on. Number of dimensions per trainable parameter of the operator. The number of layers L is therefore derived from the first dimension of weights. The argument weights contains the weights for each layer. This property determines if an operator is hermitian. Layers consisting of single qubit rotations and entanglers, inspired by the circuit-centric classifier design arXiv:1804.00633. Integer hash that uniquely represents the operator.ĭictionary of non-trainable variables that this operation depends on.Ĭustom string to label a specific operator instance. Gradient recipe for the parameter-shift method. The basis of an operation, or for controlled gates, of the target operation.īatch size of the operator if it is used with broadcasted parameters. Holdover from when in-place inversion changed then name. For example, for range= theįirst layer will have a range parameter of 2 and the second layer willĪssuming wires= and a range parameter of 2, there will be Is the difference of the wire indices representing the two qubits the The ranges argument takes an integer sequence where each elementĭetermines the range hyperparameter for each layer. This can be changed by using the imprimitive argument.
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