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 transform called one_qubit_decomposition has been added to provide a unified interface for decompositions of a single-qubit unitary matrix into sequences of X, Y, and Z rotations. All decompositions simplify the rotations angles to be between 0 and 4 pi. (#4210) (#4246)

  >>> from pennylane.transforms import one_qubit_decomposition
  >>> U = np.array([[-0.28829348-0.78829734j,  0.30364367+0.45085995j],
  ...               [ 0.53396245-0.10177564j,  0.76279558-0.35024096j]])
  >>> one_qubit_decomposition(U, 0, "ZYZ")
  [RZ(tensor(12.32427531, requires_grad=True), wires=[0]),
   RY(tensor(1.14938178, requires_grad=True), wires=[0]),
   RZ(tensor(1.73305815, requires_grad=True), wires=[0])]
  >>> one_qubit_decomposition(U, 0, "XYX", return_global_phase=True)
  [RX(tensor(10.84535137, requires_grad=True), wires=[0]),
   RY(tensor(1.39749741, requires_grad=True), wires=[0]),
   RX(tensor(0.45246584, requires_grad=True), wires=[0]),
   (0.38469215914523336-0.9230449299422961j)*(Identity(wires=[0]))]

• The has_unitary_generator attribute in qml.ops.qubit.attributes no longer contains operators with non-unitary generators. (#4183)

• PennyLane Docker builds have been updated to include the latest plugins and interface versions. (#4178)

Extended support for differentiating pulses ⚛️

• The stochastic parameter-shift gradient method can now be used with hardware-compatible Hamiltonians. (#4132) (#4215)

  This new feature generalizes the stochastic parameter-shift gradient transform for pulses (stoch_pulse_grad) to support Hermitian generating terms beyond just Pauli words in pulse Hamiltonians, which makes it hardware-compatible.

• A new differentiation method called qml.gradients.pulse_generator is available, which combines classical processing with the parameter-shift rule for multivariate gates to differentiate pulse programs. Access it in your pulse programs by setting diff_method=qml.gradients.pulse_generator. (#4160)

• qml.pulse.ParametrizedEvolution now uses batched compressed sparse row (BCSR) format. This allows for computing Jacobians of the unitary directly even when dense=False. (#4126)

  def U(params):
      H = jnp.polyval * qml.PauliZ(0) # time dependent Hamiltonian
      Um = qml.evolve(H, dense=False)(params, t=10.)
      return qml.matrix(Um)
  params = jnp.array([[0.5]], dtype=complex)
  jac = jax.jacobian(U, holomorphic=True)(params)

Broadcasting and other tweaks to Torch and Keras layers 🦾

• The TorchLayer and KerasLayer integrations with torch.nn and Keras have been upgraded. Consider the following TorchLayer:

  n_qubits = 2
  dev = qml.device("default.qubit", wires=n_qubits)
  
  @qml.qnode(dev)
  def qnode(inputs, weights):
      qml.AngleEmbedding(inputs, wires=range(n_qubits))
      qml.BasicEntanglerLayers(weights, wires=range(n_qubits))
      return [qml.expval(qml.PauliZ(wires=i)) for i in range(n_qubits)]
  
  n_layers = 6
  weight_shapes = {"weights": (n_layers, n_qubits)}
  qlayer = qml.qnn.TorchLayer(qnode, weight_shapes)

  The following features are now available:

  • Native support for parameter broadcasting. (#4131)

    >>> batch_size = 10
    >>> inputs = torch.rand((batch_size, n_qubits))
    >>> qlayer(inputs)
    >>> dev.num_executions == 1
    True

  • The ability to draw a TorchLayer and KerasLayer using qml.draw() and qml.draw_mpl(). (#4197)

    >>> print(qml.draw(qlayer, show_matrices=False)(inputs))
    0: ─╭AngleEmbedding(M0)─╭BasicEntanglerLayers(M1)─┤  <Z>
    1: ─╰AngleEmbedding(M0)─╰BasicEntanglerLayers(M1)─┤  <Z>

  • Support for KerasLayer model saving and clearer instructions on TorchLayer model saving. (#4149) (#4158)

    >>> torch.save(qlayer.state_dict(), "weights.pt")  # Saving
    >>> qlayer.load_state_dict(torch.load("weights.pt"))  # Loading
    >>> qlayer.eval()

    Hybrid models containing KerasLayer or TorchLayer objects can also be saved and loaded.

Improvements 🛠

A more flexible projector

• qml.Projector now accepts a state vector representation, which enables the creation of projectors in any basis. (#4192)

  dev = qml.device("default.qubit", wires=2)
  @qml.qnode(dev)
  def circuit(state):
      return qml.expval(qml.Projector(state, wires=[0, 1]))
  zero_state = [0, 0]
  plusplus_state = np.array([1, 1, 1, 1]) / 2

  >>> circuit(zero_state)
  tensor(1., requires_grad=True)
  >>> circuit(plusplus_state)
  tensor(0.25, requires_grad=True)

Do more with qutrits

• Three qutrit rotation operators have been added that are analogous to RX, RY, and RZ:

  • qml.TRX: an X rotation
  • qml.TRY: a Y rotation
  • qml.TRZ: a Z rotation

  (#2845) (#2846) (#2847)

• Qutrit devices now support parameter-shift differentiation. (#2845)

The qchem module

• qchem.molecular_hamiltonian(), qchem.qubit_observable(), qchem.import_operator(), and qchem.dipole_moment() now return an arithmetic operator if enable_new_opmath() is active.
  (#4138) (#4159) (#4189) (#4204)

• Non-cubic lattice support for all electron resource estimation has been added. (#3956)

• The qchem.molecular_hamiltonian() function has been upgraded to support custom wires for constructing differentiable Hamiltonians. The zero imaginary component of the Hamiltonian coefficients have been removed. (#4050) (#4094)

• Jordan-Wigner transforms that cache Pauli gate objects have been accelerated. (#4046)

• An error is now raised by qchem.molecular_hamiltonian when the dhf method is used for an open-shell system. This duplicates a similar error in qchem.Molecule but makes it clear that the pyscf backend can be used for open-shell calculations. (#4058)

• Updated various qubit tapering methods to support operator arithmetic. (#4252)

Next-generation device API

• The new device interface has been integrated with qml.execute for autograd, backpropagation, and no differentiation. (#3903)

• Support for adjoint differentiation has been added to the DefaultQubit2 device. (#4037)

• A new function called measure_with_samples that returns a sample-based measurement result given a state has been added. (#4083) (#4093) (#4162) (#4254)

• DefaultQubit2.preprocess now returns a new ExecutionConfig object with decisions for gradient_method, use_device_gradient, and grad_on_execution. (#4102)

• Support for sample-based measurements has been added to the DefaultQubit2 device. (#4105) (#4114) (#4133) (#4172)

• The DefaultQubit2 device now has a seed keyword argument. (#4120)

• Added a dense keyword to ParametrizedEvolution that allows forcing dense or sparse matrices. (#4079) (#4095) (#4285)

• Adds the Type variables pennylane.typing.Result and pennylane.typing.ResultBatch for type hinting the result of an execution. (#4018)

• qml.devices.ExecutionConfig no longer has a shots property, as it is now on the QuantumScript. It now has a use_device_gradient property. ExecutionConfig.grad_on_execution = None indicates a request for "best", instead of a string. (#4102)

• The new device interface for Jax has been integrated with qml.execute. (#4137)

• The new device interface is now integrated with qml.execute for Tensorflow. (#4169)

• The experimental device DefaultQubit2 now supports qml.Snapshot. (#4193)

• The experimental device interface is integrated with the QNode. (#4196)

• The new device interface in integrated with qml.execute for Torch. (#4257)

Handling shots

• QuantumScript now has a shots property, allowing shots to be tied to executions instead of devices. (#4067) (#4103) (#4106) (#4112)

• Several Python built-in functions are now properly defined for instances of the Shots class.

  • print: printing Shots instances is now human-readable
  • str: converting Shots instances to human-readable strings
  • ==: equating two different Shots instances
  • hash: obtaining the hash values of Shots instances

  (#4081) (#4082)

• qml.devices.ExecutionConfig no longer has a shots property, as it is now on the QuantumScript. It now has a use_device_gradient property. ExecutionConfig.grad_on_execution = None indicates a request for "best" instead of a string. (#4102)

• QuantumScript.shots has been integrated with QNodes so that shots are placed on the QuantumScript during QNode construction. (#4110)

• The gradients module has been updated to use the new Shots object internally (#4152)

Operators

• qml.prod now accepts a single quantum function input for creating new Prod operators. (#4011)

• DiagonalQubitUnitary now decomposes into RZ, IsingZZ and MultiRZ gates instead of a QubitUnitary operation with a dense matrix. (#4035)

• All objects being queued in an AnnotatedQueue are now wrapped so that AnnotatedQueue is not dependent on the has of any operators or measurement processes. (#4087)

• A dense keyword to ParametrizedEvolution that allows forcing dense or sparse matrices has been added. (#4079) (#4095)

• Added a new function qml.ops.functions.bind_new_parameters that creates a copy of an operator with new parameters without mutating the original operator. (#4113) (#4256)

• qml.CY has been moved from qml.ops.qubit.non_parametric_ops to qml.ops.op_math.controlled_ops and now inherits from qml.ops.op_math.ControlledOp. (#4116)

• qml.CZ now inherits from the ControlledOp class and supports exponentiation to arbitrary powers with pow, which is no longer limited to integers. It also supports sparse_matrix and decomposition representations. (#4117)

• The construction of the Pauli representation for the Sum class is now faster. (#4142)

• qml.drawer.drawable_layers.drawable_layers and qml.CircuitGraph have been updated to not rely on Operator equality or hash to work correctly. (#4143)

Other improvements

• A transform dispatcher and program have been added. (#4109) (#4187)

• Reduced density matrix functionality has been added via qml.math.reduce_dm and qml.math.reduce_statevector. Both functions have broadcasting support. (#4173)

• The following functions in qml.qinfo now support parameter broadcasting:

  • reduced_dm
  • purity
  • vn_entropy
  • mutual_info
  • fidelity
  • relative_entropy
  • trace_distance

  (#4234)

• The following functions in qml.math now support parameter broadcasting:

  • purity
  • vn_entropy
  • mutual_info
  • fidelity
  • relative_entropy
  • max_entropy
  • sqrt_matrix

  (#4186)

• pulse.ParametrizedEvolution now raises an error if the number of input parameters does not match the number of parametrized coefficients in the ParametrizedHamiltonian that generates it. An exception is made for HardwareHamiltonians which are not checked. (#4216)

• The default value for the show_matrices keyword argument in all drawing methods is now True. This allows for quick insights into broadcasted tapes, for example. (#3920)

• Type variables for qml.typing.Result and qml.typing.ResultBatch have been added for type hinting the result of an execution. (#4108)

• The Jax-JIT interface now uses symbolic zeros to determine trainable parameters. (#4075)

• A new function called pauli.pauli_word_prefactor() that extracts the prefactor for a given Pauli word has been added. (#4164)

• Variable-length argument lists of functions and methods in some docstrings is now more clear. (#4242)

• qml.drawer.drawable_layers.drawable_layers and qml.CircuitGraph have been updated to not rely on Operator equality or hash to work correctly. (#4143)

• Drawing mid-circuit measurements connected by classical control signals to conditional operations is now possible. (#4228)

• The autograd interface now submits all required tapes in a single batch on the backward pass. (#4245)

Breaking changes 💔

• The default value for the show_matrices keyword argument in all drawing methods is now True. This allows for quick insights into broadcasted tapes, for example. (#3920)

• DiagonalQubitUnitary now decomposes into RZ, IsingZZ, and MultiRZ gates rather than a QubitUnitary. (#4035)

• Jax trainable parameters are now Tracer instead of JVPTracer. It is not always the right definition for the JIT interface, but we update them in the custom JVP using symbolic zeros. (#4075)

• The experimental Device interface qml.devices.experimental.Device now requires that the preprocess method also returns an ExecutionConfig object. This allows the device to choose what "best" means for various hyperparameters like gradient_method and grad_on_execution. (#4007) (#4102)

• Gradient transforms with Jax no longer support argnum. Use argnums instead. (#4076)

• qml.collections, qml.op_sum, and qml.utils.sparse_hamiltonian have been removed. (#4071)

• The pennylane.transforms.qcut module now uses (op, id(op)) as nodes in directed multigraphs that are used within the circuit cutting workflow instead of op. This change removes the dependency of the module on the hash of operators. (#4227)

• Operator.data now returns a tuple instead of a list. (#4222)

• The pulse differentiation methods, pulse_generator and stoch_pulse_grad, now raise an error when they are applied to a QNode directly. Instead, use differentiation via a JAX entry point (jax.grad, jax.jacobian, ...). (#4241)

Deprecations 👋

• LieAlgebraOptimizer has been renamed to RiemannianGradientOptimizer. (#4153)

• Operation.base_name has been deprecated. Please use Operation.name or type(op).__name__ instead.

• QuantumScript's name keyword argument and property have been deprecated. This also affects QuantumTape and OperationRecorder. (#4141)

• The qml.grouping module has been removed. Its functionality has been reorganized in the qml.pauli module.

• The public methods of DefaultQubit are pending changes to follow the new device API, as used in DefaultQubit2. Warnings have been added to the docstrings to reflect this. (#4145)

• qml.math.reduced_dm has been deprecated. Please use qml.math.reduce_dm or qml.math.reduce_statevector instead. (#4173)

• qml.math.purity, qml.math.vn_entropy, qml.math.mutual_info, qml.math.fidelity, qml.math.relative_entropy, and qml.math.max_entropy no longer support state vectors as input. Please call qml.math.dm_from_state_vector on the input before passing to any of these functions. (#4186)

• The do_queue keyword argument in qml.operation.Operator has been deprecated. Instead of setting do_queue=False, use the qml.QueuingManager.stop_recording() context. (#4148)

• zyz_decomposition and xyx_decomposition are now deprecated in favour of one_qubit_decomposition. (#4230)

Documentation 📝

• The documentation is updated to construct QuantumTape upon initialization instead of with queuing. (#4243)

• The docstring for qml.ops.op_math.Pow.__new__ is now complete and it has been updated along with qml.ops.op_math.Adjoint.__new__. (#4231)

• The docstring for qml.grad now states that it should be used with the Autograd interface only. (#4202)

• The description of mult in the qchem.Molecule docstring now correctly states the value of mult that is supported. (#4058)

Bug Fixes 🐛

• Fixed adjoint jacobian results with grad_on_execution=False in the JAX-JIT interface. (#4217)

• Fixed the matrix of SProd when the coefficient is tensorflow and the target matrix is not complex128. (#4249)

• Fixed a bug where stoch_pulse_grad would ignore prefactors of rescaled Pauli words in the generating terms of a pulse Hamiltonian. (#4156)

• Fixed a bug where the wire ordering of the wires argument to qml.density_matrix was not taken into account. (#4072)

• A patch in interfaces/autograd.py that checks for the strawberryfields.gbs device has been removed. That device is pinned to PennyLane <= v0.29.0, so that patch is no longer necessary. (#4089)

• qml.pauli.are_identical_pauli_words now treats all identities as equal. Identity terms on Hamiltonians with non-standard wire orders are no longer eliminated. (#4161)

• qml.pauli_sentence() is now compatible with empty Hamiltonians qml.Hamiltonian([], []). (#4171)

• Fixed a bug with Jax where executing multiple tapes with gradient_fn="device" would fail. (#4190)

• A more meaningful error message is raised when broadcasting with adjoint differentiation on DefaultQubit. (#4203)

• The has_unitary_generator attribute in qml.ops.qubit.attributes no longer contains operators with non-unitary generators. (#4183)

• Fixed a bug where op = qml.qsvt() was incorrect up to a global phase when using convention="Wx"" and qml.matrix(op). (#4214)

• Fixed a buggy calculation of the angle in xyx_decomposition that causes it to give an incorrect decomposition. An if conditional was intended to prevent divide by zero errors, but the division was by the sine of the argument. So, any multiple of $\pi$ should trigger the conditional, but it was only checking if the argument was 0. Example: qml.Rot(2.3, 2.3, 2.3) (#4210)

• Fixed bug that caused ShotAdaptiveOptimizer to truncate dimensions of parameter-distributed shots during optimization. (#4240)

• Sum observables can now have trainable parameters. (#4251) (#4275)

Contributors ✍️

This release contains contributions from (in alphabetical order):

Venkatakrishnan AnushKrishna,
Utkarsh Azad,
Thomas Bromley,
Isaac De Vlugt,
Lillian M. A. Frederiksen,
Emiliano Godinez Ramirez
Nikhil Harle
Soran Jahangiri,
Edward Jiang,
Korbinian Kottmann,
Christina Lee,
Vincent Michaud-Rioux,
Romain Moyard,
Tristan Nemoz,
Mudit Pandey,
Manul Patel,
Borja Requena,
Modjtaba Shokrian-Zini,
Mainak Roy,
Matthew Silverman,
Jay Soni,
Edward Thomas,
David Wierichs,
Frederik Wilde.