From 3d02a26d1c2dd02ec8927646dc221793da4907c4 Mon Sep 17 00:00:00 2001 From: Mudit Pandey Date: Thu, 3 Oct 2024 17:06:21 -0400 Subject: [PATCH 01/40] Remove code being deprecated in v0.39 (#1220) [sc-73935] * Remove deprecated code from demos. * Update various `from pennylane import numpy as np` to `import numpy as np`. --- Makefile | 2 +- conf.py | 3 ++ .../tutorial_backprop.metadata.json | 2 +- demonstrations/tutorial_backprop.py | 35 ++++++++++++------- demonstrations/tutorial_bluequbit.py | 4 +++ .../tutorial_kernels_module.metadata.json | 2 +- demonstrations/tutorial_kernels_module.py | 4 ++- ...al_learning_from_experiments.metadata.json | 2 +- .../tutorial_learning_from_experiments.py | 24 ++++++------- ...utorial_local_cost_functions.metadata.json | 2 +- .../tutorial_local_cost_functions.py | 9 +++-- .../tutorial_mcm_introduction.metadata.json | 2 +- demonstrations/tutorial_mcm_introduction.py | 2 +- .../tutorial_rl_pulse.metadata.json | 2 +- demonstrations/tutorial_rl_pulse.py | 4 +-- .../tutorial_sc_qubits.metadata.json | 2 +- demonstrations/tutorial_sc_qubits.py | 24 ++++++------- .../tutorial_tn_circuits.metadata.json | 2 +- demonstrations/tutorial_tn_circuits.py | 13 +++---- .../tutorial_trapped_ions.metadata.json | 2 +- demonstrations/tutorial_trapped_ions.py | 22 ++++++------ demonstrations/tutorial_vqt.metadata.json | 2 +- demonstrations/tutorial_vqt.py | 18 +++++----- 23 files changed, 103 insertions(+), 81 deletions(-) diff --git a/Makefile b/Makefile index 76860ac0ac..ff5c334968 100644 --- a/Makefile +++ b/Makefile @@ -76,8 +76,8 @@ environment: $$PYTHON_VENV_PATH/bin/python -m pip install --upgrade git+https://github.com/PennyLaneAI/pennylane-cirq.git#egg=pennylane-cirq;\ $$PYTHON_VENV_PATH/bin/python -m pip install --upgrade git+https://github.com/PennyLaneAI/pennylane-qiskit.git#egg=pennylane-qiskit;\ $$PYTHON_VENV_PATH/bin/python -m pip install --upgrade git+https://github.com/PennyLaneAI/pennylane-qulacs.git#egg=pennylane-qulacs;\ - $$PYTHON_VENV_PATH/bin/python -m pip install --extra-index-url https://test.pypi.org/simple/ PennyLane-Lightning --pre --upgrade;\ $$PYTHON_VENV_PATH/bin/python -m pip install --extra-index-url https://test.pypi.org/simple/ PennyLane-Catalyst --pre --upgrade;\ + $$PYTHON_VENV_PATH/bin/python -m pip install --extra-index-url https://test.pypi.org/simple/ PennyLane-Lightning --pre --upgrade;\ $$PYTHON_VENV_PATH/bin/python -m pip install --upgrade git+https://github.com/PennyLaneAI/pennylane.git#egg=pennylane;\ fi;\ fi diff --git a/conf.py b/conf.py index c8a9239f6e..4e105066e1 100644 --- a/conf.py +++ b/conf.py @@ -110,6 +110,9 @@ # Raise PennyLane deprecation warnings as errors warnings.filterwarnings("error", category=PennyLaneDeprecationWarning) +warnings.filterwarnings( + "ignore", message="Device will no longer be accessible", category=PennyLaneDeprecationWarning +) # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] diff --git a/demonstrations/tutorial_backprop.metadata.json b/demonstrations/tutorial_backprop.metadata.json index b472049cb5..fa7b27bdda 100644 --- a/demonstrations/tutorial_backprop.metadata.json +++ b/demonstrations/tutorial_backprop.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2020-08-11T00:00:00+00:00", - "dateOfLastModification": "2024-08-06T00:00:00+00:00", + "dateOfLastModification": "2024-09-19T00:00:00+00:00", "categories": [ "Getting Started" ], diff --git a/demonstrations/tutorial_backprop.py b/demonstrations/tutorial_backprop.py index 6b33f67f6b..469cb1d5ca 100644 --- a/demonstrations/tutorial_backprop.py +++ b/demonstrations/tutorial_backprop.py @@ -55,7 +55,7 @@ Let's have a go implementing the parameter-shift rule manually in PennyLane. """ import pennylane as qml -from jax import numpy as np +from jax import numpy as jnp from matplotlib import pyplot as plt import jax @@ -69,19 +69,28 @@ # create a device to execute the circuit on dev = qml.device("default.qubit", wires=3) + +def CNOT_ring(wires): + """Apply CNOTs in a ring pattern""" + n_wires = len(wires) + + for w in wires: + qml.CNOT([w % n_wires, (w + 1) % n_wires]) + + @qml.qnode(dev, diff_method="parameter-shift") def circuit(params): qml.RX(params[0], wires=0) qml.RY(params[1], wires=1) qml.RZ(params[2], wires=2) - qml.broadcast(qml.CNOT, wires=[0, 1, 2], pattern="ring") + CNOT_ring(wires=[0, 1, 2]) qml.RX(params[3], wires=0) qml.RY(params[4], wires=1) qml.RZ(params[5], wires=2) - qml.broadcast(qml.CNOT, wires=[0, 1, 2], pattern="ring") + CNOT_ring(wires=[0, 1, 2]) return qml.expval(qml.PauliY(0) @ qml.PauliZ(2)) @@ -109,10 +118,10 @@ def circuit(params): def parameter_shift_term(qnode, params, i): shifted = params.copy() - shifted = shifted.at[i].add(np.pi/2) + shifted = shifted.at[i].add(jnp.pi/2) forward = qnode(shifted) # forward evaluation - shifted = shifted.at[i].add(-np.pi) + shifted = shifted.at[i].add(-jnp.pi) backward = qnode(shifted) # backward evaluation return 0.5 * (forward - backward) @@ -125,7 +134,7 @@ def parameter_shift_term(qnode, params, i): # to loop over the index ``i``: def parameter_shift(qnode, params): - gradients = np.zeros([len(params)]) + gradients = jnp.zeros([len(params)]) for i in range(len(params)): gradients = gradients.at[i].set(parameter_shift_term(qnode, params, i)) @@ -147,7 +156,7 @@ def parameter_shift(qnode, params): # Alternatively, we can directly compute quantum gradients of QNodes using # PennyLane's built in :mod:`qml.gradients ` module: -print(np.stack(qml.gradients.param_shift(circuit)(params))) +print(jnp.stack(qml.gradients.param_shift(circuit)(params))) ############################################################################## # If you count the number of quantum evaluations, you will notice that we had to evaluate the circuit @@ -372,10 +381,10 @@ def circuit(params): t = timeit.repeat("grad_qnode_backprop(params)", globals=globals(), number=num, repeat=reps) gradient_backprop.append([num_params, min(t) / num]) -gradient_shift = np.array(gradient_shift).T -gradient_backprop = np.array(gradient_backprop).T -forward_shift = np.array(forward_shift).T -forward_backprop = np.array(forward_backprop).T +gradient_shift = jnp.array(gradient_shift).T +gradient_backprop = jnp.array(gradient_backprop).T +forward_shift = jnp.array(forward_shift).T +forward_backprop = jnp.array(forward_backprop).T ############################################################################## # We now import matplotlib, and plot the results. @@ -419,8 +428,8 @@ def circuit(params): # perform a least squares regression to determine the linear best fit/gradient # for the normalized time vs. number of parameters x = gradient_shift[0] -m_shift, c_shift = np.polyfit(*gradient_shift, deg=1) -m_back, c_back = np.polyfit(*gradient_backprop, deg=1) +m_shift, c_shift = jnp.polyfit(*gradient_shift, deg=1) +m_back, c_back = jnp.polyfit(*gradient_backprop, deg=1) ax.plot(x, m_shift * x + c_shift, '--', label=f"{m_shift:.2f}p{c_shift:+.2f}") ax.plot(x, m_back * x + c_back, '--', label=f"{m_back:.2f}p{c_back:+.2f}") diff --git a/demonstrations/tutorial_bluequbit.py b/demonstrations/tutorial_bluequbit.py index 95f74963e4..574356d8ac 100644 --- a/demonstrations/tutorial_bluequbit.py +++ b/demonstrations/tutorial_bluequbit.py @@ -41,6 +41,10 @@ import matplotlib.pyplot as plt import numpy as np +# This filter will suppress deprecation warnings for viewability +import warnings +warnings.filterwarnings("ignore", "QubitDevice", qml.PennyLaneDeprecationWarning) + def bell_pair(): qml.Hadamard(0) diff --git a/demonstrations/tutorial_kernels_module.metadata.json b/demonstrations/tutorial_kernels_module.metadata.json index 71eb88b6e3..383283de05 100644 --- a/demonstrations/tutorial_kernels_module.metadata.json +++ b/demonstrations/tutorial_kernels_module.metadata.json @@ -21,7 +21,7 @@ } ], "dateOfPublication": "2021-06-24T00:00:00+00:00", - "dateOfLastModification": "2024-08-05T00:00:00+00:00", + "dateOfLastModification": "2024-09-19T00:00:00+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations/tutorial_kernels_module.py b/demonstrations/tutorial_kernels_module.py index e1b5d16c76..8f8ea23d12 100644 --- a/demonstrations/tutorial_kernels_module.py +++ b/demonstrations/tutorial_kernels_module.py @@ -250,7 +250,9 @@ def layer(x, params, wires, i0=0, inc=1): i += inc qml.RY(params[0, j], wires=[wire]) - qml.broadcast(unitary=qml.CRZ, pattern="ring", wires=wires, parameters=params[1]) + n_wires = len(wires) + for p, w in zip(params[1], wires): + qml.CRZ(p, wires=[w % n_wires, (w + 1) % n_wires]) ############################################################################## diff --git a/demonstrations/tutorial_learning_from_experiments.metadata.json b/demonstrations/tutorial_learning_from_experiments.metadata.json index 0c4563082e..3df2006331 100644 --- a/demonstrations/tutorial_learning_from_experiments.metadata.json +++ b/demonstrations/tutorial_learning_from_experiments.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2022-04-18T00:00:00+00:00", - "dateOfLastModification": "2024-08-06T00:00:00+00:00", + "dateOfLastModification": "2024-09-19T00:00:00+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations/tutorial_learning_from_experiments.py b/demonstrations/tutorial_learning_from_experiments.py index 1ab73b9589..47ac16b729 100644 --- a/demonstrations/tutorial_learning_from_experiments.py +++ b/demonstrations/tutorial_learning_from_experiments.py @@ -163,7 +163,7 @@ import pennylane as qml from pennylane.templates.layers import RandomLayers -from pennylane import numpy as np +import numpy as np np.random.seed(234087) @@ -346,6 +346,12 @@ def process_data(raw_data): dev = qml.device("lightning.qubit", wires=qubits * 2, shots=n_shots) +def CNOT_sequence(control_wires, target_wires): + """Apply CNOTs in sequence using the provided control and target wires""" + for c_wire, t_wire in zip(control_wires, target_wires): + qml.CNOT([c_wire, t_wire]) + + @qml.qnode(dev) def enhanced_circuit(ts=False): "implement the enhanced circuit, using a random unitary" @@ -361,14 +367,10 @@ def enhanced_circuit(ts=False): for q in range(qubits): qml.Hadamard(wires=q) - qml.broadcast( - qml.CNOT, pattern=[[q, qubits + q] for q in range(qubits)], wires=range(qubits * 2) - ) + CNOT_sequence(control_wires=range(qubits), target_wires=range(qubits, 2 * qubits)) RandomLayers(weights, wires=range(0, qubits), rotations=ops, seed=seed) RandomLayers(weights, wires=range(qubits, 2 * qubits), rotations=ops, seed=seed) - qml.broadcast( - qml.CNOT, pattern=[[q, qubits + q] for q in range(qubits)], wires=range(qubits * 2) - ) + CNOT_sequence(control_wires=range(qubits), target_wires=range(qubits, 2 * qubits)) for q in range(qubits): qml.Hadamard(wires=q) @@ -479,15 +481,11 @@ def enhanced_circuit(ts=False): for q in range(qubits): qml.Hadamard(wires=q) - qml.broadcast( - qml.CNOT, pattern=[[q, qubits + q] for q in range(qubits)], wires=range(qubits * 2) - ) + CNOT_sequence(control_wires=range(qubits), target_wires=range(qubits, 2 * qubits)) RandomLayers(weights, wires=range(0, qubits), rotations=ops, seed=seed) RandomLayers(weights, wires=range(qubits, 2 * qubits), rotations=ops, seed=seed) noise_layer(np.pi / 4) # added noise layer - qml.broadcast( - qml.CNOT, pattern=[[qubits + q, q] for q in range(qubits)], wires=range(qubits * 2) - ) + CNOT_sequence(control_wires=range(qubits, 2 * qubits), target_wires=range(qubits)) for q in range(qubits): qml.Hadamard(wires=qubits + q) diff --git a/demonstrations/tutorial_local_cost_functions.metadata.json b/demonstrations/tutorial_local_cost_functions.metadata.json index 91b2ec37d6..1f1e2ffcc7 100644 --- a/demonstrations/tutorial_local_cost_functions.metadata.json +++ b/demonstrations/tutorial_local_cost_functions.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2020-09-09T00:00:00+00:00", - "dateOfLastModification": "2024-08-05T00:00:00+00:00", + "dateOfLastModification": "2024-09-19T00:00:00+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations/tutorial_local_cost_functions.py b/demonstrations/tutorial_local_cost_functions.py index 959df983aa..f981a9f485 100644 --- a/demonstrations/tutorial_local_cost_functions.py +++ b/demonstrations/tutorial_local_cost_functions.py @@ -241,14 +241,16 @@ def global_cost_simple(rotations): for i in range(wires): qml.RX(rotations[0][i], wires=i) qml.RY(rotations[1][i], wires=i) - qml.broadcast(qml.CNOT, wires=range(wires), pattern="chain") + for i in range(wires - 1): + qml.CNOT([i, i + 1]) return qml.probs(wires=range(wires)) def local_cost_simple(rotations): for i in range(wires): qml.RX(rotations[0][i], wires=i) qml.RY(rotations[1][i], wires=i) - qml.broadcast(qml.CNOT, wires=range(wires), pattern="chain") + for i in range(wires - 1): + qml.CNOT([i, i + 1]) return qml.probs(wires=[0]) global_circuit = qml.QNode(global_cost_simple, dev, interface="autograd") @@ -371,7 +373,8 @@ def tunable_cost_simple(rotations): for i in range(wires): qml.RX(rotations[0][i], wires=i) qml.RY(rotations[1][i], wires=i) - qml.broadcast(qml.CNOT, wires=range(wires), pattern="chain") + for i in range(wires - 1): + qml.CNOT([i, i + 1]) return qml.probs(range(locality)) def cost_tunable(rotations): diff --git a/demonstrations/tutorial_mcm_introduction.metadata.json b/demonstrations/tutorial_mcm_introduction.metadata.json index 586633d57e..6aacafa406 100644 --- a/demonstrations/tutorial_mcm_introduction.metadata.json +++ b/demonstrations/tutorial_mcm_introduction.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2024-05-10T00:00:00+00:00", - "dateOfLastModification": "2024-08-05T00:00:00+00:00", + "dateOfLastModification": "2024-09-19T00:00:00+00:00", "categories": [ "Getting Started", "Quantum Computing" diff --git a/demonstrations/tutorial_mcm_introduction.py b/demonstrations/tutorial_mcm_introduction.py index ccac69f82d..39c887fd35 100644 --- a/demonstrations/tutorial_mcm_introduction.py +++ b/demonstrations/tutorial_mcm_introduction.py @@ -360,7 +360,7 @@ def bell_pair_with_reset(reset): magic_state = np.array([1, np.exp(1j * np.pi / 4)]) / np.sqrt(2) def t_gadget(wire, aux_wire): - qml.QubitStateVector(magic_state, aux_wire) + qml.StatePrep(magic_state, aux_wire) qml.CNOT([wire, aux_wire]) mcm = qml.measure(aux_wire, reset=True) # Resetting disentangles aux qubit qml.cond(mcm, qml.S)(wire) # Apply qml.S(wire) if mcm was 1 diff --git a/demonstrations/tutorial_rl_pulse.metadata.json b/demonstrations/tutorial_rl_pulse.metadata.json index 0e59a897ba..1dd4af105f 100644 --- a/demonstrations/tutorial_rl_pulse.metadata.json +++ b/demonstrations/tutorial_rl_pulse.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2024-04-09T00:00:00+00:00", - "dateOfLastModification": "2024-09-04T00:00:00+00:00", + "dateOfLastModification": "2024-09-19T00:00:00+00:00", "categories": [ "Getting Started", "Optimization", diff --git a/demonstrations/tutorial_rl_pulse.py b/demonstrations/tutorial_rl_pulse.py index 08316f432a..7fbc9eab20 100644 --- a/demonstrations/tutorial_rl_pulse.py +++ b/demonstrations/tutorial_rl_pulse.py @@ -224,7 +224,7 @@ @partial(jax.vmap, in_axes=(0, None, 0, None)) @qml.qnode(device=device, interface="jax") def evolve_states(state, H, params, t): - qml.QubitStateVector(state, wires=wires) + qml.StatePrep(state, wires=wires) qml.evolve(H)(params, t, atol=1e-5) return qml.state() @@ -929,7 +929,7 @@ def get_drive(timespan, freq, wire): @qml.qnode(device=device, interface="jax") def evolve_states(state, params, t): params_sq, params_cr = params - qml.QubitStateVector(state, wires=wires) + qml.StatePrep(state, wires=wires) # Single qubit pulses qml.evolve(H_int + H_sq_ini)(params_sq, t, atol=1e-5) diff --git a/demonstrations/tutorial_sc_qubits.metadata.json b/demonstrations/tutorial_sc_qubits.metadata.json index 96bd72975d..29137dd18d 100644 --- a/demonstrations/tutorial_sc_qubits.metadata.json +++ b/demonstrations/tutorial_sc_qubits.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2022-03-22T00:00:00+00:00", - "dateOfLastModification": "2024-08-06T00:00:00+00:00", + "dateOfLastModification": "2024-09-19T00:00:00+00:00", "categories": [ "Quantum Hardware", "Quantum Computing" diff --git a/demonstrations/tutorial_sc_qubits.py b/demonstrations/tutorial_sc_qubits.py index f1f1fd8da5..9de73a5a3d 100644 --- a/demonstrations/tutorial_sc_qubits.py +++ b/demonstrations/tutorial_sc_qubits.py @@ -404,7 +404,7 @@ # angle :math:`\phi` actually means a rotation by :math:`\omega_r+\phi.` In PennyLane, the operations read: import pennylane as qml -from pennylane import numpy as np +import numpy as np import matplotlib.pyplot as plt # Call the default.gaussian device with 50 shots @@ -416,14 +416,14 @@ # Implement displacement and rotation and measure both X and P observables -@qml.qnode(dev, interface="autograd") +@qml.qnode(dev) def measure_P_shots(time, state): qml.Displacement(epsilon * time, 0, wires=0) qml.Rotation((-1) ** state * chi * time, wires=0) return qml.sample(qml.QuadP(0)) -@qml.qnode(dev, interface="autograd") +@qml.qnode(dev) def measure_X_shots(time, state): qml.Displacement(epsilon * time, 0, wires=0) qml.Rotation((-1) ** state * chi * time, wires=0) @@ -499,7 +499,7 @@ def measure_X_shots(time, state): dev2 = qml.device("lightning.qubit", wires=1) # Implement Hamiltonian evolution given phase phi and time t, from a given initial state -@qml.qnode(dev2, interface="autograd") +@qml.qnode(dev2) def H_evolve(state, phi, time): if state == 1: @@ -513,7 +513,7 @@ def H_evolve(state, phi, time): # Implement X rotation exactly -@qml.qnode(dev2, interface="autograd") +@qml.qnode(dev2) def Sc_X_rot(state, phi): if state == 1: @@ -524,7 +524,7 @@ def Sc_X_rot(state, phi): # Implement Y rotation exactly -@qml.qnode(dev2, interface="autograd") +@qml.qnode(dev2) def Sc_Y_rot(state, phi): if state == 1: @@ -617,17 +617,17 @@ def Sc_Y_rot(state, phi): Two_qubit_H = qml.Hamiltonian(coeffs, ops) # Implement Hamiltonian evolution for time t and some initial computational basis state -@qml.qnode(dev3, interface="autograd") +@qml.qnode(dev3) def Sc_ISWAP(basis_state, time): - qml.templates.BasisStatePreparation(basis_state, wires=range(2)) + qml.BasisState(basis_state, wires=range(2)) ApproxTimeEvolution(Two_qubit_H, time, 1) return qml.state() # Implement ISWAP exactly -@qml.qnode(dev3, interface="autograd") +@qml.qnode(dev3) def iswap(basis_state): - qml.templates.BasisStatePreparation(basis_state, wires=range(2)) + qml.BasisState(basis_state, wires=range(2)) qml.ISWAP(wires=[0, 1]) return qml.state() @@ -732,10 +732,10 @@ def cnot_with_iswap(): # the evolution under this Hamiltonian for a time :math:`t=\tfrac{\pi}{4\Omega}` with :math:`R_x` and :math:`R_y` rotations # and a ``qml.Hadamard`` gate: # -@qml.qnode(dev3, interface="autograd") +@qml.qnode(dev3) def H_evolve(state, phi, time): # Prepare initial state - qml.templates.BasisStatePreparation(state, wires=range(2)) + qml.BasisState(state, wires=range(2)) # Define Hamiltonian coeffs = [np.cos(phi), np.sin(phi)] ops = [qml.PauliZ(0) @ qml.PauliX(1), qml.PauliZ(0) @ qml.PauliY(1)] diff --git a/demonstrations/tutorial_tn_circuits.metadata.json b/demonstrations/tutorial_tn_circuits.metadata.json index f0dde1deb3..5d33fa07cf 100644 --- a/demonstrations/tutorial_tn_circuits.metadata.json +++ b/demonstrations/tutorial_tn_circuits.metadata.json @@ -15,7 +15,7 @@ } ], "dateOfPublication": "2022-03-29T00:00:00+00:00", - "dateOfLastModification": "2024-08-12T00:00:00+00:00", + "dateOfLastModification": "2024-09-19T00:00:00+00:00", "categories": [ "Quantum Machine Learning" ], diff --git a/demonstrations/tutorial_tn_circuits.py b/demonstrations/tutorial_tn_circuits.py index 03e083f3f2..e317690f13 100644 --- a/demonstrations/tutorial_tn_circuits.py +++ b/demonstrations/tutorial_tn_circuits.py @@ -116,6 +116,7 @@ of tensors in the network. """ +import numpy as onp import pennylane as qml from pennylane import numpy as np @@ -232,8 +233,8 @@ def circuit(template_weights): shapes = qml.SimplifiedTwoDesign.shape(n_layers=1, n_wires=4) -weights = [np.random.random(size=shape) for shape in shapes] -template_weights = qml._np.array([weights] * 3, dtype="object") +weights = [onp.random.random(size=shape) for shape in shapes] +template_weights = onp.array([weights] * 3, dtype="object") fig, ax = qml.draw_mpl(circuit, level="device")(template_weights) ############################################################################## @@ -317,11 +318,11 @@ def block(weights, wires): ############################################################################## # As for the tensor-network architecture, we use the tree tensor-network quantum circuit. -# We use :class:`~pennylane.BasisStatePreparation` to encode the input images. -# The following code implements the :class:`~pennylane.BasisStatePreparation` encoding, +# We use :class:`~pennylane.BasisState` to encode the input images. +# The following code implements the :class:`~pennylane.BasisState` encoding, # followed by a :class:`~pennylane.TTN` circuit using the above ``block``. Finally, we compute the expectation # value of a :class:`~pennylane.PauliZ` measurement as the output. -# The circuit diagram below shows the full circuit. The :class:`~pennylane.BasisStatePreparation` +# The circuit diagram below shows the full circuit. The :class:`~pennylane.BasisState` # encoding appears in the initial :class:`~pennylane.PauliX` gates. dev = qml.device("default.qubit", wires=4) @@ -329,7 +330,7 @@ def block(weights, wires): @qml.qnode(dev) def circuit(image, template_weights): - qml.BasisStatePreparation(image, wires=range(4)) + qml.BasisState(image, wires=range(4)) qml.TTN( wires=range(4), n_block_wires=2, diff --git a/demonstrations/tutorial_trapped_ions.metadata.json b/demonstrations/tutorial_trapped_ions.metadata.json index 977922539a..40e477bcc7 100644 --- a/demonstrations/tutorial_trapped_ions.metadata.json +++ b/demonstrations/tutorial_trapped_ions.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2021-11-10T00:00:00+00:00", - "dateOfLastModification": "2024-08-06T00:00:00+00:00", + "dateOfLastModification": "2024-09-19T00:00:00+00:00", "categories": [ "Quantum Hardware", "Quantum Computing" diff --git a/demonstrations/tutorial_trapped_ions.py b/demonstrations/tutorial_trapped_ions.py index 778eb1d512..507d970bdc 100644 --- a/demonstrations/tutorial_trapped_ions.py +++ b/demonstrations/tutorial_trapped_ions.py @@ -401,7 +401,7 @@ # duration :math:`t` of the pulse, with :math:`\Omega` set to 100 kHz. import pennylane as qml -from pennylane import numpy as np +import numpy as np from scipy.linalg import expm Omega = 100 @@ -422,7 +422,7 @@ def evolution(phi, t): dev = qml.device("default.qubit", wires=1) -@qml.qnode(dev, interface="autograd") +@qml.qnode(dev) def ion_hadamard(state): if state == 1: @@ -440,7 +440,7 @@ def ion_hadamard(state): return qml.state() #For comparison, we use the Hadamard built into PennyLane -@qml.qnode(dev, interface="autograd") +@qml.qnode(dev) def hadamard(state): if state == 1: @@ -459,7 +459,7 @@ def hadamard(state): # A similar exercise can be done for the :math:`T` gate: -@qml.qnode(dev, interface="autograd") +@qml.qnode(dev) def ion_Tgate(state): if state == 1: @@ -472,7 +472,7 @@ def ion_Tgate(state): return qml.state() -@qml.qnode(dev, interface="autograd") +@qml.qnode(dev) def tgate(state): if state == 1: @@ -504,7 +504,7 @@ def tgate(state): import matplotlib.pyplot as plt -@qml.qnode(dev, interface="autograd") +@qml.qnode(dev) def evolution_prob(t): qml.QubitUnitary(evolution(0, t / Omega), wires=0) @@ -513,7 +513,7 @@ def evolution_prob(t): t = np.linspace(0, 4 * np.pi, 101) -s = [evolution_prob(i)[1].numpy() for i in t] +s = [evolution_prob(i)[1] for i in t] fig1, ax1 = plt.subplots(figsize=(9, 6)) @@ -811,11 +811,11 @@ def Molmer_Sorensen(t): dev2 = qml.device("default.qubit",wires=2) -@qml.qnode(dev2, interface="autograd") +@qml.qnode(dev2) def ion_cnot(basis_state): #Prepare the two-qubit basis states from the input - qml.templates.BasisStatePreparation(basis_state, wires=range(2)) + qml.BasisState(basis_state, wires=range(2)) #Implements the circuit shown above qml.RY(np.pi/2, wires=0) @@ -827,10 +827,10 @@ def ion_cnot(basis_state): return qml.state() #Compare with built-in CNOT -@qml.qnode(dev2, interface="autograd") +@qml.qnode(dev2) def cnot_gate(basis_state): - qml.templates.BasisStatePreparation(basis_state, wires=range(2)) + qml.BasisState(basis_state, wires=range(2)) qml.CNOT(wires=[0,1]) diff --git a/demonstrations/tutorial_vqt.metadata.json b/demonstrations/tutorial_vqt.metadata.json index 7f5b69076d..567a2b9dc7 100644 --- a/demonstrations/tutorial_vqt.metadata.json +++ b/demonstrations/tutorial_vqt.metadata.json @@ -6,7 +6,7 @@ } ], "dateOfPublication": "2020-07-07T00:00:00+00:00", - "dateOfLastModification": "2024-08-05T00:00:00+00:00", + "dateOfLastModification": "2024-09-19T00:00:00+00:00", "categories": [ "Optimization" ], diff --git a/demonstrations/tutorial_vqt.py b/demonstrations/tutorial_vqt.py index 3b3b324994..4ffce13164 100644 --- a/demonstrations/tutorial_vqt.py +++ b/demonstrations/tutorial_vqt.py @@ -110,7 +110,6 @@ import pennylane as qml from matplotlib import pyplot as plt import numpy as np -from numpy import array import scipy from scipy.optimize import minimize import networkx as nx @@ -250,6 +249,14 @@ def single_rotation(phi_params, qubits): # +def CRX_ring(parameters, wires): + """Apply a controlled RX on all wires in a ring pattern""" + n_wires = len(wires) + + for param, w in zip(parameters, wires): + qml.CRX(param, wires=[w % n_wires, (w + 1) % n_wires]) + + depth = 4 dev = qml.device("lightning.qubit", wires=nr_qubits) @@ -257,17 +264,12 @@ def single_rotation(phi_params, qubits): def quantum_circuit(rotation_params, coupling_params, sample=None, return_state=False): # Prepares the initial basis state corresponding to the sample - qml.BasisStatePreparation(sample, wires=range(nr_qubits)) + qml.BasisState(sample, wires=range(nr_qubits)) # Prepares the variational ansatz for the circuit for i in range(0, depth): single_rotation(rotation_params[i], range(nr_qubits)) - qml.broadcast( - unitary=qml.CRX, - pattern="ring", - wires=range(nr_qubits), - parameters=coupling_params[i], - ) + CRX_ring(coupling_params[i], list(range(nr_qubits))) if return_state: return qml.state() From 31c79993e6e3fe04b6f8cacb40f0df832ebb6f83 Mon Sep 17 00:00:00 2001 From: soranjh Date: Fri, 4 Oct 2024 13:26:31 -0400 Subject: [PATCH 02/40] add first version --- ...rial_how_to_spin_hamiltonian.metadata.json | 43 +++++ .../tutorial_how_to_spin_hamiltonian.py | 176 ++++++++++++++++++ 2 files changed, 219 insertions(+) create mode 100644 demonstrations/tutorial_how_to_spin_hamiltonian.metadata.json create mode 100644 demonstrations/tutorial_how_to_spin_hamiltonian.py diff --git a/demonstrations/tutorial_how_to_spin_hamiltonian.metadata.json b/demonstrations/tutorial_how_to_spin_hamiltonian.metadata.json new file mode 100644 index 0000000000..6cd2c109b8 --- /dev/null +++ b/demonstrations/tutorial_how_to_spin_hamiltonian.metadata.json @@ -0,0 +1,43 @@ +{ + "title": "How to build spin Hamiltonians", + "authors": [ + { + "username": "dikshadhawan" + } + ], + "dateOfPublication": "2024-10-04T00:00:00+00:00", + "dateOfLastModification": "2024-10-04T00:00:00+00:00", + "categories": [ + "Getting Started", + "How-to" + ], + "tags": [], + "previewImages": [ + { + "type": "thumbnail", + "uri": "/_static/demo_thumbnails/regular_demo_thumbnails/thumbnail_how_to_use_registers.png" + }, + { + "type": "large_thumbnail", + "uri": "/_static/demo_thumbnails/large_demo_thumbnails/thumbnail_large_how_to_use_registers.png" + } + ], + "seoDescription": "Learn how to build spin Hamiltonians with PennyLane.", + "doi": "", + "canonicalURL": "/qml/demos/tutorial_how_to_spin_hamiltonian", + "references": [], + "basedOnPapers": [], + "referencedByPapers": [], + "relatedContent": [ + { + "type": "demonstration", + "id": "tutorial_quantum_chemistry", + "weight": 1.0 + }, + { + "type": "demonstration", + "id": "tutorial_fermionic_operators", + "weight": 1.0 + } + ] +} diff --git a/demonstrations/tutorial_how_to_spin_hamiltonian.py b/demonstrations/tutorial_how_to_spin_hamiltonian.py new file mode 100644 index 0000000000..12c247f841 --- /dev/null +++ b/demonstrations/tutorial_how_to_spin_hamiltonian.py @@ -0,0 +1,176 @@ +r"""How to build spin Hamiltonians +================================== +Spin systems provide simple but powerful models for studying problems in physics, chemistry and +quantum computing. The quantum spin models are typically described by a Hamiltonian that can be used +in a quantum algorithm to explore properties of the spin modes that are intractable with classical +computational methods. PennyLane provides a powerful set of tools that enables the users to +intuitively construct a broad range of spin Hamiltonians. Here we show you how to use these tools +for your problem of interest. +""" + +###################################################################### +# Hamiltonian templates +# --------------------- +# +# PennyLane has a range of functions for constructing spin model Hamiltonians with minimal input +# data needed from the user. Let’s look at the Fermi-Hubbard model as an example. This model can +# represent a chain of hydrogen atoms where each atom, or site, can hold one spin-up and one +# spin-down particle. The Hamiltonian describing this model has two components: the kinetic energy +# component which is parameterized by a hopping parameter and the potential energy component +# parameterized by the on-site interaction strength. The Fermi-Hubbard Hamiltonian can then be +# constructed in PennyLane by passing the hoping and interaction parameters to the fermi_hubbard +# function. We also need to provide information about the number of sites we would like to be +# included in our Hamiltonian. +# + +import pennylane as qml +n = [2] +t = 0.2 +u = 0.3 +hamiltonian = qml.spin.fermi_hubbard("chain", n, t, u) +hamiltonian + +###################################################################### +# The fermi_hubbard function is general enough to go beyond the simple “chain” model and construct +# the Hamiltonian for a wide range of two-dimensional and three-dimensional lattice shapes. For +# those cases, we need to provide the number of sites in each direction of the lattice. We can +# construct the Hamiltonian for a cubic lattice with 3 sites on each direction as + +hamiltonian = qml.spin.fermi_hubbard("cubic", [3, 3, 3], t, u) +hamiltonian + +###################################################################### +# Similarity, a broad range of other well-investigated spin model Hamiltonians can be constructed +# with the dedicated functions available in the spin module, by just providing the lattice +# information and the Hamiltonian parameters. +# +# Building Hamiltonians manually +# ------------------------------ +# +# The Hamiltonian template functions are great and simple tools for someone who just wants to build +# a Hamiltonian quickly. However, PennyLane offers intuitive tools that can be used to construct +# spin Hamiltonians manually which are very handy for building customized Hamiltonians. Let’s learn +# how to use this tools by constructing the Hamiltonian for the transverse field Ising model on a +# two-dimension lattice. +# +# The Hamiltonian is represented as: +# +# .. math:: +# +# \hat{H} = -J \sum_{} \sigma_i^{z} \sigma_j^{z} - h\sum_{i} \sigma_{i}^{x}, +# +# where :math:`J` is the coupling defined for the Hamiltonian, :math:`h` is the strength of +# transverse magnetic field and :math:`i,j` represent the indices for neighbouring spins. +# +# Our approach for doing this is to construct a Lattice object that represents the spin sites and +# their connectivity. The Lattice object can be constructed by defining the unit cell translation +# vectors, the positions of the sites and the number of unit cells in the lattice. Once we have the +# Lattice object, we can use its attributes, e.g., ``vertices`` and ``edges``, to construct our +# custom Hamiltonian. + +from pennylan.spin import Lattice +from pennylane import X, Y, Z +import numpy as np + +# The unit cell translation vectors +vectors = [[1, 0], [0, 1]] + +# The coordinates of the nodes inside the unit cell +nodes = [[0, 0]] + +# Number of unit cells in each direction +n_cells = [2, 2] + +# Lattice representing the system +lattice = Lattice(n_cells, vectors, nodes) + +# Coupling and onsite parameters +coupling, onsite = 1.0, 10.0 + +# Construct the Hamiltonian manually +def h_custom(lattice, coupling, onsite): + hamiltonian = 0.0 + + # add two-site terms + for edge in lattice.edges: + i, j = edge[0], edge[1] + hamiltonian += - coupling * (Z(i) @ Z(j)) + + # add one-site terms + for vertex in range(lattice.vertices): + hamiltonian += -onsite * X(vertex) + + return hamiltonian + + +h_custom(lattice, coupling, onsite) + +###################################################################### +# We also have a helper function construct_lattice that helps you construct the lattice object just +# by passing the shape of the lattice without defining the nodes and the translation vectors. +# +# The lattice object is a very flexible and versatile tool that allows you construct more +# complicated Hamiltonians. To show this ability, let’s look at a more advanced example. +# +# Building customized Hamiltonians +# -------------------------------- +# +# Now let's look at a more complicated example and see how our existing tools allow building such +# Hamiltonians intuitively. We chose the anisotropic Kitaev Honeycomb model where the coupling +# parameters depend on the orientation of the bonds. We can build the Hamiltonian by building the +# lattice manually and adding custom edges between the nodes. The custom edges can be defined based +# on the nodes they connect. Currently, we use the following format to define a custom 'XX' edge +# with coupling constant 0.5 between nodes 0 and 1: + +custom_edge = [[(0, 1), ('XX', 0.5)] + +###################################################################### +# We can also support a UI where the user defines the neighbour vectors manually. This option is a +# bit complicated and is abstracted away in the current UI. Let's now build our Hamiltonian. +# The unit cell translation vectors +vectors = [[1, 0], [0.5, np.sqrt(3) / 2]] + +# The coordinates of the nodes inside the unit cell +nodes = [[0.5, 0.5 / 3 ** 0.5], [1, 1 / 3 ** 0.5]] + +# Number of unit cells in each direction +n_cells = [3, 3] + +# Add custom edges to the lattice +# the first term adds custom 'XX' edge with +# coupling constant 0.5 between 0 and 1 nodes +custom_edges = [[(0, 1), ('XX', 0.5)], + [(1, 2), ('YY', 0.6)], + [(1, 6), ('ZZ', 0.7)], + [(3, 6), ('XY', 0.8)]] + +# Lattice representing the system +lattice = Lattice(n_cells, vectors, nodes, custom_edges=custom_edges) + +# Construct the Hamiltonian manually +def h_custom(lattice): + opmap = {'X': X, 'Y': Y, 'Z': Z} + + hamiltonian = 0.0 + + # add two-site terms + for edge in lattice.edges: + i, j = edge[0], edge[1] + k, l = edge[2][0][0], edge[2][0][1] + hamiltonian += opmap[k](i) @ opmap[l](j) * edge[2][1] + return hamiltonian + +h_custom(lattice) + +###################################################################### +# You can compare the constructed Hamiltonian with the template we already have for the Kitaev +# model. +# +# Conclusion +# ---------- +# To be added ... +# +# About the author +# ---------------- +# +# .. include:: ../_static/authors/diksha_dhawan.txt From e2398dfb838928015ceb58271c526dd1f26ff361 Mon Sep 17 00:00:00 2001 From: soranjh Date: Mon, 7 Oct 2024 11:32:59 -0400 Subject: [PATCH 03/40] add section for customized ham --- .../tutorial_how_to_spin_hamiltonian.py | 160 ++++++++++-------- 1 file changed, 92 insertions(+), 68 deletions(-) diff --git a/demonstrations/tutorial_how_to_spin_hamiltonian.py b/demonstrations/tutorial_how_to_spin_hamiltonian.py index 12c247f841..e6ed5d5ac4 100644 --- a/demonstrations/tutorial_how_to_spin_hamiltonian.py +++ b/demonstrations/tutorial_how_to_spin_hamiltonian.py @@ -24,9 +24,11 @@ # import pennylane as qml + n = [2] t = 0.2 u = 0.3 + hamiltonian = qml.spin.fermi_hubbard("chain", n, t, u) hamiltonian @@ -37,7 +39,6 @@ # construct the Hamiltonian for a cubic lattice with 3 sites on each direction as hamiltonian = qml.spin.fermi_hubbard("cubic", [3, 3, 3], t, u) -hamiltonian ###################################################################### # Similarity, a broad range of other well-investigated spin model Hamiltonians can be constructed @@ -62,109 +63,132 @@ # where :math:`J` is the coupling defined for the Hamiltonian, :math:`h` is the strength of # transverse magnetic field and :math:`i,j` represent the indices for neighbouring spins. # -# Our approach for doing this is to construct a Lattice object that represents the spin sites and -# their connectivity. The Lattice object can be constructed by defining the unit cell translation -# vectors, the positions of the sites and the number of unit cells in the lattice. Once we have the -# Lattice object, we can use its attributes, e.g., ``vertices`` and ``edges``, to construct our -# custom Hamiltonian. +# Our approach for doing this is to construct a lattice that represents the spin sites and +# their connectivity. This is done by using the Lattice class that can be constructed either by +# calling the helper function generate_lattice or by manually constructing the object. Let's see +# examples of both methods. First we use generate_lattice to construct a square lattice containing 9 +# sites which are all connected to their nearest neighbor. + +lattice = qml.spin.lattice._generate_lattice('square', [3, 3]) + +###################################################################### +# Let's visualize this lattice to see how it looks: -from pennylan.spin import Lattice -from pennylane import X, Y, Z import numpy as np +import matplotlib.pyplot as plt -# The unit cell translation vectors -vectors = [[1, 0], [0, 1]] +plt.figure(figsize=lattice.n_cells[::-1]) -# The coordinates of the nodes inside the unit cell -nodes = [[0, 0]] +nodes = lattice.lattice_points -# Number of unit cells in each direction -n_cells = [2, 2] +for edge in lattice.edges: -# Lattice representing the system -lattice = Lattice(n_cells, vectors, nodes) + start_index, end_index, color = edge + start_pos, end_pos = nodes[start_index], nodes[end_index] -# Coupling and onsite parameters -coupling, onsite = 1.0, 10.0 + x_axis = [start_pos[0], end_pos[0]] + y_axis = [start_pos[1], end_pos[1]] + plt.plot(x_axis, y_axis, color='gold') -# Construct the Hamiltonian manually -def h_custom(lattice, coupling, onsite): - hamiltonian = 0.0 + plt.scatter(nodes[:,0], nodes[:,1], color='dodgerblue', s=100) + for index, pos in enumerate(nodes): + plt.text(pos[0]-0.2, pos[1]+0.1, str(index), color='gray') - # add two-site terms - for edge in lattice.edges: - i, j = edge[0], edge[1] - hamiltonian += - coupling * (Z(i) @ Z(j)) +plt.axis("off") +plt.show() - # add one-site terms - for vertex in range(lattice.vertices): - hamiltonian += -onsite * X(vertex) +###################################################################### +# Now, we construct the same lattice manually by explicitly defining the positions of the sites in a +# unit cell, the translation vectors defining the lattice and the number of cells in each direction. - return hamiltonian +from pennylane.spin import Lattice +nodes = [[0, 0]] # coordinates of nodes inside the unit cell +vectors = [[1, 0], [0, 1]] # unit cell translation vectors +n_cells = [3, 3] # number of unit cells in each direction -h_custom(lattice, coupling, onsite) +lattice = Lattice(n_cells, vectors, nodes) ###################################################################### -# We also have a helper function construct_lattice that helps you construct the lattice object just -# by passing the shape of the lattice without defining the nodes and the translation vectors. +# This gives us the same lattice as we created with generate_lattice but constructing the lattice +# manually is more flexible while generate_lattice only works for some predefined lattice shapes. # -# The lattice object is a very flexible and versatile tool that allows you construct more -# complicated Hamiltonians. To show this ability, let’s look at a more advanced example. +# Now that we have the lattice, we can use its attributes, e.g., edges and vertices, to construct +# our transverse field Isingmodel Hamiltonian. We just need to define the coupling and onsite +# parameters + +from pennylane import X, Y, Z + +coupling, onsite = 1.0, 1.0 + +hamiltonian = 0.0 +# add the one-site terms +for vertex in range(lattice.n_sites): + hamiltonian += -onsite * X(vertex) +# add the coupling terms +for edge in lattice.edges_indices: + i, j = edge[0], edge[1] + hamiltonian += - coupling * (Z(i) @ Z(j)) + +hamiltonian + +###################################################################### +# In this example we just used the in-built attributes of the lattice we created without further +# customising them. The lattice can be constructed in a very flexible way that allows constructing +# customized Hamiltonians. Let's look at an example. # # Building customized Hamiltonians # -------------------------------- -# -# Now let's look at a more complicated example and see how our existing tools allow building such -# Hamiltonians intuitively. We chose the anisotropic Kitaev Honeycomb model where the coupling -# parameters depend on the orientation of the bonds. We can build the Hamiltonian by building the -# lattice manually and adding custom edges between the nodes. The custom edges can be defined based -# on the nodes they connect. Currently, we use the following format to define a custom 'XX' edge -# with coupling constant 0.5 between nodes 0 and 1: +# Now we work on a more complicated Hamiltonian to see how our existing tools allow building it +# intuitively. We chose the anisotropic Kitaev Honeycomb model where the coupling +# parameters depend on the orientation of the bonds. [need an image here] We can build the Hamiltonian by building the +# lattice manually and adding custom edges between the nodes. For instance, to define a custom 'XX' +# edge with coupling constant 0.5 between nodes 0 and 1, we use: -custom_edge = [[(0, 1), ('XX', 0.5)] +custom_edge = [(0, 1), ('XX', 0.5)] ###################################################################### -# We can also support a UI where the user defines the neighbour vectors manually. This option is a -# bit complicated and is abstracted away in the current UI. Let's now build our Hamiltonian. -# The unit cell translation vectors -vectors = [[1, 0], [0.5, np.sqrt(3) / 2]] +# Let's now build our Hamiltonian. We first define the unit cell by specifying the positions of the +# nodes and the unit cell translation vector and then define the number of unit cells in each +# direction. -# The coordinates of the nodes inside the unit cell nodes = [[0.5, 0.5 / 3 ** 0.5], [1, 1 / 3 ** 0.5]] - -# Number of unit cells in each direction +vectors = [[1, 0], [0.5, np.sqrt(3) / 2]] n_cells = [3, 3] -# Add custom edges to the lattice -# the first term adds custom 'XX' edge with -# coupling constant 0.5 between 0 and 1 nodes +###################################################################### +# Now we add custom edges to the lattice. We have three different edge orientations that we define +# as + custom_edges = [[(0, 1), ('XX', 0.5)], [(1, 2), ('YY', 0.6)], - [(1, 6), ('ZZ', 0.7)], - [(3, 6), ('XY', 0.8)]] + [(1, 6), ('ZZ', 0.7)]] + +###################################################################### +# Then we construct the lattice and pass it to the spin_hamiltonian function, which is a helper +# function that constructs a Hamiltonian from a lattice. and the Hamiltonian similar to other examples. -# Lattice representing the system lattice = Lattice(n_cells, vectors, nodes, custom_edges=custom_edges) +hamiltonian = qml.spin.spin_hamiltonian(lattice=lattice) + +###################################################################### +# The spin_hamiltonian function has a simple logic and loops over the custom edges and nodes +# to build the Hamiltonian. In our example, we can also manually do that with a simple code. -# Construct the Hamiltonian manually -def h_custom(lattice): - opmap = {'X': X, 'Y': Y, 'Z': Z} - hamiltonian = 0.0 +opmap = {'X': X, 'Y': Y, 'Z': Z} - # add two-site terms - for edge in lattice.edges: - i, j = edge[0], edge[1] - k, l = edge[2][0][0], edge[2][0][1] - hamiltonian += opmap[k](i) @ opmap[l](j) * edge[2][1] - return hamiltonian +hamiltonian = 0.0 +for edge in lattice.edges: + i, j = edge[0], edge[1] + k, l = edge[2][0][0], edge[2][0][1] + hamiltonian += opmap[k](i) @ opmap[l](j) * edge[2][1] -h_custom(lattice) +hamiltonian ###################################################################### -# You can compare the constructed Hamiltonian with the template we already have for the Kitaev -# model. +# We compare the constructed Hamiltonian with the template we already have for the Kitaev +# model. Can you compare them and verify that the Hamiltonians are the same? # # Conclusion # ---------- From 3d7b93a6a0f22ea90c23975ae1a3cc64d917263e Mon Sep 17 00:00:00 2001 From: soranjh Date: Mon, 7 Oct 2024 13:19:11 -0400 Subject: [PATCH 04/40] update plot function --- .../tutorial_how_to_spin_hamiltonian.py | 48 +++++++++++-------- 1 file changed, 29 insertions(+), 19 deletions(-) diff --git a/demonstrations/tutorial_how_to_spin_hamiltonian.py b/demonstrations/tutorial_how_to_spin_hamiltonian.py index e6ed5d5ac4..07ae40aeb4 100644 --- a/demonstrations/tutorial_how_to_spin_hamiltonian.py +++ b/demonstrations/tutorial_how_to_spin_hamiltonian.py @@ -72,30 +72,33 @@ lattice = qml.spin.lattice._generate_lattice('square', [3, 3]) ###################################################################### -# Let's visualize this lattice to see how it looks: +# Let's visualize this lattice to see how it looks. We create a simple function for plotting the +# lattice. import numpy as np import matplotlib.pyplot as plt -plt.figure(figsize=lattice.n_cells[::-1]) +def plot(lattice): -nodes = lattice.lattice_points + plt.figure(figsize=lattice.n_cells[::-1]) + nodes = lattice.lattice_points -for edge in lattice.edges: + for edge in lattice.edges: + start_index, end_index, color = edge + start_pos, end_pos = nodes[start_index], nodes[end_index] - start_index, end_index, color = edge - start_pos, end_pos = nodes[start_index], nodes[end_index] + x_axis = [start_pos[0], end_pos[0]] + y_axis = [start_pos[1], end_pos[1]] + plt.plot(x_axis, y_axis, color='gold') - x_axis = [start_pos[0], end_pos[0]] - y_axis = [start_pos[1], end_pos[1]] - plt.plot(x_axis, y_axis, color='gold') + plt.scatter(nodes[:,0], nodes[:,1], color='dodgerblue', s=100) + for index, pos in enumerate(nodes): + plt.text(pos[0]-0.2, pos[1]+0.1, str(index), color='gray') - plt.scatter(nodes[:,0], nodes[:,1], color='dodgerblue', s=100) - for index, pos in enumerate(nodes): - plt.text(pos[0]-0.2, pos[1]+0.1, str(index), color='gray') + plt.axis("off") + plt.show() -plt.axis("off") -plt.show() +plot(lattice) ###################################################################### # Now, we construct the same lattice manually by explicitly defining the positions of the sites in a @@ -156,6 +159,11 @@ vectors = [[1, 0], [0.5, np.sqrt(3) / 2]] n_cells = [3, 3] +###################################################################### +# Let's plot the lattice to see how it looks like. + +plot(Lattice(n_cells, vectors, nodes)) + ###################################################################### # Now we add custom edges to the lattice. We have three different edge orientations that we define # as @@ -164,11 +172,12 @@ [(1, 2), ('YY', 0.6)], [(1, 6), ('ZZ', 0.7)]] +lattice = Lattice(n_cells, vectors, nodes, custom_edges=custom_edges) + ###################################################################### -# Then we construct the lattice and pass it to the spin_hamiltonian function, which is a helper -# function that constructs a Hamiltonian from a lattice. and the Hamiltonian similar to other examples. +# Then we pass the lattice to the spin_hamiltonian function, which is a helper +# function that constructs a Hamiltonian from a lattice. -lattice = Lattice(n_cells, vectors, nodes, custom_edges=custom_edges) hamiltonian = qml.spin.spin_hamiltonian(lattice=lattice) ###################################################################### @@ -187,8 +196,9 @@ hamiltonian ###################################################################### -# We compare the constructed Hamiltonian with the template we already have for the Kitaev -# model. Can you compare them and verify that the Hamiltonians are the same? +# See how we can easily and intuitively construct the Kitaev model Hamiltonian with the tools in +# these tools. You can compare the constructed Hamiltonian with the template we already have for +# the Kitaev model and verify that the Hamiltonians are the same. # # Conclusion # ---------- From 03dc5591ccfe2ae7d34f3359a69bbad692b722e4 Mon Sep 17 00:00:00 2001 From: Mikhail Andrenkov Date: Wed, 9 Oct 2024 13:40:52 -0400 Subject: [PATCH 05/40] Merge `master` into `dev` (#1236) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit **Summary:** Fixes a merge conflict between `master` and `dev` introduced by #1232 (due to divergent `dateOfLastModification`). To reproduce this PR: 1. Run `git checkout dev`. 2. Run `git checkout -b merge-master-into-dev`. 3. Run `git merge master`. 4. Accept all incoming changes for merge conflicts on `dateOfLastModification`. **Relevant GHA Workflow Runs:** * https://github.com/PennyLaneAI/qml/actions/runs/11241666164 * https://github.com/PennyLaneAI/qml/actions/runs/11253088983 --------- Co-authored-by: David Wierichs Co-authored-by: Korbinian Kottmann <43949391+Qottmann@users.noreply.github.com> Co-authored-by: Ivana Kurečić --- .github/workflows/validate-demo-metadata.yml | 2 +- ...humbnail_large_constant_depth_mps_prep.png | Bin 0 -> 158792 bytes .../OGthumbnail_constant_depth_mps_prep.png | Bin 0 -> 153556 bytes .../thumbnail_constant_depth_mps_prep.png | Bin 0 -> 34854 bytes .../constant_depth_mps_prep/algorithm.png | Bin 0 -> 284568 bytes .../algorithm_preview.png | Bin 0 -> 149900 bytes .../constant_depth_mps_prep/fusion.png | Bin 0 -> 48908 bytes .../operator_pushing.png | Bin 0 -> 34657 bytes .../operator_pushing_example.png | Bin 0 -> 95604 bytes .../constant_depth_mps_prep/sequential.png | Bin 0 -> 106521 bytes .../adjoint_diff_benchmarking.metadata.json | 3 +- demonstrations/ahs_aquila.metadata.json | 5 +- .../braket-parallel-gradients.metadata.json | 5 +- .../circuits_as_fourier_series.metadata.json | 3 +- .../covalent_cloud_gpu.metadata.json | 3 +- .../ensemble_multi_qpu.metadata.json | 3 +- .../function_fitting_qsp.metadata.json | 3 +- demonstrations/gbs.metadata.json | 5 +- ...ing_started_with_hybrid_jobs.metadata.json | 5 +- demonstrations/gqe_training.metadata.json | 7 +- ...o_use_qiskit1_with_pennylane.metadata.json | 3 +- demonstrations/ibm_pennylane.metadata.json | 3 +- demonstrations/learning2learn.metadata.json | 5 +- .../ml_classical_shadows.metadata.json | 3 +- demonstrations/oqc_pulse.metadata.json | 5 +- demonstrations/plugins_hybrid.metadata.json | 5 +- demonstrations/pytorch_noise.metadata.json | 3 +- demonstrations/qnspsa.metadata.json | 5 +- demonstrations/qonn.metadata.json | 3 +- demonstrations/qrack.metadata.json | 3 +- .../qsim_beyond_classical.metadata.json | 3 +- .../quantum_neural_net.metadata.json | 3 +- demonstrations/quantum_volume.metadata.json | 5 +- ...L_model_using_JAX_and_JAXopt.metadata.json | 5 +- ...ML_model_using_JAX_and_Optax.metadata.json | 5 +- ...using_JAX_catalyst_and_Optax.metadata.json | 3 +- ...ircuits_with_tensor_networks.metadata.json | 3 +- demonstrations/tutorial_QGAN.metadata.json | 3 +- demonstrations/tutorial_QUBO.metadata.json | 5 +- .../tutorial_adaptive_circuits.metadata.json | 3 +- .../tutorial_adjoint_diff.metadata.json | 3 +- ...rial_adversarial_attacks_QML.metadata.json | 5 +- .../tutorial_apply_qsvt.metadata.json | 3 +- .../tutorial_backprop.metadata.json | 3 +- .../tutorial_barren_gadgets.metadata.json | 3 +- .../tutorial_barren_plateaus.metadata.json | 3 +- .../tutorial_block_encoding.metadata.json | 3 +- .../tutorial_bluequbit.metadata.json | 5 +- .../tutorial_chemical_reactions.metadata.json | 3 +- ...tutorial_circuit_compilation.metadata.json | 5 +- ..._classical_expval_estimation.metadata.json | 5 +- .../tutorial_classical_kernels.metadata.json | 3 +- .../tutorial_classical_shadows.metadata.json | 3 +- ...rial_classically_boosted_vqe.metadata.json | 3 +- ...clifford_circuit_simulations.metadata.json | 7 +- .../tutorial_coherent_vqls.metadata.json | 3 +- ...rial_constant_depth_mps_prep.metadata.json | 47 + .../tutorial_constant_depth_mps_prep.py | 851 ++++++++++++++++++ .../tutorial_contextuality.metadata.json | 5 +- ..._data_reuploading_classifier.metadata.json | 5 +- ...tutorial_diffable-mitigation.metadata.json | 3 +- .../tutorial_diffable_shadows.metadata.json | 3 +- .../tutorial_differentiable_HF.metadata.json | 3 +- .../tutorial_doubly_stochastic.metadata.json | 3 +- .../tutorial_eqnn_force_field.metadata.json | 5 +- ..._equivariant_graph_embedding.metadata.json | 5 +- .../tutorial_error_mitigation.metadata.json | 5 +- .../tutorial_error_prop.metadata.json | 3 +- ..._expressivity_fourier_series.metadata.json | 5 +- demonstrations/tutorial_falqon.metadata.json | 3 +- ...tutorial_fermionic_operators.metadata.json | 5 +- ...rial_gaussian_transformation.metadata.json | 3 +- .../tutorial_general_parshift.metadata.json | 3 +- .../tutorial_geometric_qml.metadata.json | 5 +- .../tutorial_givens_rotations.metadata.json | 3 +- .../tutorial_grovers_algorithm.metadata.json | 9 +- ..._to_pennylane_knowing_qiskit.metadata.json | 3 +- .../tutorial_haar_measure.metadata.json | 3 +- .../tutorial_here_comes_the_sun.metadata.json | 5 +- ...ial_how_to_collect_mcm_stats.metadata.json | 3 +- ..._create_dynamic_mcm_circuits.metadata.json | 3 +- ...in_time_compile_vqe_catalyst.metadata.json | 3 +- ...rial_how_to_use_noise_models.metadata.json | 3 +- ...utorial_how_to_use_registers.metadata.json | 3 +- ...implicit_diff_susceptibility.metadata.json | 5 +- ...al_initial_state_preparation.metadata.json | 5 +- ...ntro_amplitude_amplification.metadata.json | 5 +- .../tutorial_intro_qrom.metadata.json | 5 +- .../tutorial_intro_qsvt.metadata.json | 5 +- .../tutorial_isingmodel_PyTorch.metadata.json | 3 +- ...tutorial_jax_transformations.metadata.json | 5 +- ...torial_kernel_based_training.metadata.json | 3 +- .../tutorial_kernels_module.metadata.json | 3 +- .../tutorial_lcu_blockencoding.metadata.json | 3 +- ...arning_dynamics_incoherently.metadata.json | 7 +- .../tutorial_learning_few_data.metadata.json | 3 +- ...al_learning_from_experiments.metadata.json | 3 +- .../tutorial_learningshallow.metadata.json | 4 +- .../tutorial_liealgebra.metadata.json | 4 +- demonstrations/tutorial_liesim.metadata.json | 3 +- .../tutorial_liesim_extension.metadata.json | 3 +- ...utorial_local_cost_functions.metadata.json | 3 +- ...ial_magic_state_distillation.metadata.json | 3 +- demonstrations/tutorial_mapping.metadata.json | 3 +- demonstrations/tutorial_mbqc.metadata.json | 3 +- .../tutorial_mcm_introduction.metadata.json | 3 +- ...utorial_measurement_optimize.metadata.json | 3 +- ...utorial_mitigation_advantage.metadata.json | 5 +- .../tutorial_mol_geo_opt.metadata.json | 3 +- demonstrations/tutorial_mps.metadata.json | 3 +- ...al_multiclass_classification.metadata.json | 3 +- .../tutorial_neutral_atoms.metadata.json | 5 +- ...l_noisy_circuit_optimization.metadata.json | 3 +- .../tutorial_noisy_circuits.metadata.json | 3 +- demonstrations/tutorial_odegen.metadata.json | 4 +- .../tutorial_optimal_control.metadata.json | 5 +- demonstrations/tutorial_pasqal.metadata.json | 3 +- .../tutorial_phase_kickback.metadata.json | 5 +- .../tutorial_photonics.metadata.json | 3 +- ...onal_quantum_neural_networks.metadata.json | 3 +- ...utorial_pulse_programming101.metadata.json | 5 +- .../tutorial_qaoa_intro.metadata.json | 3 +- .../tutorial_qaoa_maxcut.metadata.json | 3 +- demonstrations/tutorial_qcbm.metadata.json | 5 +- .../tutorial_qchem_external.metadata.json | 3 +- demonstrations/tutorial_qft.metadata.json | 5 +- .../tutorial_qft_arithmetics.metadata.json | 3 +- demonstrations/tutorial_qgrnn.metadata.json | 3 +- .../tutorial_qnn_module_tf.metadata.json | 3 +- .../tutorial_qnn_module_torch.metadata.json | 5 +- ..._qnn_multivariate_regression.metadata.json | 3 +- demonstrations/tutorial_qpe.metadata.json | 5 +- .../tutorial_qsvt_hardware.metadata.json | 5 +- ...ial_quantum_analytic_descent.metadata.json | 3 +- .../tutorial_quantum_chemistry.metadata.json | 3 +- ...rial_quantum_circuit_cutting.metadata.json | 3 +- .../tutorial_quantum_dropout.metadata.json | 5 +- .../tutorial_quantum_gans.metadata.json | 3 +- .../tutorial_quantum_metrology.metadata.json | 3 +- ...ial_quantum_natural_gradient.metadata.json | 3 +- ...al_quantum_transfer_learning.metadata.json | 3 +- .../tutorial_quanvolution.metadata.json | 3 +- .../tutorial_qubit_rotation.metadata.json | 3 +- .../tutorial_qubit_tapering.metadata.json | 3 +- .../tutorial_qubitization.metadata.json | 5 +- ...l_qutrits_bernstein_vazirani.metadata.json | 5 +- ...tutorial_resource_estimation.metadata.json | 3 +- .../tutorial_rl_pulse.metadata.json | 5 +- demonstrations/tutorial_rosalin.metadata.json | 3 +- .../tutorial_rotoselect.metadata.json | 3 +- .../tutorial_sc_qubits.metadata.json | 3 +- ...hadow_hamiltonian_simulation.metadata.json | 3 +- demonstrations/tutorial_spsa.metadata.json | 5 +- .../tutorial_state_preparation.metadata.json | 3 +- ...l_stochastic_parameter_shift.metadata.json | 3 +- .../tutorial_teleportation.metadata.json | 3 +- .../tutorial_testing_symmetry.metadata.json | 3 +- .../tutorial_tn_circuits.metadata.json | 3 +- .../tutorial_toric_code.metadata.json | 3 +- .../tutorial_trapped_ions.metadata.json | 3 +- .../tutorial_unitary_designs.metadata.json | 3 +- .../tutorial_univariate_qvr.metadata.json | 3 +- ...orial_variational_classifier.metadata.json | 3 +- demonstrations/tutorial_vqe.metadata.json | 3 +- demonstrations/tutorial_vqe_qng.metadata.json | 3 +- .../tutorial_vqe_spin_sectors.metadata.json | 3 +- demonstrations/tutorial_vqe_vqd.metadata.json | 3 +- demonstrations/tutorial_vqls.metadata.json | 3 +- demonstrations/tutorial_vqt.metadata.json | 3 +- .../tutorial_zx_calculus.metadata.json | 5 +- demonstrations/vqe_parallel.metadata.json | 3 +- demonstrations_metadata.md | 21 +- .../demo.metadata.schema.0.1.3.json | 2 +- .../demo.metadata.schema.0.1.4.json | 135 +++ update_metadata.py | 133 --- 175 files changed, 1258 insertions(+), 525 deletions(-) create mode 100644 _static/demo_thumbnails/large_demo_thumbnails/thumbnail_large_constant_depth_mps_prep.png create mode 100644 _static/demo_thumbnails/opengraph_demo_thumbnails/OGthumbnail_constant_depth_mps_prep.png create mode 100644 _static/demo_thumbnails/regular_demo_thumbnails/thumbnail_constant_depth_mps_prep.png create mode 100644 _static/demonstration_assets/constant_depth_mps_prep/algorithm.png create mode 100644 _static/demonstration_assets/constant_depth_mps_prep/algorithm_preview.png create mode 100644 _static/demonstration_assets/constant_depth_mps_prep/fusion.png create mode 100644 _static/demonstration_assets/constant_depth_mps_prep/operator_pushing.png create mode 100644 _static/demonstration_assets/constant_depth_mps_prep/operator_pushing_example.png create mode 100644 _static/demonstration_assets/constant_depth_mps_prep/sequential.png create mode 100644 demonstrations/tutorial_constant_depth_mps_prep.metadata.json create mode 100644 demonstrations/tutorial_constant_depth_mps_prep.py create mode 100644 metadata_schemas/demo.metadata.schema.0.1.4.json delete mode 100644 update_metadata.py diff --git a/.github/workflows/validate-demo-metadata.yml b/.github/workflows/validate-demo-metadata.yml index 5e4540bdb7..ed5f617b76 100644 --- a/.github/workflows/validate-demo-metadata.yml +++ b/.github/workflows/validate-demo-metadata.yml @@ -101,7 +101,7 @@ jobs: METADATA_FILE_LIST: ${{ needs.generate-metadata-file-list.outputs.metadata_files }} run: | cd metadata_schemas - ${{ steps.poetry.outputs.bin }} run check-jsonschema -v --traceback-mode full --schemafile 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