Release 0.35.1

Bug fixes 🐛

• Lightning simulators need special handling of diagonalizing gates when performing sampling measurements. (#5343)

• Updated the lower bound on the required Catalyst version to v0.5.0. (#5320)

Contributors ✍️

This release contains contributions from (in alphabetical order):

Vincent Michaud-Rioux, Erick Ochoa Lopez.

Release 0.35.0

New features since last release

Qiskit 1.0 integration 🔌

• This version of PennyLane makes it easier to import circuits from Qiskit. (#5218) (#5168)

  The qml.from_qiskit function converts a Qiskit QuantumCircuit into a PennyLane quantum function. Although qml.from_qiskit already exists in PennyLane, we have made a number of improvements to make importing from Qiskit easier. And yes — qml.from_qiskit functionality is compatible with both Qiskit 1.0 and earlier versions! Here's a comprehensive list of the improvements:

  • You can now append PennyLane measurements onto the quantum function returned by qml.from_qiskit. Consider this simple Qiskit circuit:

    import pennylane as qml 
    from qiskit import QuantumCircuit
    
    qc = QuantumCircuit(2) 
    qc.rx(0.785, 0) 
    qc.ry(1.57, 1) 

    We can convert it into a PennyLane QNode in just a few lines, with PennyLane measurements easily included:

    >>> dev = qml.device("default.qubit") 
    >>> measurements = qml.expval(qml.Z(0) @ qml.Z(1)) 
    >>> qfunc = qml.from_qiskit(qc, measurements=measurements) 
    >>> qnode = qml.QNode(qfunc, dev) 
    >>> qnode() 
    tensor(0.00056331, requires_grad=True) 

  • Quantum circuits that already contain Qiskit-side measurements can be faithfully converted with qml.from_qiskit. Consider this example Qiskit circuit:

    qc = QuantumCircuit(3, 2) # Teleportation
    
    qc.rx(0.9, 0) # Prepare input state on qubit 0
    
    qc.h(1) # Prepare Bell state on qubits 1 and 2 qc.cx(1, 2)
    
    qc.cx(0, 1) # Perform teleportation 
    qc.h(0) 
    qc.measure(0, 0) 
    qc.measure(1, 1)
    
    with qc.if_test((1, 1)): # Perform first conditional 
        qc.x(2) 

    This circuit can be converted into PennyLane with the Qiskit measurements still accessible. For example, we can use those results as inputs to a mid-circuit measurement in PennyLane:

    @qml.qnode(dev) 
    def teleport(): 
        m0, m1 = qml.from_qiskit(qc)() 
        qml.cond(m0, qml.Z)(2) 
        return qml.density_matrix(2) 

    >>> teleport() 
    tensor([[0.81080498+0.j , 0. +0.39166345j], 
    [0. -0.39166345j, 0.18919502+0.j ]], requires_grad=True) 

  • It is now more intuitive to handle and differentiate parametrized Qiskit circuits. Consider the following circuit:

    from qiskit.circuit import Parameter 
    from pennylane import numpy as np
    
    angle0 = Parameter("x") angle1 = Parameter("y")
    
    qc = QuantumCircuit(2, 2) 
    qc.rx(angle0, 0) 
    qc.ry(angle1, 1) 
    qc.cx(1, 0) 

    We can convert this circuit into a QNode with two arguments, corresponding to x and y:

    measurements = qml.expval(qml.PauliZ(0)) 
    qfunc = qml.from_qiskit(qc, measurements) 
    qnode = qml.QNode(qfunc, dev) 

    The QNode can be evaluated and differentiated:

    >>> x, y = np.array([0.4, 0.5], requires_grad=True) 
    >>> qnode(x, y) 
    tensor(0.80830707, requires_grad=True) 
    
    >>> qml.grad(qnode)(x, y) 
    (tensor(-0.34174675, requires_grad=True), tensor(-0.44158016, requires_grad=True)) 

    This shows how easy it is to make a Qiskit circuit differentiable with PennyLane.

  • In addition to circuits, it is also possible to convert operators from Qiskit to PennyLane with a new function called qml.from_qiskit_op. (#5251)

    A Qiskit SparsePauliOp can be converted to a PennyLane operator using qml.from_qiskit_op:

    >>> from qiskit.quantum_info import SparsePauliOp 
    >>> qiskit_op = SparsePauliOp(["II", "XY"]) 
    >>> qiskit_op SparsePauliOp(['II', 'XY'], coeffs=[1.+0.j, 1.+0.j]) 
    >>> pl_op = qml.from_qiskit_op(qiskit_op) 
    >>> pl_op I(0) + X(1) @ Y(0) 

    Combined with qml.from_qiskit, it becomes easy to quickly calculate quantities like expectation values by converting the whole workflow to PennyLane:

    qc = QuantumCircuit(2) # Create circuit 
    qc.rx(0.785, 0) 
    qc.ry(1.57, 1)
    
    measurements = qml.expval(pl_op) # Create QNode 
    qfunc = qml.from_qiskit(qc, measurements) 
    qnode = qml.QNode(qfunc, dev) 

    >>> qnode() # Evaluate! 
    tensor(0.29317504, requires_grad=True) 

Native mid-circuit measurements on Default Qubit 💡

• Mid-circuit measurements can now be more scalable and efficient in finite-shots mode with default.qubit by simulating them in a similar way to what happens on quantum hardware. (#5088) (#5120)

  Previously, mid-circuit measurements (MCMs) would be automatically replaced with an additional qubit using the @qml.defer_measurements transform. The circuit below would have required thousands of qubits to simulate.

  Now, MCMs are performed in a similar way to quantum hardware with finite shots on default.qubit. For each shot and each time an MCM is encountered, the device evaluates the probability of projecting onto |0> or |1> and makes a random choice to collapse the circuit state. This approach works well when there are a lot of MCMs and the number of shots is not too high.

  import pennylane as qml
  
  dev = qml.device("default.qubit", shots=10)
  
  @qml.qnode(dev) 
  def f(): 
      for i in range(1967): 
          qml.Hadamard(0) 
          qml.measure(0) 
      return qml.sample(qml.PauliX(0)) 

  >>> f() 
  tensor([-1, -1, -1, 1, 1, -1, 1, -1, 1, -1], requires_grad=True) 

Work easily and efficiently with operators 🔧

• Over the past few releases, PennyLane's approach to operator arithmetic has been in the process of being overhauled. We have a few objectives:

  1. To make it as easy to work with PennyLane operators as it would be with pen and paper.
  2. To improve the efficiency of operator arithmetic.

  The updated operator arithmetic functionality is still being finalized, but can be activated using qml.operation.enable_new_opmath(). In the next release, the new behaviour will become the default, so we recommend enabling now to become familiar with the new system!

  The following updates have been made in this version of PennyLane:

  • You can now easily access Pauli operators via I, X, Y, and Z: (#5116)

    >>> from pennylane import I, X, Y, Z 
    >>> X(0) X(0) ```
    
    The original long-form names `Identity`, `PauliX`, `PauliY`, and `PauliZ` remain available, but use of the short-form names is now recommended.
    

  • A new qml.commutator function is now available that allows you to compute commutators between PennyLane operators. (#5051) (#5052) (#5098)

    >>> qml.commutator(X(0), Y(0)) 
    2j * Z(0) 

  • Operators in PennyLane can have a backend Pauli representation, which can be used to perform faster operator arithmetic. Now, the Pauli representation will be automatically used for calculations when available. (#4989) (#5001) (#5003) (#5017) (#5027)

    The Pauli representation can be optionally accessed via op.pauli_rep:

    >>> qml.operation.enable_new_opmath() 
    >>> op = X(0) + Y(0) 
    >>> op.pauli_rep 
    1.0 * X(0) + 1.0 * Y(0) 

  • Extensive improvements have been made to the string representations of PennyLane operators, making them shorter and possible to copy-paste as valid PennyLane code. (#5116) (#5138)

    >>> 0.5 * X(0) 
    0.5 * X(0)
    >>> 0.5 * (X(0) + Y(1)) 
    0.5 * (X(0) + Y(1)) 
    

    Sums with many terms are broken up into multiple lines, but can still be copied back as valid code:

    >>> 0.5 * (X(0) @ X(1)) + 0.7 * (X(1) @ X(2)) + 0.8 * (X(2) @ X(3)) 
    ( 
         0.5 * (X(0) @ X(1)) 
         + 0.7 * (X(1) @ X(2)) 
         + 0.8 * (X(2) @ X(3)) 
    ) 
    

  • Linear combinations of operators and operator multiplication via Sum and Prod, respectively, have been updated to reach feature parity with Hamiltonian and Tensor, respectively. This should minimize the effort to port over any existing code. (#5070) (#5132) (#5133)

    Updates include support for grouping via the pauli module:

    >>> obs = [X(0) @ Y(1), Z(0), Y(0) @ Z(1), Y(1)] 
    >>> qml.pauli.group_observables(obs) 
    [[Y(0) @ Z(1)], [X(0) @ Y(1), Y(1)], [Z(0)]] 

New Clifford device 🦾

• A new default.clifford device enables efficient simulati...

Release 0.34.0.post1

This postfix release pins additional requirements in doc/requirements.txt for building the website documentation. This allows the website to be rebuilt to show the "Getting involved" section.

Release 0.34.0

New features since last release

Statistics and drawing for mid-circuit measurements 🎨

• It is now possible to return statistics of composite mid-circuit measurements. (#4888)

  Mid-circuit measurement results can be composed using basic arithmetic operations and then statistics can be calculated by putting the result within a PennyLane measurement like qml.expval(). For example:

  import pennylane as qml
  
  dev = qml.device("default.qubit")
  
  @qml.qnode(dev) 
  def circuit(phi, theta): 
      qml.RX(phi, wires=0) 
      m0 = qml.measure(wires=0) 
      qml.RY(theta, wires=1) 
      m1 = qml.measure(wires=1) 
      return qml.expval(~m0 + m1)
  
  print(circuit(1.23, 4.56)) 

  1.2430187928114291 
  

  Another option, for ease-of-use when using qml.sample(), qml.probs(), or qml.counts(), is to provide a simple list of mid-circuit measurement results:

  dev = qml.device("default.qubit")
  
  @qml.qnode(dev) 
  def circuit(phi, theta): 
      qml.RX(phi, wires=0) 
      m0 = qml.measure(wires=0) 
      qml.RY(theta, wires=1) 
      m1 = qml.measure(wires=1) 
      return qml.sample(op=[m0, m1])
  
  print(circuit(1.23, 4.56, shots=5)) 

  [[0 1] 
   [0 1] 
   [0 0] 
   [1 0] 
   [0 1]] 
  

  Composite mid-circuit measurement statistics are supported on default.qubit and default.mixed. To learn more about which measurements and arithmetic operators are supported, refer to the measurements page and the documentation for qml.measure.

• Mid-circuit measurements can now be visualized with the text-based qml.draw() and the graphical qml.draw_mpl() methods. (#4775) (#4803) (#4832) (#4901) (#4850) (#4917) (#4930) (#4957)

  Drawing of mid-circuit measurement capabilities including qubit reuse and reset, postselection, conditioning, and collecting statistics is now supported. Here is an all-encompassing example:

  def circuit(): 
      m0 = qml.measure(0, reset=True) 
      m1 = qml.measure(1, postselect=1) 
      qml.cond(m0 - m1 == 0, qml.S)(0) 
      m2 = qml.measure(1) 
      qml.cond(m0 + m1 == 2, qml.T)(0) 
      qml.cond(m2, qml.PauliX)(1) 

  The text-based drawer outputs:

  >>> print(qml.draw(circuit)()) 
  0: ──┤↗│  │0⟩────────S───────T────┤ 
  1: ───║────────┤↗₁├──║──┤↗├──║──X─┤ 
        ╚═════════║════╬═══║═══╣  ║ 
                  ╚════╩═══║═══╝  ║ 
                           ╚══════╝ 

  The graphical drawer outputs:

  >>> print(qml.draw_mpl(circuit)()) 

Catalyst is seamlessly integrated with PennyLane ⚗️

• Catalyst, our next-generation compilation framework, is now accessible within PennyLane, allowing you to more easily benefit from hybrid just-in-time (JIT) compilation.

  To access these features, simply install pennylane-catalyst:

  pip install pennylane-catalyst 
  

  The qml.compiler module provides support for hybrid quantum-classical compilation. (#4692) (#4979)

  Through the use of the qml.qjit decorator, entire workflows can be JIT compiled — including both quantum and classical processing — down to a machine binary on first-function execution. Subsequent calls to the compiled function will execute the previously-compiled binary, resulting in significant performance improvements.

  import pennylane as qml
  
  dev = qml.device("lightning.qubit", wires=2)
  
  @qml.qjit 
  @qml.qnode(dev) 
  def circuit(theta): 
      qml.Hadamard(wires=0) 
      qml.RX(theta, wires=1) 
      qml.CNOT(wires=[0,1]) 
      return qml.expval(qml.PauliZ(wires=1)) 

  >>> circuit(0.5) # the first call, compilation occurs here array(0.) 
  >>> circuit(0.5) # the precompiled quantum function is called 
  array(0.) 

  Currently, PennyLane supports the Catalyst hybrid compiler with the qml.qjit decorator. A significant benefit of Catalyst is the ability to preserve complex control flow around quantum operations — such as if statements and for loops, and including measurement feedback — during compilation, while continuing to support end-to-end autodifferentiation.

• The following functions can now be used with the qml.qjit decorator: qml.grad, qml.jacobian, qml.vjp, qml.jvp, and qml.adjoint. (#4709) (#4724) (#4725) (#4726)

  When qml.grad or qml.jacobian are used with @qml.qjit, they are patched to catalyst.grad and catalyst.jacobian, respectively.

  dev = qml.device("lightning.qubit", wires=1)
  
  @qml.qjit 
  def workflow(x):
  
      @qml.qnode(dev) 
      def circuit(x): 
          qml.RX(np.pi * x[0], wires=0) 
          qml.RY(x[1], wires=0) 
          return qml.probs()
  
      g = qml.jacobian(circuit)
  
      return g(x) 

  >>> workflow(np.array([2.0, 1.0])) 
  array([[ 3.48786850e-16, -4.20735492e-01], 
         [-8.71967125e-17, 4.20735492e-01]]) 

• JIT-compatible functionality for control flow has been added via qml.for_loop, qml.while_loop, and qml.cond. (#4698)

  qml.for_loop and qml.while_loop can be deployed as decorators on functions that are the body of the loop. The arguments to both follow typical conventions:

  @qml.for_loop(lower_bound, upper_bound, step) 
  

  @qml.while_loop(cond_function) 
  

  Here is a concrete example with qml.for_loop:

  dev = qml.device("lightning.qubit", wires=1)
  
  @qml.qjit 
  @qml.qnode(dev) 
  def circuit(n: int, x: float):
  
      @qml.for_loop(0, n, 1) 
      def loop_rx(i, x): 
          # perform some work and update (some of) the arguments 
          qml.RX(x, wires=0)
  
          # update the value of x for the next iteration 
          return jnp.sin(x)
  
      # apply the for loop 
      final_x = loop_rx(x)
  
      return qml.expval(qml.PauliZ(0)), final_x 

  >>> circuit(7, 1.6) 
  (array(0.97926626), array(0.55395718)) 

Decompose circuits into the Clifford+T gateset 🧩

• The new qml.clifford_t_decomposition() transform provides an approximate breakdown of an input circuit into the Clifford+T gateset. Behind the scenes, this decomposition is enacted via the sk_decomposition() function using the Solovay-Kitaev algorithm. (#4801) (#4802)

  The Solovay-Kitaev algorithm approximately decomposes a quantum circuit into the Clifford+T gateset. To account for this, a desired total circuit decomposition error, epsilon, must be specified when using qml.clifford_t_decomposition:

  dev = qml.device("default.qubit")
  
  @qml.qnode(dev) 
  def circuit(): 
      qml.RX(1.1, 0) 
      return qml.state()
  
  circuit = qml.clifford_t_decomposition(circuit, epsilon=0.1) 

  >>> print(qml.draw(circuit)()) 
  0:   ──T†──H──T†──H──T──H──T──H──T──H──T──H──T†──H──T†──T†──H──T†──H──T──H──T──H──T──H──T──H──T†──H
  
   ───T†──H──T──H──GlobalPhase(0.39)─┤ 

  The resource requirements of this circuit can also be evaluated:

  >>> with qml.Tracker(dev) as tracker: 
  ...     circuit() 
  >>> resources_lst = tracker.history["resources"] 
  >>> resources_lst[0] 
  wires: 1 
  gates: 34 
  depth: 34 
  shots: Shots(total=None) 
  gate_types: {'Adjoint(T)': 8, 'Hadamard': 16, 'T': 9, 'GlobalPhase': 1} 
  gate_sizes:   {1: 33, 0: 1} 

Use an iterative approach for quantum phase estimation 🔄

• Iterative Quantum Phase Estimation is now available with qml.iterative_qpe. (#4804)

  The subroutine can be used similarly to mid-circuit measurements:

  import pennylane as qml
  
  dev = qml.device("default.qubit", shots=5)
  
  @qml.qnode(dev) 
  def circuit():
  
        # Initial state 
        qml.PauliX(wires=[0])
  
        # Iterative QPE 
        measurements = qml.iterative_qpe(qml.RZ(2., wires=[0]), ancilla=[1], iters=3)
  
        return [qml.sample(op=meas) for meas in measurements] 

  >>> print(circuit()) 
  [array([0, 0, 0, 0, 0]), array([1, 0, 0, 0, 0]), array([0, 1, 1, 1...

Release 0.33.1

Bug fixes 🐛

• Fix gradient performance regression due to expansion of VJP products. (#4806)

• qml.defer_measurements now correctly transforms circuits when terminal measurements include wires used in mid-circuit measurements. (#4787)

• Any ScalarSymbolicOp, like Evolution, now states that it has a matrix if the target is a Hamiltonian. (#4768)

• In default.qubit, initial states are now initialized with the simulator's wire order, not the circuit's wire order. (#4781)

Contributors ✍️

This release contains contributions from (in alphabetical order):

Christina Lee, Lee James O'Riordan, Mudit Pandey

Release 0.33.0

New features since last release

Postselection and statistics in mid-circuit measurements 📌

• It is now possible to request postselection on a mid-circuit measurement. (#4604)

  This can be achieved by specifying the postselect keyword argument in qml.measure as either 0 or 1, corresponding to the basis states.

  import pennylane as qml
  
  dev = qml.device("default.qubit")
  
  @qml.qnode(dev, interface=None)
  def circuit():
      qml.Hadamard(wires=0)
      qml.CNOT(wires=[0, 1])
      qml.measure(0, postselect=1)
      return qml.expval(qml.PauliZ(1)), qml.sample(wires=1)
  

  This circuit prepares the $| \Phi^{+} \rangle$ Bell state and postselects on measuring $|1\rangle$ in wire 0. The output of wire 1 is then also $|1\rangle$ at all times:

  >>> circuit(shots=10)
  (-1.0, array([1, 1, 1, 1, 1, 1]))
  

  Note that the number of shots is less than the requested amount because we have thrown away the samples where $|0\rangle$ was measured in wire 0.

• Measurement statistics can now be collected for mid-circuit measurements. (#4544)

  dev = qml.device("default.qubit")
  
  @qml.qnode(dev)
  def circ(x, y):
      qml.RX(x, wires=0)
      qml.RY(y, wires=1)
      m0 = qml.measure(1)
      return qml.expval(qml.PauliZ(0)), qml.expval(m0), qml.sample(m0)
  

  >>> circ(1.0, 2.0, shots=10000)
  (0.5606, 0.7089, array([0, 1, 1, ..., 1, 1, 1]))
  

  Support is provided for both finite-shot and analytic modes and devices default to using the deferred measurement principle to enact the mid-circuit measurements.

Exponentiate Hamiltonians with flexible Trotter products 🐖

• Higher-order Trotter-Suzuki methods are now easily accessible through a new operation called TrotterProduct. (#4661)

  Trotterization techniques are an affective route towards accurate and efficient Hamiltonian simulation. The Suzuki-Trotter product formula allows for the ability to express higher-order approximations to the matrix exponential of a Hamiltonian, and it is now available to use in PennyLane via the TrotterProduct operation. Simply specify the order of the approximation and the evolution time.

  coeffs = [0.25, 0.75]
  ops = [qml.PauliX(0), qml.PauliZ(0)]
  H = qml.dot(coeffs, ops)
  
  dev = qml.device("default.qubit", wires=2)
  
  @qml.qnode(dev)
  def circuit():
      qml.Hadamard(0)
      qml.TrotterProduct(H, time=2.4, order=2)
      return qml.state()

  >>> circuit()
  [-0.13259524+0.59790098j 0. +0.j -0.13259524-0.77932754j 0. +0.j ]

• Approximating matrix exponentiation with random product formulas, qDrift, is now available with the new QDrift operation. (#4671)

  As shown in 1811.08017, qDrift is a Markovian process that can provide a speedup in Hamiltonian simulation. At a high level, qDrift works by randomly sampling from the Hamiltonian terms with a probability that depends on the Hamiltonian coefficients. This method for Hamiltonian simulation is now ready to use in PennyLane with the QDrift operator. Simply specify the evolution time and the number of samples drawn from the Hamiltonian, n:

  coeffs = [0.25, 0.75]
  ops = [qml.PauliX(0), qml.PauliZ(0)]
  H = qml.dot(coeffs, ops)
  
  dev = qml.device("default.qubit", wires=2)
  
  @qml.qnode(dev)
  def circuit():
      qml.Hadamard(0)
      qml.QDrift(H, time=1.2, n = 10)
      return qml.probs()

  >>> circuit()
  array([0.61814334, 0. , 0.38185666, 0. ])

Building blocks for quantum phase estimation 🧱

• A new operator called CosineWindow has been added to prepare an initial state based on a cosine wave function. (#4683)

  As outlined in 2110.09590, the cosine tapering window is part of a modification to quantum phase estimation that can provide a cubic improvement to the algorithm's error rate. Using CosineWindow will prepare a state whose amplitudes follow a cosinusoidal distribution over the computational basis.

  import matplotlib.pyplot as plt
  
  dev = qml.device('default.qubit', wires=4)
  
  @qml.qnode(dev)
  def example_circuit():
      qml.CosineWindow(wires=range(4))
      return qml.state()
  
  output = example_circuit()
  
  plt.style.use("pennylane.drawer.plot")
  plt.bar(range(len(output)), output)
  plt.show()

  

• Controlled gate sequences raised to decreasing powers, a sub-block in quantum phase estimation, can now be created with the new ControlledSequence operator. (#4707)

  To use ControlledSequence, specify the controlled unitary operator and the control wires, control:

  dev = qml.device("default.qubit", wires = 4)
  
  @qml.qnode(dev)
  def circuit():
      for i in range(3):
          qml.Hadamard(wires = i)
      qml.ControlledSequence(qml.RX(0.25, wires = 3), control = [0, 1, 2])
      qml.adjoint(qml.QFT)(wires = range(3))
          return qml.probs(wires = range(3))

  >>> print(circuit())
  [0.92059345 0.02637178 0.00729619 0.00423258 0.00360545 0.00423258 0.00729619 0.02637178]

New device capabilities, integration with Catalyst, and more! ⚗️

• default.qubit now uses the new qml.devices.Device API and functionality in qml.devices.qubit. If you experience any issues with the updated default.qubit, please let us know by posting an issue. The old version of the device is still accessible by the short name default.qubit.legacy, or directly via qml.devices.DefaultQubitLegacy. (#4594) (#4436) (#4620) (#4632)

  This changeover has a number of benefits for default.qubit, including:

  • The number of wires is now optional — simply having qml.device("default.qubit") is valid! If wires are not provided at instantiation, the device automatically infers the required number of wires for each circuit provided for execution.

    dev = qml.device("default.qubit")
    
    @qml.qnode(dev)
    def circuit():
        qml.PauliZ(0)
        qml.RZ(0.1, wires=1)
        qml.Hadamard(2)
        return qml.state()

    >>> print(qml.draw(circuit)())
    0: ──Z────────┤ State
    1: ──RZ(0.10)─┤ State
    2: ──H────────┤ State

  • default.qubit is no longer silently swapped out with an interface-appropriate device when the backpropagation differentiation method is used. For example, consider:

    import jax
    
    dev = qml.device("default.qubit", wires=1)
    
    @qml.qnode(dev, diff_method="backprop")
    def f(x):
        qml.RX(x, wires=0)
        return qml.expval(qml.PauliZ(0))
    f(jax.numpy.array(0.2))

    In previous versions of PennyLane, the device will be swapped for the JAX equivalent:

    >>> f.device
    <DefaultQubitJax device (wires=1, shots=None) at 0x7f8c8bff50a0>
    >>> f.device == dev
    False

    Now, default.qubit can itself dispatch to all the interfaces in a backprop-compatible way and hence does not need to be swapped:

    >>> f.device
    <default.qubit device (wires=1) at 0x7f20d043b040>
    >>> f.device == dev
    True

• A QNode that has been decorated with qjit from PennyLane's Catalyst library for just-in-time hybrid compilation is now compatible with qml.draw. (#4609)

  import catalyst
  
  @catalyst.qjit
  @qml.qnode(qml.device("lightning.qubit", wires=3))
  def circuit(x, y, z, c):
      """A quantum circuit on three wires."""
  
      @catalyst.for_loop(0, c, 1)
      def loop(i):
          qml.Hadamard(wires=i)
  
      qml.RX(x, wires=0)
      loop()
      qml.RY(y, wires=1)
      qml.RZ(z, wires=2)
      return qml.expval(qml.PauliZ(0))
  
  draw = qml.draw(circuit, decimals=None)(1.234, 2.345, 3.456, 1)

  >>> print(draw)
  0: ──RX──H──┤ <Z>
  1: ──H───RY─┤
  2: ──RZ─────┤

Improvements 🛠

More PyTrees!

• MeasurementProcess and QuantumScript objects are now registered as JAX PyTrees. (#4607) (#4608)

  It is now possible to JIT-compile functions with arguments that are a MeasurementProcess or a QuantumScript:

  tape0 = qml.tape.QuantumTape([qml.RX(1.0, 0), qml.RY(0.5, 0)], [qml.expval(qml.PauliZ(0))])
  dev = qml.device('lightning.qubit', wires=5)
  
  execute_kwargs = {"device": dev, "gradient_fn": qml.gradients.param_shift, "interface":"jax"}
  
  jitted_execute = jax.jit(qml.execute, static_argnames=execute_kwargs.keys())
  jitted_execute((tape0, ), **execute_kwargs)

Improving QChem and existing algorithms

• Computationally expensive functions in integrals.py, electron_repulsion and `_h...

Release 0.32.0-post1

This release changes doc/requirements.txt to upgrade jax, jaxlib, and pin ml-dtypes.

Release 0.32.0

New features since last release

Encode matrices using a linear combination of unitaries ⛓️️

• It is now possible to encode an operator A into a quantum circuit by decomposing it into a linear combination of unitaries using PREP (qml.StatePrep) and SELECT (qml.Select) routines. (#4431) (#4437) (#4444) (#4450) (#4506) (#4526)

  Consider an operator A composed of a linear combination of Pauli terms:

  >>> A = qml.PauliX(2) + 2 * qml.PauliY(2) + 3 * qml.PauliZ(2)

  A decomposable block-encoding circuit can be created:

  def block_encode(A, control_wires):
      probs = A.coeffs / np.sum(A.coeffs)
      state = np.pad(np.sqrt(probs, dtype=complex), (0, 1))
      unitaries = A.ops
  
      qml.StatePrep(state, wires=control_wires)
      qml.Select(unitaries, control=control_wires)
      qml.adjoint(qml.StatePrep)(state, wires=control_wires)

  >>> print(qml.draw(block_encode, show_matrices=False)(A, control_wires=[0, 1]))
  0: ─╭|Ψ⟩─╭Select─╭|Ψ⟩†─┤
  1: ─╰|Ψ⟩─├Select─╰|Ψ⟩†─┤
  2: ──────╰Select───────┤

  This circuit can be used as a building block within a larger QNode to perform algorithms such as QSVT and Hamiltonian simulation.

• Decomposing a Hermitian matrix into a linear combination of Pauli words via qml.pauli_decompose is now faster and differentiable. (#4395) (#4479) (#4493)

  def find_coeffs(p):
      mat = np.array([[3, p], [p, 3]])
      A = qml.pauli_decompose(mat)
      return A.coeffs

  >>> import jax
  >>> from jax import numpy as np
  >>> jax.jacobian(find_coeffs)(np.array(2.))
  Array([0., 1.], dtype=float32, weak_type=True)

Monitor PennyLane's inner workings with logging 📃

• Python-native logging can now be enabled with qml.logging.enable_logging(). (#4377) (#4383)

  Consider the following code that is contained in my_code.py:

  import pennylane as qml
  qml.logging.enable_logging() # enables logging
  
  dev = qml.device("default.qubit", wires=2)
  
  @qml.qnode(dev)
  def f(x):
      qml.RX(x, wires=0)
      return qml.state()
  
  f(0.5)

  Executing my_code.py with logging enabled will detail every step in PennyLane's pipeline that gets used to run your code.

  $ python my_code.py
  [1967-02-13 15:18:38,591][DEBUG][<PID 8881:MainProcess>] - pennylane.qnode.__init__()::"Creating QNode(func=<function f at 0x7faf2a6fbaf0>, device=<DefaultQubit device (wires=2, shots=None) at 0x7faf2a689b50>, interface=auto, diff_method=best, expansion_strategy=gradient, max_expansion=10, grad_on_execution=best, mode=None, cache=True, cachesize=10000, max_diff=1, gradient_kwargs={}"
  ...
  

  Additional logging configuration settings can be specified by modifying the contents of the logging configuration file, which can be located by running qml.logging.config_path(). Follow our logging docs page for more details!

More input states for quantum chemistry calculations ⚛️

• Input states obtained from advanced quantum chemistry calculations can be used in a circuit. (#4427) (#4433) (#4461) (#4476) (#4505)

  Quantum chemistry calculations rely on an initial state that is typically selected to be the trivial Hartree-Fock state. For molecules with a complicated electronic structure, using initial states obtained from affordable post-Hartree-Fock calculations helps to improve the efficiency of the quantum simulations. These calculations can be done with external quantum chemistry libraries such as PySCF.

  It is now possible to import a PySCF solver object in PennyLane and extract the corresponding wave function in the form of a state vector that can be directly used in a circuit. First, perform your classical quantum chemistry calculations and then use the qml.qchem.import_state function to import the solver object and return a state vector.

  >>> from pyscf import gto, scf, ci
  >>> mol = gto.M(atom=[['H', (0, 0, 0)], ['H', (0,0,0.71)]], basis='sto6g')
  >>> myhf = scf.UHF(mol).run()
  >>> myci = ci.UCISD(myhf).run()
  >>> wf_cisd = qml.qchem.import_state(myci, tol=1e-1)
  >>> print(wf_cisd)
  [ 0.        +0.j  0.        +0.j  0.        +0.j  0.1066467 +0.j
    1.        +0.j  0.        +0.j  0.        +0.j  0.        +0.j
    2.        +0.j  0.        +0.j  0.        +0.j  0.        +0.j
   -0.99429698+0.j  0.        +0.j  0.        +0.j  0.        +0.j]

  The state vector can be implemented in a circuit using qml.StatePrep.

  >>> dev = qml.device('default.qubit', wires=4)
  >>> @qml.qnode(dev)
  ... def circuit():
  ... qml.StatePrep(wf_cisd, wires=range(4))
  ... return qml.state()
  >>> print(circuit())
  [ 0.        +0.j  0.        +0.j  0.        +0.j  0.1066467 +0.j
    1.        +0.j  0.        +0.j  0.        +0.j  0.        +0.j
    2.        +0.j  0.        +0.j  0.        +0.j  0.        +0.j
   -0.99429698+0.j  0.        +0.j  0.        +0.j  0.        +0.j]

  The currently supported post-Hartree-Fock methods are RCISD, UCISD, RCCSD, and UCCSD which denote restricted (R) and unrestricted (U) configuration interaction (CI) and coupled cluster (CC) calculations with single and double (SD) excitations.

Reuse and reset qubits after mid-circuit measurements ♻️

• PennyLane now allows you to define circuits that reuse a qubit after a mid-circuit measurement has taken place. Optionally, the wire can also be reset to the $|0\rangle$ state. (#4402) (#4432)

  Post-measurement reset can be activated by setting reset=True when calling qml.measure. In this version of PennyLane, executing circuits with qubit reuse will result in the defer_measurements transform being applied. This transform replaces each reused wire with an additional qubit. However, future releases of PennyLane will explore device-level support for qubit reuse without consuming additional qubits.

  Qubit reuse and reset is also fully differentiable:

  dev = qml.device("default.qubit", wires=4)
  
  @qml.qnode(dev)
  def circuit(p):
      qml.RX(p, wires=0)
      m = qml.measure(0, reset=True)
      qml.cond(m, qml.Hadamard)(1)
  
      qml.RX(p, wires=0)
      m = qml.measure(0)
      qml.cond(m, qml.Hadamard)(1)
      return qml.expval(qml.PauliZ(1))
  

  >>> jax.grad(circuit)(0.4)
  Array(-0.35867804, dtype=float32, weak_type=True)
  

  You can read more about mid-circuit measurements in the documentation, and stay tuned for more mid-circuit measurement features in the next few releases!

Improvements 🛠

A new PennyLane drawing style

• Circuit drawings and plots can now be created following a PennyLane style. (#3950)

  The qml.draw_mpl function accepts a style='pennylane' argument to create PennyLane themed circuit diagrams:

  def circuit(x, z):
      qml.QFT(wires=(0,1,2,3))
      qml.Toffoli(wires=(0,1,2))
      qml.CSWAP(wires=(0,2,3))
      qml.RX(x, wires=0)
      qml.CRZ(z, wires=(3,0))
      return qml.expval(qml.PauliZ(0))
  
  qml.draw_mpl(circuit, style="pennylane")(1, 1)

  

  PennyLane-styled plots can also be drawn by passing "pennylane.drawer.plot" to Matplotlib's plt.style.use function:

  import matplotlib.pyplot as plt
  
  plt.style.use("pennylane.drawer.plot")
  for i in range(3):
      plt.plot(np.random.rand(10))

  If the font Quicksand Bold isn't available, an available default font is used instead.

Making operators immutable and PyTrees

• Any class inheriting from Operator is now automatically registered as a pytree with JAX. This unlocks the ability to jit functions of Operator. (#4458)

  >>> op = qml.adjoint(qml.RX(1.0, wires=0))
  >>> jax.jit(qml.matrix)(op)
  Array([[0.87758255-0.j        , 0.        +0.47942555j],
         [0.        +0.47942555j, 0.87758255-0.j        ]],      dtype=complex64, weak_type=True)
  >>> jax.tree_util.tree_map(lambd...

Release 0.31.1

Improvements 🛠

• data.Dataset now uses HDF5 instead of dill for serialization. (#4097)

• The qchem functions primitive_norm and contracted_norm are modified to be compatible with higher versions of scipy. (#4321)

Bug Fixes 🐛

• Dataset URLs are now properly escaped when fetching from S3. (#4412)

Contributors ✍️

This release contains contributions from (in alphabetical order):

Utkarsh Azad, Jack Brown, Diego Guala, Soran Jahangiri, Matthew Silverman

Release 0.31.0

New features since last release

Seamlessly create and combine fermionic operators 🔬

• Fermionic operators and arithmetic are now available. (#4191) (#4195) (#4200) (#4201) (#4209) (#4229) (#4253) (#4255) (#4262) (#4278)

  There are a couple of ways to create fermionic operators with this new feature:

  • qml.FermiC and qml.FermiA: the fermionic creation and annihilation operators, respectively. These operators are defined by passing the index of the orbital that the fermionic operator acts on. For instance, the operators a⁺(0) and a(3) are respectively constructed as

    >>> qml.FermiC(0)
    a⁺(0)
    >>> qml.FermiA(3)
    a(3)

    These operators can be composed with (*) and linearly combined with (+ and -) other Fermi operators to create arbitrary fermionic Hamiltonians. Multiplying several Fermi operators together creates an operator that we call a Fermi word:

    >>> word = qml.FermiC(0) * qml.FermiA(0) * qml.FermiC(3) * qml.FermiA(3)
    >>> word 
    a⁺(0) a(0) a⁺(3) a(3)

    Fermi words can be linearly combined to create a fermionic operator that we call a Fermi sentence:

    >>> sentence = 1.2 * word - 0.345 * qml.FermiC(3) * qml.FermiA(3)
    >>> sentence
    1.2 * a⁺(0) a(0) a⁺(3) a(3)
    - 0.345 * a⁺(3) a(3)

  • via qml.fermi.from_string: create a fermionic operator that represents multiple creation and annihilation operators being multiplied by each other (a Fermi word).

    >>> qml.fermi.from_string('0+ 1- 0+ 1-')
    a⁺(0) a(1) a⁺(0) a(1)
    >>> qml.fermi.from_string('0^ 1 0^ 1')
    a⁺(0) a(1) a⁺(0) a(1)

    Fermi words created with from_string can also be linearly combined to create a Fermi sentence:

    >>> word1 = qml.fermi.from_string('0+ 0- 3+ 3-')
    >>> word2 = qml.fermi.from_string('3+ 3-')
    >>> sentence = 1.2 * word1 + 0.345 * word2
    >>> sentence
    1.2 * a⁺(0) a(0) a⁺(3) a(3)
    + 0.345 * a⁺(3) a(3)

  Additionally, any fermionic operator, be it a single fermionic creation/annihilation operator, a Fermi word, or a Fermi sentence, can be mapped to the qubit basis by using qml.jordan_wigner:

  >>> qml.jordan_wigner(sentence)
  ((0.4725+0j)*(Identity(wires=[0]))) + ((-0.4725+0j)*(PauliZ(wires=[3]))) + ((-0.3+0j)*(PauliZ(wires=[0]))) + ((0.3+0j)*(PauliZ(wires=[0]) @ PauliZ(wires=[3])))

  Learn how to create fermionic Hamiltonians describing some simple chemical systems by checking out our fermionic operators demo!

Workflow-level resource estimation 🧮

• PennyLane's Tracker now monitors the resource requirements of circuits being executed by the device. (#4045) (#4110)

  Suppose we have a workflow that involves executing circuits with different qubit numbers. We can obtain the resource requirements as a function of the number of qubits by executing the workflow with the Tracker context:

  dev = qml.device("default.qubit", wires=4)
  
  @qml.qnode(dev)
  def circuit(n_wires):
      for i in range(n_wires):
          qml.Hadamard(i)
      return qml.probs(range(n_wires))
  
  with qml.Tracker(dev) as tracker:
      for i in range(1, 5):
          circuit(i)

  The resource requirements of individual circuits can then be inspected as follows:

  >>> resources = tracker.history["resources"]
  >>> resources[0]
  wires: 1
  gates: 1
  depth: 1
  shots: Shots(total=None)
  gate_types:
  {'Hadamard': 1}
  gate_sizes:
  {1: 1}
  >>> [r.num_wires for r in resources]
  [1, 2, 3, 4]

  Moreover, it is possible to predict the resource requirements without evaluating circuits using the null.qubit device, which follows the standard execution pipeline but returns numeric zeros. Consider the following workflow that takes the gradient of a 50-qubit circuit:

  n_wires = 50
  dev = qml.device("null.qubit", wires=n_wires)
  
  weight_shape = qml.StronglyEntanglingLayers.shape(2, n_wires)
  weights = np.random.random(weight_shape, requires_grad=True)
  
  @qml.qnode(dev, diff_method="parameter-shift")
  def circuit(weights):
      qml.StronglyEntanglingLayers(weights, wires=range(n_wires))
      return qml.expval(qml.PauliZ(0))
  
  with qml.Tracker(dev) as tracker:
      qml.grad(circuit)(weights)

  The tracker can be inspected to extract resource requirements without requiring a 50-qubit circuit run:

  >>> tracker.totals
  {'executions': 451, 'batches': 2, 'batch_len': 451}
  >>> tracker.history["resources"][0]
  wires: 50
  gates: 200
  depth: 77
  shots: Shots(total=None)
  gate_types:
  {'Rot': 100, 'CNOT': 100}
  gate_sizes:
  {1: 100, 2: 100}

• Custom operations can now be constructed that solely define resource requirements — an explicit decomposition or matrix representation is not needed. (#4033)

  PennyLane is now able to estimate the total resource requirements of circuits that include one or more of these operations, allowing you to estimate requirements for high-level algorithms composed of abstract subroutines.

  These operations can be defined by inheriting from ResourcesOperation and overriding the resources() method to return an appropriate Resources object:

  class CustomOp(qml.resource.ResourcesOperation):
      def resources(self):
          n = len(self.wires)
          r = qml.resource.Resources(
              num_wires=n,
              num_gates=n ** 2,
              depth=5,
          )
          return r

  >>> wires = [0, 1, 2]
  >>> c = CustomOp(wires)
  >>> c.resources()
  wires: 3
  gates: 9
  depth: 5
  shots: Shots(total=None)
  gate_types:
  {}
  gate_sizes:
  {}

  A quantum circuit that contains CustomOp can be created and inspected using qml.specs:

  dev = qml.device("default.qubit", wires=wires)
  
  @qml.qnode(dev)
  def circ():
      qml.PauliZ(wires=0)
      CustomOp(wires)
      return qml.state()

  >>> specs = qml.specs(circ)()
  >>> specs["resources"].depth
  6

Community contributions from UnitaryHack 🤝

• ParametrizedHamiltonian now has an improved string representation. (#4176)

  >>> def f1(p, t): return p[0] * jnp.sin(p[1] * t)
  >>> def f2(p, t): return p * t
  >>> coeffs = [2., f1, f2]
  >>> observables =  [qml.PauliX(0), qml.PauliY(0), qml.PauliZ(0)]
  >>> qml.dot(coeffs, observables)
    (2.0*(PauliX(wires=[0])))
  + (f1(params_0, t)*(PauliY(wires=[0])))
  + (f2(params_1, t)*(PauliZ(wires=[0])))

• The quantum information module now supports trace distance. (#4181)

  Two cases are enabled for calculating the trace distance:

  • A QNode transform via qml.qinfo.trace_distance:

    dev = qml.device('default.qubit', wires=2)
    
    @qml.qnode(dev)
    def circuit(param):
        qml.RY(param, wires=0)
        qml.CNOT(wires=[0, 1])
        return qml.state()

    >>> trace_distance_circuit = qml.qinfo.trace_distance(circuit, circuit, wires0=[0], wires1=[0])
    >>> x, y = np.array(0.4), np.array(0.6)
    >>> trace_distance_circuit((x,), (y,))
    0.047862689546603415

  • Flexible post-processing via qml.math.trace_distance:

    >>> rho = np.array([[0.3, 0], [0, 0.7]])
    >>> sigma = np.array([[0.5, 0], [0, 0.5]])
    >>> qml.math.trace_distance(rho, sigma)
    0.19999999999999998

• It is now possible to prepare qutrit basis states with qml.QutritBasisState. (#4185)

  wires = range(2)
  dev = qml.device("default.qutrit", wires=wires)
  
  @qml.qnode(dev)
  def qutrit_circuit():
      qml.QutritBasisState([1, 1], wires=wires)
      qml.TAdd(wires=wires)
      return qml.probs(wires=1)

  >>> qutrit_circuit()
  array([0., 0., 1.])

• A new tra...