diff --git a/demonstrations/tutorial_classically_boosted_vqe.metadata.json b/demonstrations/tutorial_classically_boosted_vqe.metadata.json index 797d86cf63..df8e6990f6 100644 --- a/demonstrations/tutorial_classically_boosted_vqe.metadata.json +++ b/demonstrations/tutorial_classically_boosted_vqe.metadata.json @@ -9,7 +9,7 @@ } ], "dateOfPublication": "2022-10-31T00:00:00+00:00", - "dateOfLastModification": "2024-12-13T00:00:00+00:00", + "dateOfLastModification": "2025-01-23T00:00:00+00:00", "categories": [ "Quantum Chemistry" ], diff --git a/demonstrations/tutorial_classically_boosted_vqe.py b/demonstrations/tutorial_classically_boosted_vqe.py index dd65f9a332..986ec0a7b2 100644 --- a/demonstrations/tutorial_classically_boosted_vqe.py +++ b/demonstrations/tutorial_classically_boosted_vqe.py @@ -449,7 +449,7 @@ def hadamard_test(Uq, Ucl, component="real"): qml.Hadamard(wires=[0]) qml.ControlledQubitUnitary( - Uq.conjugate().T @ Ucl, control_wires=[0], wires=wires[1:] + Uq.conjugate().T @ Ucl, wires=wires ) qml.Hadamard(wires=[0]) diff --git a/demonstrations/tutorial_how_to_quantum_just_in_time_compile_vqe_catalyst.py b/demonstrations/tutorial_how_to_quantum_just_in_time_compile_vqe_catalyst.py index e35b5ccb1c..e4a956d1ee 100644 --- a/demonstrations/tutorial_how_to_quantum_just_in_time_compile_vqe_catalyst.py +++ b/demonstrations/tutorial_how_to_quantum_just_in_time_compile_vqe_catalyst.py @@ -123,7 +123,7 @@ def cost(params): @qml.qjit @qml.qnode(dev) def cost(params): - qml.BasisState.compute_decomposition(hf, wires=range(qubits)) + qml.BasisState(hf, wires=range(qubits)) qml.DoubleExcitation(params[0], wires=[0, 1, 2, 3]) qml.DoubleExcitation(params[1], wires=[0, 1, 4, 5]) return qml.expval(H) diff --git a/demonstrations/tutorial_kak_decomposition.metadata.json b/demonstrations/tutorial_kak_decomposition.metadata.json index 94e43e6012..ae7f442f81 100644 --- a/demonstrations/tutorial_kak_decomposition.metadata.json +++ b/demonstrations/tutorial_kak_decomposition.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2024-11-25T00:00:00+00:00", - "dateOfLastModification": "2025-01-07T09:00:00+00:00", + "dateOfLastModification": "2025-03-17T09:00:00+00:00", "categories": [ "Quantum Computing", "Algorithms" diff --git a/demonstrations/tutorial_kak_decomposition.py b/demonstrations/tutorial_kak_decomposition.py index a4789f8a4e..8a61aa3cc9 100644 --- a/demonstrations/tutorial_kak_decomposition.py +++ b/demonstrations/tutorial_kak_decomposition.py @@ -355,7 +355,7 @@ def check_cartan_decomposition(g, k, space_name): """Given an algebra g and an operator subspace k, verify that k is a subalgebra and gives rise to a Cartan decomposition.""" # Check Lie closure of k - k_lie_closure = qml.pauli.dla.lie_closure(k) + k_lie_closure = qml.lie_closure(k) k_is_closed = len(k_lie_closure) == len(k) print(f"The Lie closure of k is as big as k itself: {k_is_closed}.") diff --git a/demonstrations/tutorial_liesim.metadata.json b/demonstrations/tutorial_liesim.metadata.json index f3d75d959d..a307de56e6 100644 --- a/demonstrations/tutorial_liesim.metadata.json +++ b/demonstrations/tutorial_liesim.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2024-06-07T00:00:00+00:00", - "dateOfLastModification": "2024-10-07T00:00:00+00:00", + "dateOfLastModification": "2025-03-17T00:00:00+00:00", "categories": [ "Quantum Computing", "Getting Started" diff --git a/demonstrations/tutorial_liesim.py b/demonstrations/tutorial_liesim.py index 14f91b9a05..20630ee3da 100644 --- a/demonstrations/tutorial_liesim.py +++ b/demonstrations/tutorial_liesim.py @@ -186,7 +186,7 @@ # work with PauliSentence instances for efficiency generators = [op.pauli_rep for op in generators] -dla = qml.pauli.lie_closure(generators, pauli=True) +dla = qml.lie_closure(generators, pauli=True) dim_g = len(dla) ############################################################################## @@ -239,7 +239,7 @@ # the forward pass of the expectation value computation. For demonstration purposes, # we choose a random subset of ``depth=10`` generators for gates from the DLA. -adjoint_repr = qml.pauli.structure_constants(dla) +adjoint_repr = qml.structure_constants(dla) depth = 10 gate_choice = np.random.choice(dim_g, size=depth) diff --git a/demonstrations/tutorial_liesim_extension.metadata.json b/demonstrations/tutorial_liesim_extension.metadata.json index 7da2c15c82..3837196b18 100644 --- a/demonstrations/tutorial_liesim_extension.metadata.json +++ b/demonstrations/tutorial_liesim_extension.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2024-06-18T00:00:00+00:00", - "dateOfLastModification": "2025-01-13T00:00:00+00:00", + "dateOfLastModification": "2025-03-17T00:00:00+00:00", "categories": [ "Quantum Computing", "Quantum Machine Learning" diff --git a/demonstrations/tutorial_liesim_extension.py b/demonstrations/tutorial_liesim_extension.py index b11a640b6e..ce62806eb9 100644 --- a/demonstrations/tutorial_liesim_extension.py +++ b/demonstrations/tutorial_liesim_extension.py @@ -99,7 +99,7 @@ def TFIM(n): generators += [Z(i) for i in range(n)] generators = [op.pauli_rep for op in generators] - dla = qml.pauli.lie_closure(generators, pauli=True) + dla = qml.lie_closure(generators, pauli=True) dim_dla = len(dla) return generators, dla, dim_dla diff --git a/demonstrations/tutorial_noisy_circuits.metadata.json b/demonstrations/tutorial_noisy_circuits.metadata.json index 92522adae3..0ba26ef0b7 100644 --- a/demonstrations/tutorial_noisy_circuits.metadata.json +++ b/demonstrations/tutorial_noisy_circuits.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2021-02-22T00:00:00+00:00", - "dateOfLastModification": "2024-10-07T00:00:00+00:00", + "dateOfLastModification": "2024-12-18T00:00:00+00:00", "categories": [ "Getting Started" ], diff --git a/demonstrations/tutorial_noisy_circuits.py b/demonstrations/tutorial_noisy_circuits.py index 1491215009..af1a7f4d7e 100644 --- a/demonstrations/tutorial_noisy_circuits.py +++ b/demonstrations/tutorial_noisy_circuits.py @@ -144,12 +144,13 @@ def bitflip_circuit(p): qml.CNOT(wires=[0, 1]) qml.BitFlip(p, wires=0) qml.BitFlip(p, wires=1) - return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1)) + return qml.expval(qml.PauliZ(0) @ qml.PauliZ(1)), qml.state() ps = [0.001, 0.01, 0.1, 0.2] for p in ps: - print(f"QNode output for bit flip probability {p} is {bitflip_circuit(p):.4f}") + result = bitflip_circuit(p) + print(f"QNode output for bit flip probability {p} is {result[0]:.4f}") ###################################################################### @@ -158,7 +159,7 @@ def bitflip_circuit(p): # mitigation and error correction are so important. We can use PennyLane to look under the hood and # see the output state of the circuit for the largest noise parameter -print(f"Output state for bit flip probability {p} is \n{np.real(dev.state)}") +print(f"Output state for bit flip probability {p} is \n{result[1]}") ###################################################################### # Besides the bit flip channel, PennyLane supports several other noisy channels that are commonly diff --git a/demonstrations/tutorial_odegen.metadata.json b/demonstrations/tutorial_odegen.metadata.json index ab2eb2a794..df0ad04903 100644 --- a/demonstrations/tutorial_odegen.metadata.json +++ b/demonstrations/tutorial_odegen.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2023-12-12T00:00:00+00:00", - "dateOfLastModification": "2024-10-07T00:00:00+00:00", + "dateOfLastModification": "2025-01-28T00:00:00+00:00", "categories": [ "Optimization", "Quantum Computing", diff --git a/demonstrations/tutorial_odegen.py b/demonstrations/tutorial_odegen.py index 257bb3884c..8894ff68d8 100644 --- a/demonstrations/tutorial_odegen.py +++ b/demonstrations/tutorial_odegen.py @@ -45,10 +45,10 @@ that can be executed on hardware. -SPS & ODEgen ------------- +SPS and ODEgen gradient rules +----------------------------- -Let us start by deriving both the SPS rule and ODEgen. +Let us start by deriving both the SPS and ODEgen rules. We are interested in cost functions of the form @@ -163,6 +163,7 @@ Let us define it in PennyLane and also import some libraries that we are going to need for this demo. """ + import pennylane as qml import numpy as np import jax.numpy as jnp @@ -181,21 +182,21 @@ ############################################################################## # We are going to consider a system of transmon qubits described by the Hamiltonian -# +# # .. math:: H(\theta, t) = - \sum_i \frac{\omega_i}{2} Z_i + \sum_i \Omega_i(t) \sin(\nu_i t + \phi_i(t)) Y_i + \sum_{q, p \in \mathcal{C}} \frac{g_{qp}}{2} (X_i X_p + Y_i Y_p). -# -# The first term describes the single qubits with frequencies :math:`\omega_i.` -# The second term describes the driving with drive amplitudes :math:`\Omega_i,` drive frequencies :math:`\nu_i` and phases :math:`\phi_i.` -# You can check out our :doc:`recent demo on driving qubits on OQC's Lucy ` if +# +# The first term describes the single qubits with frequencies :math:`\omega_i.` +# The second term describes the driving with drive amplitudes :math:`\Omega_i,` drive frequencies :math:`\nu_i` and phases :math:`\phi_i.` +# You can check out our :doc:`recent demo on driving qubits on OQC's Lucy ` if # you want to learn more about the details of controlling transmon qubits. -# The third term describes the coupling between neighboring qubits. We only have two qubits and a simple topology of +# The third term describes the coupling between neighboring qubits. We only have two qubits and a simple topology of # :math:`\mathcal{C} = \{(0, 1)\},` hence only one coupling constant :math:`g_{01}.` -# The coupling is necessary to generate entanglement, which is achieved with cross-resonant driving in fixed-coupling +# The coupling is necessary to generate entanglement, which is achieved with cross-resonant driving in fixed-coupling # transmon systems, as is the case here. -# -# We will use realistic parameters for the transmons, taken from the `coaxmon design paper `_ [#Patterson]_ +# +# We will use realistic parameters for the transmons, taken from the `coaxmon design paper `_ [#Patterson]_ # (this is the blue-print for the transmon qubits in OQC's Lucy that you can :doc:`access on a pulse level in PennyLane `). -# In order to prepare the singlet ground state, we will perform a cross-resonance pulse, i.e. driving one qubit at its coupled neighbor's +# In order to prepare the singlet ground state, we will perform a cross-resonance pulse, i.e. driving one qubit at its coupled neighbor's # frequency for entanglement generation (see [#Patterson]_ or [#Krantz]_) while simultaneously driving the other qubit on resonance. # We choose a gate time of :math:`100 \text{ ns}.` We will use a piecewise constant function :func:`~pennylane.pulse.pwc` to parametrize both # the amplitude :math:`\Omega_i(t)` and the phase :math:`\phi_i(t)` in time, with ``t_bins = 10`` time bins to allow for enough flexibility in the evolution. @@ -221,7 +222,7 @@ def wrapped(p, t): H_pulse += drive_field(T_CR, qubit_freq[0]) * qml.PauliY(wires[1]) # off-resonance driving of qubit 1 ############################################################################## -# We can now define the cost function that computes the expectation value of +# We can now define the cost function that computes the expectation value of # the Heisenberg Hamiltonian after evolving the state with the parametrized pulse Hamiltonian. # We then define the two separate qnodes with ODEgen and SPS as their differentiation methods, respectively. @@ -235,7 +236,14 @@ def qnode0(params): value_and_grad_jax = jax.jit(jax.value_and_grad(qnode_jax)) num_split_times = 8 -qnode_sps = qml.QNode(qnode0, dev, interface="jax", diff_method=qml.gradients.stoch_pulse_grad, use_broadcasting=True, num_split_times=num_split_times) +gradient_kwargs = {"use_broadcasting": True, "num_split_times": num_split_times} +qnode_sps = qml.QNode( + qnode0, + dev, + interface="jax", + diff_method=qml.gradients.stoch_pulse_grad, + gradient_kwargs=gradient_kwargs, +) value_and_grad_sps = jax.value_and_grad(qnode_sps) qnode_odegen = qml.QNode(qnode0, dev, interface="jax", diff_method=qml.gradients.pulse_odegen) @@ -304,12 +312,12 @@ def partial_step(grad_circuit, opt_state, theta): ############################################################################## -# We see that with analytic gradients (ODEgen), we can reach the ground state energy within 100 epochs, whereas with SPS gradients we cannot find the path +# We see that with analytic gradients (ODEgen), we can reach the ground state energy within 100 epochs, whereas with SPS gradients we cannot find the path # towards the minimum due to the stochasticity of the gradient estimates. Note that both optimizations start from the same (random) initial point. # This picture solidifies when repeating this procedure for multiple runs from different random initializations, as was demonstrated in [#Kottmann]_. # # We also want to make sure that this is a fair comparison in terms of quantum resources. In the case of ODEgen, we maximally have :math:`\mathcal{R}_\text{ODEgen} = 2 (4^n - 1) = 30` expectation values. -# For SPS we have :math:`2 N_g N_s = 32` (due to :math:`N_g = 2` and :math:`N_s=8` time samples per gradient that we chose in ``num_split_times`` above). Thus, overall, we require fewer +# For SPS we have :math:`2 N_g N_s = 32` (due to :math:`N_g = 2` and :math:`N_s=8` time samples per gradient that we chose in ``num_split_times`` above). Thus, overall, we require fewer # quantum resources for ODEgen gradients while achieving better performance. # # Conclusion @@ -323,9 +331,8 @@ def partial_step(grad_circuit, opt_state, theta): # Running VQE using ODEgen on hardware has recently been demonstrated in [#Kottmann]_ and you can directly find `the code here `_. - ############################################################################## -# +# # References # ---------- # diff --git a/demonstrations/tutorial_pulse_programming101.metadata.json b/demonstrations/tutorial_pulse_programming101.metadata.json index 7801031d1f..9c5a8f71c3 100644 --- a/demonstrations/tutorial_pulse_programming101.metadata.json +++ b/demonstrations/tutorial_pulse_programming101.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2023-03-08T00:00:00+00:00", - "dateOfLastModification": "2024-10-07T00:00:00+00:00", + "dateOfLastModification": "2025-02-14T00:00:00+00:00", "categories": [ "Quantum Hardware", "Quantum Computing" diff --git a/demonstrations/tutorial_pulse_programming101.py b/demonstrations/tutorial_pulse_programming101.py index b1b4ce33ea..aa050030a1 100644 --- a/demonstrations/tutorial_pulse_programming101.py +++ b/demonstrations/tutorial_pulse_programming101.py @@ -322,15 +322,21 @@ def wrapped(p, t): ############################################################################## # Now we define the ``qnode`` that computes the expectation value of the molecular Hamiltonian. +# We need to wrap the ``qnode`` in a function so that we can convert the expectation value to a real number. +# This will enable use to make use of gradient descent methods that require real-valued loss functions. dev = qml.device("default.qubit", wires=range(n_wires)) -@qml.qnode(dev, interface="jax") def qnode(theta, t=duration): - qml.BasisState(jnp.array(data.tapered_hf_state), wires=H_obj.wires) - qml.evolve(H_pulse)(params=(*theta, *theta), t=t) - return qml.expval(H_obj) + @qml.qnode(dev) + def _qnode_inner(theta, t=duration): + qml.BasisState(jnp.array(data.tapered_hf_state), wires=H_obj.wires) + qml.evolve(H_pulse)(params=(*theta, *theta), t=t) + return qml.expval(H_obj) + + expectation_value = _qnode_inner(theta, t) # Execute the qnode + return jnp.real(expectation_value) # Typecast to real number value_and_grad = jax.jit(jax.value_and_grad(qnode)) diff --git a/demonstrations/tutorial_testing_symmetry.metadata.json b/demonstrations/tutorial_testing_symmetry.metadata.json index 2a7ca1c3df..b42d1b3179 100644 --- a/demonstrations/tutorial_testing_symmetry.metadata.json +++ b/demonstrations/tutorial_testing_symmetry.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2023-01-24T00:00:00+00:00", - "dateOfLastModification": "2024-11-06T00:00:00+00:00", + "dateOfLastModification": "2025-01-23T00:00:00+00:00", "categories": [ "Algorithms", "Quantum Computing" diff --git a/demonstrations/tutorial_testing_symmetry.py b/demonstrations/tutorial_testing_symmetry.py index 61e0776885..802ac04055 100644 --- a/demonstrations/tutorial_testing_symmetry.py +++ b/demonstrations/tutorial_testing_symmetry.py @@ -308,14 +308,14 @@ def prep_plus(): # Implement controlled symmetry operations on system def CU_sys(): - qml.ControlledQubitUnitary(c_mat @ c_mat, control_wires=[aux[0]], wires=system) - qml.ControlledQubitUnitary(c_mat, control_wires=[aux[1]], wires=system) + qml.ControlledQubitUnitary(c_mat @ c_mat, wires=[aux[0]] + list(system)) + qml.ControlledQubitUnitary(c_mat, wires=[aux[1]] + list(system)) # Implement controlled symmetry operations on copy def CU_cpy(): - qml.ControlledQubitUnitary(c_mat @ c_mat, control_wires=[aux[0]], wires=copy) - qml.ControlledQubitUnitary(c_mat, control_wires=[aux[1]], wires=copy) + qml.ControlledQubitUnitary(c_mat @ c_mat, wires=[aux[0]] + list(copy)) + qml.ControlledQubitUnitary(c_mat, wires=[aux[1]] + list(copy)) ###################################################################### # Let’s combine everything and actually run our circuit! diff --git a/demonstrations_v2/tutorial_kak_decomposition/demo.py b/demonstrations_v2/tutorial_kak_decomposition/demo.py index a4789f8a4e..8a61aa3cc9 100644 --- a/demonstrations_v2/tutorial_kak_decomposition/demo.py +++ b/demonstrations_v2/tutorial_kak_decomposition/demo.py @@ -355,7 +355,7 @@ def check_cartan_decomposition(g, k, space_name): """Given an algebra g and an operator subspace k, verify that k is a subalgebra and gives rise to a Cartan decomposition.""" # Check Lie closure of k - k_lie_closure = qml.pauli.dla.lie_closure(k) + k_lie_closure = qml.lie_closure(k) k_is_closed = len(k_lie_closure) == len(k) print(f"The Lie closure of k is as big as k itself: {k_is_closed}.") diff --git a/demonstrations_v2/tutorial_kak_decomposition/metadata.json b/demonstrations_v2/tutorial_kak_decomposition/metadata.json index 94e43e6012..ae7f442f81 100644 --- a/demonstrations_v2/tutorial_kak_decomposition/metadata.json +++ b/demonstrations_v2/tutorial_kak_decomposition/metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2024-11-25T00:00:00+00:00", - "dateOfLastModification": "2025-01-07T09:00:00+00:00", + "dateOfLastModification": "2025-03-17T09:00:00+00:00", "categories": [ "Quantum Computing", "Algorithms" diff --git a/demonstrations_v2/tutorial_liesim/demo.py b/demonstrations_v2/tutorial_liesim/demo.py index 14f91b9a05..20630ee3da 100644 --- a/demonstrations_v2/tutorial_liesim/demo.py +++ b/demonstrations_v2/tutorial_liesim/demo.py @@ -186,7 +186,7 @@ # work with PauliSentence instances for efficiency generators = [op.pauli_rep for op in generators] -dla = qml.pauli.lie_closure(generators, pauli=True) +dla = qml.lie_closure(generators, pauli=True) dim_g = len(dla) ############################################################################## @@ -239,7 +239,7 @@ # the forward pass of the expectation value computation. For demonstration purposes, # we choose a random subset of ``depth=10`` generators for gates from the DLA. -adjoint_repr = qml.pauli.structure_constants(dla) +adjoint_repr = qml.structure_constants(dla) depth = 10 gate_choice = np.random.choice(dim_g, size=depth) diff --git a/demonstrations_v2/tutorial_liesim/metadata.json b/demonstrations_v2/tutorial_liesim/metadata.json index f3d75d959d..a307de56e6 100644 --- a/demonstrations_v2/tutorial_liesim/metadata.json +++ b/demonstrations_v2/tutorial_liesim/metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2024-06-07T00:00:00+00:00", - "dateOfLastModification": "2024-10-07T00:00:00+00:00", + "dateOfLastModification": "2025-03-17T00:00:00+00:00", "categories": [ "Quantum Computing", "Getting Started" diff --git a/demonstrations_v2/tutorial_liesim_extension/demo.py b/demonstrations_v2/tutorial_liesim_extension/demo.py index b11a640b6e..ce62806eb9 100644 --- a/demonstrations_v2/tutorial_liesim_extension/demo.py +++ b/demonstrations_v2/tutorial_liesim_extension/demo.py @@ -99,7 +99,7 @@ def TFIM(n): generators += [Z(i) for i in range(n)] generators = [op.pauli_rep for op in generators] - dla = qml.pauli.lie_closure(generators, pauli=True) + dla = qml.lie_closure(generators, pauli=True) dim_dla = len(dla) return generators, dla, dim_dla diff --git a/demonstrations_v2/tutorial_liesim_extension/metadata.json b/demonstrations_v2/tutorial_liesim_extension/metadata.json index 7da2c15c82..3837196b18 100644 --- a/demonstrations_v2/tutorial_liesim_extension/metadata.json +++ b/demonstrations_v2/tutorial_liesim_extension/metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2024-06-18T00:00:00+00:00", - "dateOfLastModification": "2025-01-13T00:00:00+00:00", + "dateOfLastModification": "2025-03-17T00:00:00+00:00", "categories": [ "Quantum Computing", "Quantum Machine Learning" diff --git a/poetry.lock b/poetry.lock index d497c996b5..eff89c4dac 100644 --- a/poetry.lock +++ b/poetry.lock @@ -661,138 +661,46 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