Update pennylane requirement from ~=0.36.0 to ~=0.37.0

[dependabot]

Updates the requirements on pennylane to permit the latest version.

Release notes

Sourced from pennylane's releases.

Release 0.37.0

• A new default.tensor device is now available for performing tensor network and matrix product state simulations of quantum circuits using the quimb backend. [(#5699)](PennyLaneAI/pennylane#5699) [(#5744)](PennyLaneAI/pennylane#5744) [(#5786)](PennyLaneAI/pennylane#5786) [(#5795)](PennyLaneAI/pennylane#5795)

  Either method can be selected when instantiating the default.tensor device by setting the method keyword argument to "tn" (tensor network) or "mps" (matrix product state).

  There are several templates in PennyLane that are tensor-network focused, which are excellent candidates for the "tn" method for default.tensor. The following example shows how a circuit comprising gates in a tree tensor network architecture can be efficiently simulated using method="tn".

  import pennylane as qml
  n_wires = 16
  dev = qml.device("default.tensor", method="tn")
  def block(weights, wires):
  qml.CNOT(wires=[wires[0], wires[1]])
  qml.RY(weights[0], wires=wires[0])
  qml.RY(weights[1], wires=wires[1])
  n_block_wires = 2
  n_params_block = 2
  n_blocks = qml.TTN.get_n_blocks(range(n_wires), n_block_wires)
  template_weights = [[0.1, -0.3]] * n_blocks
  @​qml.qnode(dev)
  def circuit(template_weights):
  for i in range(n_wires):
  qml.Hadamard(i)
  qml.TTN(range(n_wires), n_block_wires, block, n_params_block, template_weights)
  return qml.expval(qml.Z(n_wires - 1))

  >>> circuit(template_weights)
  0.3839174759751649

  For matrix product state simulations (method="mps"), we can make the execution be approximate by setting max_bond_dim (see the device's documentation for more details). The maximum bond dimension has implications for the speed of the simulation and lets us control the degree of the approximation, as shown in the following example. First, set up the circuit:

  import numpy as np
  n_layers = 10
  n_wires = 10
  initial_shape, weights_shape = qml.SimplifiedTwoDesign.shape(n_layers, n_wires)
  np.random.seed(1967)

... (truncated)

Commits

• af5bd6d Bump required versions of Lightning and Catalyst (#5963)
• 9a60574 Fix jax enable x64 (#5960)
• add20fc FF Fixes (#5931)
• 0218745 Doc fixes (#5952)
• ea14834 QA release fixes (#5934)
• 60f9da2 [sc-59665] v0.37 changelog (#5928)
• b8b9f19 Lazy check keras layer (#5956)
• 9c22ac7 Fix invert dunder method. (#5955)
• 0e5dd6b Fixes split_non_commuting with non-trainable autograd coefficients (#5945)
• a6f133b Documentation fixes (#5946)
• Additional commits viewable in compare view

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[dependabot]

Superseded by #2492.