diff --git a/demonstrations/ahs_aquila.py b/demonstrations/ahs_aquila.py index 8b16b3f9ca..86516ef183 100644 --- a/demonstrations/ahs_aquila.py +++ b/demonstrations/ahs_aquila.py @@ -254,7 +254,7 @@ # {'area': {'width': Decimal('0.000075'), 'height': Decimal('0.000076')}, # 'geometry': {'spacingRadialMin': Decimal('0.000004'), # 'spacingVerticalMin': Decimal('0.000004'), -# 'positionResolution': Decimal('1E-7'), +# 'positionResolution': Decimal('1E-8'), # 'numberSitesMax': 256}} # # We can see that the atom field has a width of :math:`75 \, \mu m` and a height of :math:`76 \, \mu m`. @@ -289,6 +289,7 @@ plt.scatter([x for x, y in coordinates], [y for x, y in coordinates]) plt.xlabel("μm") plt.ylabel("μm") +plt.show() ############################################################################## # .. figure:: ../_static/demonstration_assets/ahs_aquila/rydberg_blockade_coordinates.png @@ -347,16 +348,17 @@ # 'rydbergGlobal': {'rabiFrequencyRange': (Decimal('0.0'), # Decimal('15800000.0')), # 'rabiFrequencyResolution': Decimal('400.0'), -# 'rabiFrequencySlewRateMax': Decimal('250000000000000.0'), +# 'rabiFrequencySlewRateMax': Decimal('400000000000000.0'), # 'detuningRange': (Decimal('-125000000.0'), Decimal('125000000.0')), # 'detuningResolution': Decimal('0.2'), -# 'detuningSlewRateMax': Decimal('2500000000000000.0'), +# 'detuningSlewRateMax': Decimal('6000000000000000.0'), # 'phaseRange': (Decimal('-99.0'), Decimal('99.0')), # 'phaseResolution': Decimal('5E-7'), # 'timeResolution': Decimal('1E-9'), # 'timeDeltaMin': Decimal('5E-8'), # 'timeMin': Decimal('0.0'), -# 'timeMax': Decimal('0.000004')}} +# 'timeMax': Decimal('0.000004')}, +# 'rydbergLocal': None} # # It is important to note that these quantities are in radians per second rather than Hz where relevant, and # are all in SI units. This means that for amplitude and detuning, we will need to convert from angular @@ -436,7 +438,7 @@ def angular_SI_to_MHz(angular_SI): def gaussian_fn(p, t): - return p[0] * jnp.exp(-((t-p[1])**2) / (2*p[2]**2)) + return p[0] * jnp.exp(-((t - p[1]) ** 2) / (2 * p[2] ** 2)) # Visualize pulse, time in μs @@ -453,6 +455,7 @@ def gaussian_fn(p, t): plt.ylabel("Amplitude [MHz]") plt.plot(time, y) +plt.show() ############################################################################## # @@ -467,10 +470,7 @@ def gaussian_fn(p, t): # We can then define our drive using via :func:`~pennylane.pulse.rydberg_drive`: # -global_drive = qml.pulse.rydberg_drive(amplitude=gaussian_fn, - phase=0, - detuning=0, - wires=[0, 1, 2]) +global_drive = qml.pulse.rydberg_drive(amplitude=gaussian_fn, phase=0, detuning=0, wires=[0, 1, 2]) ###################################################################### # With only amplitude as non-zero, the overall driven Hamiltonian in this case simplifies to: @@ -606,7 +606,7 @@ def circuit(params): start_val = amplitude[0] stop_val = amplitude[-1] max_val = np.max(amplitude) -max_rate = np.max([(amplitude[i + 1] - amplitude[i]) / timestep for i in range(len(times)-1)]) +max_rate = np.max([(amplitude[i + 1] - amplitude[i]) / timestep for i in range(len(times) - 1)]) print(f"start value: {start_val:.3} MHz") print(f"stop value: {stop_val:.3} MHz") @@ -636,10 +636,7 @@ def circuit(params): # amp_fn = qml.pulse.rect(gaussian_fn, windows=[0.01, 1.749]) -global_drive = qml.pulse.rydberg_drive(amplitude=amp_fn, - phase=0, - detuning=0, - wires=[0, 1, 2]) +global_drive = qml.pulse.rydberg_drive(amplitude=amp_fn, phase=0, detuning=0, wires=[0, 1, 2]) ###################################################################### # At this point we could skip directly to defining a ``qnode`` using the ``aquila`` device and running our @@ -679,6 +676,7 @@ def circuit(params): plt.xlabel("μm") plt.ylabel("μm") plt.legend() +plt.show() ############################################################################## # @@ -705,8 +703,7 @@ def circuit(params): # values for plotting the function defined in PennyLane for amplitude input_times = np.linspace(*ts, 1000) input_amplitudes = [ - qml.pulse.rect(gaussian_fn, windows=[0.01, 1.749])(amplitude_params, _t) - for _t in input_times + qml.pulse.rect(gaussian_fn, windows=[0.01, 1.749])(amplitude_params, _t) for _t in input_times ] # plot PL input and hardware setpoints for comparison diff --git a/demonstrations/braket-parallel-gradients.py b/demonstrations/braket-parallel-gradients.py index b5801ac29b..a86bb56a28 100644 --- a/demonstrations/braket-parallel-gradients.py +++ b/demonstrations/braket-parallel-gradients.py @@ -95,7 +95,6 @@ Let's load the SV1 simulator in PennyLane with 25 qubits by specifying the device ARN. """ - device_arn = "arn:aws:braket:::device/quantum-simulator/amazon/sv1" ############################################################################## @@ -193,8 +192,8 @@ def circuit(params): # # .. code-block:: none # -# Execution time on remote device (seconds): 3.5898206680030853 -# Execution time on local device (seconds): 23.50668462700196 +# Execution time on remote device (seconds): 7.571744918823242 +# Execution time on local device (seconds): 27.32159185409546 # # Nice! These timings highlight the advantage of using the Amazon Braket SV1 device for simulations # with large qubit numbers. In general, simulation times scale exponentially with the number of @@ -235,7 +234,7 @@ def circuit(params): # # .. code-block:: none # -# Gradient calculation time on remote device (seconds): 20.92005863400118 +# Gradient calculation time on remote device (seconds): 6.4775872230529785 # # Now, the local device: # @@ -260,9 +259,9 @@ def circuit(params): # # .. code-block:: none # -# Gradient calculation time on local device (seconds): 941.8518133479993 +# Gradient calculation time on local device (seconds): 181.5902488231659 # -# Wow, the local device needs around 15 minutes or more! Compare this to less than a minute spent +# Wow, the local device needs around 3 minutes! Compare this to less around 6 seconds spent # calculating the gradient on SV1. This provides a powerful lesson in parallelization. # # What if we had run on SV1 with ``parallel=False``? It would have taken around 3 minutes—still @@ -394,37 +393,37 @@ def circuit(params, **kwargs): # # .. code-block:: none # -# Initial cost: -29.98570234095951 +# Initial cost: -29.98570234095953 # Completed iteration 1 -# Time to complete iteration: 93.96246099472046 seconds -# Cost at step 1: -27.154071768632154 +# Time to complete iteration: 11.028863906860352 seconds +# Cost at step 1: -29.995107300982195 # Completed iteration 2 -# Time to complete iteration: 84.80994844436646 seconds -# Cost at step 2: -29.98726230006233 +# Time to complete iteration: 10.132628917694092 seconds +# Cost at step 2: -29.99981056829394 # Completed iteration 3 -# Time to complete iteration: 83.13504934310913 seconds -# Cost at step 3: -29.999163153600062 +# Time to complete iteration: 9.985284090042114 seconds +# Cost at step 3: -30.00233965117029 # Completed iteration 4 -# Time to complete iteration: 85.61391234397888 seconds -# Cost at step 4: -30.002158646044307 +# Time to complete iteration: 10.059210062026978 seconds +# Cost at step 4: -30.01206799710033 # Completed iteration 5 -# Time to complete iteration: 86.70688223838806 seconds -# Cost at step 5: -30.012058444011906 +# Time to complete iteration: 9.966963052749634 seconds +# Cost at step 5: -30.048670540725286 # Completed iteration 6 -# Time to complete iteration: 83.26341080665588 seconds -# Cost at step 6: -30.063709712612443 +# Time to complete iteration: 11.216133832931519 seconds +# Cost at step 6: -30.13240170461342 # Completed iteration 7 -# Time to complete iteration: 85.25566911697388 seconds -# Cost at step 7: -30.32522304705352 +# Time to complete iteration: 10.09446096420288 seconds +# Cost at step 7: -30.25935979157505 # Completed iteration 8 -# Time to complete iteration: 83.55433392524719 seconds -# Cost at step 8: -31.411030331978186 +# Time to complete iteration: 10.020287990570068 seconds +# Cost at step 8: -30.41499293575487 # Completed iteration 9 -# Time to complete iteration: 84.08745908737183 seconds -# Cost at step 9: -33.87153965616938 +# Time to complete iteration: 10.345153093338013 seconds +# Cost at step 9: -30.587353834845057 # Completed iteration 10 -# Time to complete iteration: 87.4032838344574 seconds -# Cost at step 10: -36.05424874438809 +# Time to complete iteration: 10.07306981086731 seconds +# Cost at step 10: -30.76855713404487 # Parameters saved to params.npy # # This example shows us that a 20-qubit QAOA problem can be trained within around 1-2 minutes per @@ -592,7 +591,7 @@ def cost_function(params, **kwargs): # :target: javascript:void(0); ############################################################################## -# Great! 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