PennyLaneAI/pennylane v0.23.0

Release 0.23.0

on GitHub

New features since last release

More powerful circuit cutting ✂️

• Quantum circuit cutting (running N-wire circuits on devices with fewer than N wires) is now supported for QNodes of finite-shots using the new @qml.cut_circuit_mc transform. (#2313) (#2321) (#2332) (#2358) (#2382) (#2399) (#2407) (#2444)

  With these new additions, samples from the original circuit can be simulated using a Monte Carlo method, using fewer qubits at the expense of more device executions. Additionally, this transform can take an optional classical processing function as an argument and return an expectation value.

  The following 3-qubit circuit contains a WireCut operation and a sample measurement. When decorated with @qml.cut_circuit_mc, we can cut the circuit into two 2-qubit fragments:

  dev = qml.device("default.qubit", wires=2, shots=1000)
  
  @qml.cut_circuit_mc
  @qml.qnode(dev)
  def circuit(x):
      qml.RX(0.89, wires=0)
      qml.RY(0.5, wires=1)
      qml.RX(1.3, wires=2)
  
      qml.CNOT(wires=[0, 1])
      qml.WireCut(wires=1)
      qml.CNOT(wires=[1, 2])
  
      qml.RX(x, wires=0)
      qml.RY(0.7, wires=1)
      qml.RX(2.3, wires=2)
      return qml.sample(wires=[0, 2])

  we can then execute the circuit as usual by calling the QNode:

  >>> x = 0.3
  >>> circuit(x)
  tensor([[1, 1],
          [0, 1],
          [0, 1],
          ...,
          [0, 1],
          [0, 1],
          [0, 1]], requires_grad=True)

  Furthermore, the number of shots can be temporarily altered when calling the QNode:

  >>> results = circuit(x, shots=123)
  >>> results.shape
  (123, 2)

  The cut_circuit_mc transform also supports returning sample-based expectation values of observables using the classical_processing_fn argument. Refer to the UsageDetails section of the transform documentation for an example.

• The cut_circuit transform now supports automatic graph partitioning by specifying auto_cutter=True to cut arbitrary tape-converted graphs using the general purpose graph partitioning framework KaHyPar. (#2330) (#2428)

  Note that KaHyPar needs to be installed separately with the auto_cutter=True option.

  For integration with the existing low-level manual cut pipeline, refer to the documentation of the
  function.

  @qml.cut_circuit(auto_cutter=True)
  @qml.qnode(dev)
  def circuit(x):
      qml.RX(x, wires=0)
      qml.RY(0.9, wires=1)
      qml.RX(0.3, wires=2)
      qml.CZ(wires=[0, 1])
      qml.RY(-0.4, wires=0)
      qml.CZ(wires=[1, 2])
      return qml.expval(qml.grouping.string_to_pauli_word("ZZZ"))

  >>> x = np.array(0.531, requires_grad=True)
  >>> circuit(x)
  0.47165198882111165
  >>> qml.grad(circuit)(x)
  -0.276982865449393

Grand QChem unification ⚛️ 🏰

• Quantum chemistry functionality --- previously split between an external pennylane-qchem package and internal qml.hf differentiable Hartree-Fock solver --- is now unified into a single, included, qml.qchem module. (#2164) (#2385) (#2352) (#2420) (#2454) (#2199) (#2371) (#2272) (#2230) (#2415) (#2426) (#2465)

  The qml.qchem module provides a differentiable Hartree-Fock solver and the functionality to construct a fully-differentiable molecular Hamiltonian.

  For example, one can continue to generate molecular Hamiltonians using qml.qchem.molecular_hamiltonian:

  symbols = ["H", "H"]
  geometry = np.array([[0., 0., -0.66140414], [0., 0., 0.66140414]])
  hamiltonian, qubits = qml.qchem.molecular_hamiltonian(symbols, geometry, method="dhf")

  By default, this will use the differentiable Hartree-Fock solver; however, simply set method="pyscf" to continue to use PySCF for Hartree-Fock calculations.

• Functions are added for building a differentiable dipole moment observable. Functions for computing multipole moment molecular integrals, needed for building the dipole moment observable, are also added. (#2173) (#2166)

  The dipole moment observable can be constructed using qml.qchem.dipole_moment:

  symbols  = ['H', 'H']
  geometry = np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 1.0]])
  mol = qml.qchem.Molecule(symbols, geometry)
  args = [geometry]
  D = qml.qchem.dipole_moment(mol)(*args)

• The efficiency of computing molecular integrals and Hamiltonian is improved. This has been done by adding optimized functions for building fermionic and qubit observables and optimizing the functions used for computing the electron repulsion integrals. (#2316)

• The 6-31G basis set is added to the qchem basis set repo. This addition allows performing differentiable Hartree-Fock calculations with basis sets beyond the minimal sto-3g basis set for atoms with atomic number 1-10. (#2372)

  The 6-31G basis set can be used to construct a Hamiltonian as

  symbols = ["H", "H"]
  geometry = np.array([[0.0, 0.0, 0.0], [0.0, 0.0, 1.0]])
  H, qubits = qml.qchem.molecular_hamiltonian(symbols, geometry, basis="6-31g")

• External dependencies are replaced with local functions for spin and particle number observables. (#2197) (#2362)

Pattern matching optimization 🔎 💎

• Added an optimization transform that matches pieces of user-provided identity templates in a circuit and replaces them with an equivalent component. (#2032)

  For example, consider the following circuit where we want to replace sequence of two pennylane.S gates with a pennylane.PauliZ gate.

  def circuit():
      qml.S(wires=0)
      qml.PauliZ(wires=0)
      qml.S(wires=1)
      qml.CZ(wires=[0, 1])
      qml.S(wires=1)
      qml.S(wires=2)
      qml.CZ(wires=[1, 2])
      qml.S(wires=2)
      return qml.expval(qml.PauliX(wires=0))

  We specify use the following pattern that implements the identity:

  with qml.tape.QuantumTape() as pattern:
      qml.S(wires=0)
      qml.S(wires=0)
      qml.PauliZ(wires=0)

  To optimize the circuit with this identity pattern, we apply the qml.transforms.pattern_matching transform.

  >>> dev = qml.device('default.qubit', wires=5)
  >>> qnode = qml.QNode(circuit, dev)
  >>> optimized_qfunc = qml.transforms.pattern_matching_optimization(pattern_tapes=[pattern])(circuit)
  >>> optimized_qnode = qml.QNode(optimized_qfunc, dev)
  >>> print(qml.draw(qnode)())
  0: ──S──Z─╭C──────────┤  <X>
  1: ──S────╰Z──S─╭C────┤
  2: ──S──────────╰Z──S─┤
  >>> print(qml.draw(optimized_qnode)())
  0: ──S⁻¹─╭C────┤  <X>
  1: ──Z───╰Z─╭C─┤
  2: ──Z──────╰Z─┤

  For more details on using pattern matching optimization you can check the corresponding documentation and also the following paper.

Measure the distance between two unitaries📏

• Added the HilbertSchmidt and the LocalHilbertSchmidt templates to be used for computing distance measures between unitaries. (#2364)

  Given a unitary U, qml.HilberSchmidt can be used to measure the distance between unitaries and to define a cost function (cost_hst) used for learning a unitary V that is equivalent to U up to a global phase:

  # Represents unitary U
  with qml.tape.QuantumTape(do_queue=False) as u_tape:
      qml.Hadamard(wires=0)
  
  # Represents unitary V
  def v_function(params):
      qml.RZ(params[0], wires=1)
  
  @qml.qnode(dev)
  def hilbert_test(v_params, v_function, v_wires, u_tape):
      qml.HilbertSchmidt(v_params, v_function=v_function, v_wires=v_wires, u_tape=u_tape)
      return qml.probs(u_tape.wires + v_wires)
  
  def cost_hst(parameters, v_function, v_wires, u_tape):
      return (1 - hilbert_test(v_params=parameters, v_function=v_function, v_wires=v_wires, u_tape=u_tape)[0])

  >>> cost_hst(parameters=[0.1], v_function=v_function, v_wires=[1], u_tape=u_tape)
  tensor(0.999, requires_grad=True)

  For more information refer to the documentation of qml.HilbertSchmidt.

More tensor network support 🕸️

• Adds the qml.MERA template for implementing quantum circuits with the shape of a multi-scale entanglement renormalization ansatz (MERA). (#2418)

  MERA follows the style of previous tensor network templates and is similar to quantum convolutional neural networks.

    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_wires = 4
    n_block_wires = 2
    n_params_block = 2
    n_blocks = qml.MERA.get_n_blocks(range(n_wires),n_block_wires)
    template_weights = [[0.1,-0.3]]*n_blocks
  
    dev= qml.device('default.qubit',wires=range(n_wires))
    @qml.qnode(dev)
    def circuit(template_weights):
        qml.MERA(range(n_wires),n_block_wires,block, n_params_block, template_weights)
        return qml.expval(qml.PauliZ(wires=1))

  It may be necessary to reorder the wires to see the MERA architecture clearly:

  >>> print(qml.draw(circuit,expansion_strategy='device',wire_order=[2,0,1,3])(template_weights))
  2: ───────────────╭C──RY(0.10)──╭X──RY(-0.30)───────────────┤
  0: ─╭X──RY(-0.30)─│─────────────╰C──RY(0.10)──╭C──RY(0.10)──┤
  1: ─╰C──RY(0.10)──│─────────────╭X──RY(-0.30)─╰X──RY(-0.30)─┤  <Z>
  3: ───────────────╰X──RY(-0.30)─╰C──RY(0.10)────────────────┤

New transform for transpilation ⚙️

• Added a swap based transpiler transform. (#2118)

  The transpile function takes a quantum function and a coupling map as inputs and compiles the circuit to ensure that it can be executed on corresponding hardware. The transform can be used as a decorator in the following way:

  dev = qml.device('default.qubit', wires=4)
  
  @qml.qnode(dev)
  @qml.transforms.transpile(coupling_map=[(0, 1), (1, 2), (2, 3)])
  def circuit(param):
      qml.CNOT(wires=[0, 1])
      qml.CNOT(wires=[0, 2])
      qml.CNOT(wires=[0, 3])
      qml.PhaseShift(param, wires=0)
      return qml.probs(wires=[0, 1, 2, 3])

  >>> print(qml.draw(circuit)(0.3))
  0: ─╭C───────╭C──────────╭C──Rϕ(0.30)─┤ ╭Probs
  1: ─╰X─╭SWAP─╰X────╭SWAP─╰X───────────┤ ├Probs
  2: ────╰SWAP─╭SWAP─╰SWAP──────────────┤ ├Probs
  3: ──────────╰SWAP────────────────────┤ ╰Probs

Improvements

• QuantumTape objects are now iterable, allowing iteration over the contained operations and measurements. (#2342)

  with qml.tape.QuantumTape() as tape:
      qml.RX(0.432, wires=0)
      qml.RY(0.543, wires=0)
      qml.CNOT(wires=[0, 'a'])
      qml.RX(0.133, wires='a')
      qml.expval(qml.PauliZ(wires=[0]))

  Given a QuantumTape object the underlying quantum circuit can be iterated over using a for loop:

  >>> for op in tape:
  ...     print(op)
  RX(0.432, wires=[0])
  RY(0.543, wires=[0])
  CNOT(wires=[0, 'a'])
  RX(0.133, wires=['a'])
  expval(PauliZ(wires=[0]))

  Indexing into the circuit is also allowed via tape[i]:

  >>> tape[0]
  RX(0.432, wires=[0])

  A tape object can also be converted to a sequence (e.g., to a list) of operations and measurements:

  >>> list(tape)
  [RX(0.432, wires=[0]),
   RY(0.543, wires=[0]),
   CNOT(wires=[0, 'a']),
   RX(0.133, wires=['a']),
   expval(PauliZ(wires=[0]))]

• Added the QuantumTape.shape method and QuantumTape.numeric_type attribute to allow extracting information about the shape and numeric type of the output returned by a quantum tape after execution. (#2044)

  dev = qml.device("default.qubit", wires=2)
  a = np.array([0.1, 0.2, 0.3])
  
  def func(a):
      qml.RY(a[0], wires=0)
      qml.RX(a[1], wires=0)
      qml.RY(a[2], wires=0)
  
  with qml.tape.QuantumTape() as tape:
      func(a)
      qml.state()

  >>> tape.shape(dev)
  (1, 4)
  >>> tape.numeric_type
  complex

• Defined a MeasurementProcess.shape method and a MeasurementProcess.numeric_type attribute to allow extracting information about the shape and numeric type of results obtained when evaluating QNodes using the specific measurement process. (#2044)

• The parameter-shift Hessian can now be computed for arbitrary operations that support the general parameter-shift rule for gradients, using qml.gradients.param_shift_hessian (#2319)

  Multiple ways to obtain the gradient recipe are supported, in the following order of preference:

  • A custom grad_recipe. It is iterated to obtain the shift rule for the second-order derivatives in the diagonal entries of the Hessian.

  • Custom parameter_frequencies. The second-order shift rule can directly be computed using them.

  • An operation's generator. Its eigenvalues will be used to obtain parameter_frequencies, if they are not given explicitly for an operation.

• The strategy for expanding a circuit can now be specified with the qml.specs transform, for example to calculate the specifications of the
  circuit that will actually be executed by the device (expansion_strategy="device"). (#2395)

• The default.qubit and default.mixed devices now skip over identity operators instead of performing matrix multiplication with the identity. (#2356) (#2365)

• The function qml.eigvals is modified to use the efficient scipy.sparse.linalg.eigsh method for obtaining the eigenvalues of a SparseHamiltonian. This scipy method is called to compute :math:k eigenvalues of a sparse :math:N \times N matrix if k is smaller than :math:N-1. If a larger :math:k is requested, the dense matrix representation of the Hamiltonian is constructed and the regular qml.math.linalg.eigvalsh is applied. (#2333)

• The function qml.ctrl was given the optional argument control_values=None. If overridden, control_values takes an integer or a list of integers corresponding to the binary value that each control value should take. The same change is reflected in ControlledOperation. Control values of 0 are implemented by qml.PauliX applied before and after the controlled operation (#2288)

• Operators now have a has_matrix property denoting whether or not the operator defines a matrix. (#2331) (#2476)

• Circuit cutting now performs expansion to search for wire cuts in contained operations or tapes. (#2340)

• The qml.draw and qml.draw_mpl transforms are now located in the drawer module. They can still be accessed via the top-level qml namespace. (#2396)

• Raise a warning where caching produces identical shot noise on execution results with finite shots. (#2478)

Deprecations

• The ObservableReturnTypes Sample, Variance, Expectation, Probability, State, and MidMeasure have been moved to measurements from operation. (#2329) (#2481)

Breaking changes

• The caching ability of devices has been removed. Using the caching on the QNode level is the recommended alternative going forward. (#2443)

  One way for replicating the removed QubitDevice caching behaviour is by creating a cache object (e.g., a dictionary) and passing it to the QNode:

  n_wires = 4
  wires = range(n_wires)
  
  dev = qml.device('default.qubit', wires=n_wires)
  
  cache = {}
  
  @qml.qnode(dev, diff_method='parameter-shift', cache=cache)
  def expval_circuit(params):
      qml.templates.BasicEntanglerLayers(params, wires=wires, rotation=qml.RX)
      return qml.expval(qml.PauliZ(0) @ qml.PauliY(1) @ qml.PauliX(2) @ qml.PauliZ(3))
  
  shape = qml.templates.BasicEntanglerLayers.shape(5, n_wires)
  params = np.random.random(shape)

  >>> expval_circuit(params)
  tensor(0.20598436, requires_grad=True)
  >>> dev.num_executions
  1
  >>> expval_circuit(params)
  tensor(0.20598436, requires_grad=True)
  >>> dev.num_executions
  1

• The qml.finite_diff function has been removed. Please use qml.gradients.finite_diff to compute the gradient of tapes of QNodes. Otherwise, manual implementation is required. (#2464)

• The get_unitary_matrix transform has been removed, please use qml.matrix instead. (#2457)

• The update_stepsize method has been removed from GradientDescentOptimizer and its child optimizers. The stepsize property can be interacted with directly instead. (#2370)

• Most optimizers no longer flatten and unflatten arguments during computation. Due to this change, user provided gradient functions must return the same shape as qml.grad. (#2381)

• The old circuit text drawing infrastructure has been removed. (#2310)

  • RepresentationResolver was replaced by the Operator.label method.
  • qml.drawer.CircuitDrawer was replaced by qml.drawer.tape_text.
  • qml.drawer.CHARSETS was removed because unicode is assumed to be accessible.
  • Grid and qml.drawer.drawable_grid were removed because the custom data class was replaced by list of sets of operators or measurements.
  • qml.transforms.draw_old was replaced by qml.draw.
  • qml.CircuitGraph.greedy_layers was deleted, as it was no longer needed by the circuit drawer and did not seem to have uses outside of that situation.
  • qml.CircuitGraph.draw was deleted, as we draw tapes instead.
  • The tape method qml.tape.QuantumTape.draw now simply calls qml.drawer.tape_text.
  • In the new pathway, the charset keyword was deleted, the max_length keyword defaults to 100, and the decimals and show_matrices keywords were added.

• The deprecated QNode, available via qml.qnode_old.QNode, has been removed. Please transition to using the standard qml.QNode. (#2336) (#2337) (#2338)

  In addition, several other components which powered the deprecated QNode have been removed:

  • The deprecated, non-batch compatible interfaces, have been removed.

  • The deprecated tape subclasses QubitParamShiftTape, JacobianTape, CVParamShiftTape, and ReversibleTape have been removed.

• The deprecated tape execution method tape.execute(device) has been removed. Please use qml.execute([tape], device) instead. (#2339)

Bug fixes

• Fixed a bug in the qml.PauliRot operation, where computing the generator was not taking into account the operation wires. (#2466)

• Fixed a bug where non-trainable arguments were shifted in the NesterovMomentumOptimizer if a trainable argument was after it in the argument list. (#2466)

• Fixed a bug with @jax.jit for grad when diff_method="adjoint" and mode="backward". (#2460)

• Fixed a bug where qml.DiagonalQubitUnitary did not support @jax.jit and @tf.function. (#2445)

• Fixed a bug in the qml.PauliRot operation, where computing the generator was not taking into account the operation wires. (#2442)

• Fixed a bug with the padding capability of AmplitudeEmbedding where the inputs are on the GPU. (#2431)

• Fixed a bug by adding a comprehensible error message for calling qml.probs without passing wires or an observable. (#2438)

• The behaviour of qml.about() was modified to avoid warnings being emitted due to legacy behaviour of pip. (#2422)

• Fixed a bug where observables were not considered when determining the use of the jax-jit interface. (#2427) (#2474)

• Fixed a bug where computing statistics for a relatively few number of shots (e.g., shots=10), an error arose due to indexing into an array using a DeviceArray. (#2427)

• PennyLane Lightning version in Docker container is pulled from latest wheel-builds. (#2416)

• Optimizers only consider a variable trainable if they have requires_grad = True. (#2381)

• Fixed a bug with qml.expval, qml.var, qml.state and qml.probs (when qml.probs is the only measurement) where the dtype specified on the device did not match the dtype of the QNode output. (#2367)

• Fixed a bug where the output shapes from batch transforms are inconsistent with the QNode output shape. (#2215)

• Fixed a bug caused by the squeezing in qml.gradients.param_shift_hessian. (#2215)

• Fixed a bug in which the expval/var of a Tensor(Observable) would depend on the order in which the observable is defined: (#2276)

  >>> @qml.qnode(dev)
  ... def circ(op):
  ...   qml.RX(0.12, wires=0)
  ...   qml.RX(1.34, wires=1)
  ...   qml.RX(3.67, wires=2)
  ...   return qml.expval(op)
  >>> op1 = qml.Identity(wires=0) @ qml.Identity(wires=1) @ qml.PauliZ(wires=2)
  >>> op2 = qml.PauliZ(wires=2) @ qml.Identity(wires=0) @ qml.Identity(wires=1)
  >>> print(circ(op1), circ(op2))
  -0.8636111153905662 -0.8636111153905662

• Fixed a bug where qml.hf.transform_hf() would fail due to missing wires in the qubit operator that is prepared for tapering the HF state. (#2441)

• Fixed a bug with custom device defined jacobians not being returned properly. (#2485)

Documentation

• The sections on adding operator and observable support in the "How to add a plugin" section of the plugins page have been updated. (#2389)

• The missing arXiv reference in the LieAlgebra optimizer has been fixed. (#2325)

Contributors

This release contains contributions from (in alphabetical order):

Karim Alaa El-Din, Guillermo Alonso-Linaje, Juan Miguel Arrazola, Ali Asadi, Utkarsh Azad, Sam Banning, Thomas Bromley, Alain Delgado, Isaac De Vlugt, Olivia Di Matteo, Amintor Dusko, Anthony Hayes, David Ittah, Josh Izaac, Soran Jahangiri, Nathan Killoran, Christina Lee, Angus Lowe, Romain Moyard, Zeyue Niu, Matthew Silverman, Lee James O'Riordan, Maria Schuld, Jay Soni, Antal Száva, Maurice Weber, David Wierichs.