diff --git a/.github/workflows/python-test.yml b/.github/workflows/python-test.yml index f4f39f3..60f79b2 100644 --- a/.github/workflows/python-test.yml +++ b/.github/workflows/python-test.yml @@ -35,11 +35,3 @@ jobs: run: | python -m pytest qopt_tests --cov=qopt --cov-report=xml # it is important to use python -m because it adds the current directory to the path! - - - name: Upload code coverage - uses: codecov/codecov-action@v2 - with: - file: ./coverage.xml - name: ${{ matrix.python-version }} - env_vars: OS,PYTHON - fail_ci_if_error: true diff --git a/.gitignore b/.gitignore index e99e0e7..f07f667 100644 --- a/.gitignore +++ b/.gitignore @@ -13,3 +13,8 @@ qopt/examples/rabi_driving/__pycache__/lab_frame_setup.cpython-37.pyc .coverage coverage.xml temp/File Name +temp/tensorflow/ +doc/latexbuild/ +~/tensorflow_datasets/ +logs/20220823-190009/train/events.out.tfevents.1661274009.DESKTOP-29V7IHI.12928.0.v2 +doc/short_latex_build/ diff --git a/README.md b/README.md index d2c4f21..d82ccd0 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,6 @@ [![Documentation Status](https://img.shields.io/readthedocs/qopt)](https://qopt.readthedocs.io/en/latest/) [![PyPI version](https://img.shields.io/pypi/v/qopt)](https://pypi.org/project/qopt/) [![License](https://img.shields.io/github/license/qutech/qopt)](https://github.com/qutech/qopt/blob/master/LICENSE) -[![codecov](https://codecov.io/gh/qutech/qopt/branch/master/graph/badge.svg)](https://app.codecov.io/gh/qutech/qopt) ## Documentation The documentation can be found on diff --git a/doc/source/index.rst b/doc/source/index.rst index 4adca2f..879634c 100644 --- a/doc/source/index.rst +++ b/doc/source/index.rst @@ -48,15 +48,17 @@ Summary ======= This python package is designed to facilitate the simulation of -state-of-the-art quantum bits (qubits) including operation limitations, where -an emphasis is put on the description of realistic experimental setups. +state-of-the-art quantum bits (qubits) including realistic experimental +operation conditions, and for the optimization of noise-robust control pulses. For this purpose, an extensive set of noise simulation tools is included and complemented by methods to describe the limitations posed by the -electronics steering the quantum operations. - -The simulation interfaces to optimization algorithms to be used in -optimal quantum control, the field of study which optimizes the accuracy of -quantum operations by intelligent steering methods. +electronics steering the quantum operations. Compared to other available +simulation packages qopt stands out by the ability to efficiently simulate the +effects of fast Non-Markovian noise, while providing a general interface to +define control pulses for any qubit type making qopt platform-independent. +The simulation interfaces to optimization algorithms to apply +optimal quantum control theory, the field of study which optimizes the +accuracy of quantum operations by intelligent steering methods. The functionalities can be coarsely divided into simulation and optimization of quantum operations. Various cost functions can be evaluated on the simulated @@ -68,14 +70,12 @@ cost functions by the optimization parameters based on analytical calculations. Simulation ---------- -The evolution of a closed quantum system is described by Schroedinger's -equation, such that the dynamics are determined by the Hamiltonian of the -system. Solving Schroedinger's equation yields a description of the temporal -evolution of the quantum system. - -The Hamiltonian is the sum of effects which can be controlled, those who can -not be controlled (the drift) and effects which cannot be even predicted -(the noise.) +The qopt package simulates closed or open quantum systems on a pulse level by +solving the corresponding partial differential equations being the Schroedinger +equation or a Lindblad master equation. +Thereby, pulses are discretized in time and the differential equations are +solved using matrix exponentials. The total propagator is then available for +every time step to analyse the dynamics of the qubit system. Noise @@ -90,20 +90,20 @@ given stating the advantages and requirements of each method. The most forward way to simulate noise is to draw samples from the noise distribution and repeat the simulation for each of those noise realizations. Any cost function is then averaged over the repetitions. -The sampling is based on pseudo random number generators. +The sampling can be based on pseudo random number generators. Monte Carlo simulations are universally applicable but computationally -expensive for high frequency noise. +expensive for high-frequency noise. **Lindblad Master Equation** In order to include dissipation effects in the simulation, the qubit and its -environment must be described as open quantum system, described by a master +environment can be modeled as open quantum system, described by a master equation in Lindblad form. The solution of the master equation is in the general case not unitary unlike the propagators calculated from Schroedinger's equation, such that it can also describe the loss of energy or information into the environment. This approach is numerically efficient but -only applicable to systems subjected to markovian noise. +only applicable to systems subjected to Markovian noise. **Filter Functions** @@ -127,10 +127,25 @@ error rate caused to leakage. Pulse Parametrization --------------------- -In many practical applications the optimization parameters do not appear -directly as factors in the Hamiltonian. The control fields are modified by -taking limitations on the control electronics and the physical qubit model into -account. +The pulse parameterization translates a mathematically described pulse function +into discrete-time control amplitudes that appear in the Hamiltonian describing +the qubit model. This comprises sampling a potentially continuously defined +control pulse, evaluating the physical function that describes the relation +between pulse values and the control amplitudes, and including the hardware +limitations of the control electronics that generate the control pulse. + + +**Amplitude Functions** + +The amplitude functions encode a differential relationship between the +optimization parameters, which describe the pulse, and the control amplitudes, +which appear in the Hamiltonian and describe the dynamics of the quantum +system. An example would be a sinusoidal pulse that drives resonant excitations +of a qubit. The optimization parameters would be the pulse length, the pulse +frequency and the amplitude. The amplitude function samples the continuous +pulse and maps the voltage values to energy values from the Hamiltonian, +which are the control amplitudes in this example. + **Transfer Functions** @@ -140,15 +155,8 @@ for example exponential saturation to consider finite voltage rise times in pulse generators, Gaussian smoothing of pulses to mimic bandwidth limitations on arbitrary waveform generators, linear transformations or even the measured response of an arbitrary waveform generator to a set of input -voltages. - -**Amplitude Functions** - -A differentiable functional relation between the optimization parameters and -the control amplitudes can be expressed in the amplitude functions. This can -for example be the exchange energy -as function of the voltage detuning in a double quantum dot -implemented in semiconductor spin qubits. +voltages. The transfer functions then map the ideal pulse to the actually +generated pulse. Optimization ------------ @@ -173,7 +181,7 @@ Documentation The documentation is structured in the three parts 'Features', 'Example Applications' and the 'API Documentation'. -**Features** +**qopt Features** The first part introduces the qopt functionalities step by step. Refer to this chapter for an introduction to the simulation package. @@ -197,13 +205,6 @@ descriptions. During the implementation of a simulation using qopt, you can frequently jump to the classes and functions your are using to look up the signatures. -Citing -====== - -If you are using qopt for your work then please cite the -[qopt paper](https://doi.org/10.1103/PhysRevApplied.17.034036), as the funding -of the development depends on the public impact. - .. toctree:: :maxdepth: 2 @@ -213,6 +214,13 @@ of the development depends on the public impact. examples/examples qopt API Documentation +Citing +====== + +If you are using qopt for your work then please cite the +[qopt paper](https://doi.org/10.1103/PhysRevApplied.17.034036), as the funding +of the development depends on the public impact. + Indices and tables diff --git a/doc/source/qopt_features/entanglement_fidelity.ipynb b/doc/source/qopt_features/entanglement_fidelity.ipynb index a32d5f1..b79e180 100644 --- a/doc/source/qopt_features/entanglement_fidelity.ipynb +++ b/doc/source/qopt_features/entanglement_fidelity.ipynb @@ -110,7 +110,7 @@ "cell_type": "markdown", "source": [ "More information contains the gate fidelity, which describes the entire quantum gate and not only the action on a single initial state. We can set up the gate fidelity of this solver with the\n", - "$X_\\pi$-Rotation as target. The gate fidelity (equivalent to the entanglement fidelity) $I_e$ between a quantum\n", + "$X_\\pi$-Rotation as target. The entanglement fidelity (equivalent to the process fidelity) $I_e$ between a quantum\n", "channel described by a unitary evolution $U$ and a target evolution $V$ can be\n", "calculated as Hilbert-Schmidt vector product:\n", "\\begin{equation}\n", @@ -353,12 +353,12 @@ { "cell_type": "markdown", "source": [ - "qopt supports the paradigm of the separation of error contributions. In this case, this means that qopt allows to separate leakage errors from coherent errors. Here we define a cost function for the coherent costs, which corrects leakage errors, by mapping the truncated unitary propagator to the closest unitary matrix before calculating the gate infidelity. The 'pure' leakage can be calculated as the distance from unitarity of the truncated propagator:\n", + "qopt supports the paradigm of the separation of error contributions. In this case, this means that qopt allows to separate leakage errors from coherent errors. Next, we define a cost function for the coherent costs, which corrects leakage errors, by mapping the truncated unitary propagator to the closest unitary matrix before calculating the gate infidelity.\n", "\n", + "The 'pure' leakage can be calculated as the distance from unitarity of the truncated propagator:\n", "\\begin{equation}\n", "L = 1 - \\frac{1}{d} \\text{tr}\\left( U_{\\text{trunc}}^\\dagger U_{\\text{trunc}} \\right).\n", "\\end{equation}\n", - "\n", "Please note that $d$ is still the dimension of the computational space." ], "metadata": { diff --git a/doc/source/qopt_features/filter_functions_basic.ipynb b/doc/source/qopt_features/filter_functions_basic.ipynb index ad90dd1..f8e0d73 100644 --- a/doc/source/qopt_features/filter_functions_basic.ipynb +++ b/doc/source/qopt_features/filter_functions_basic.ipynb @@ -98,11 +98,11 @@ "source": [ "## Calculate Filter Functions\n", "\n", - "We will now discuss how to calculate filter function with the qopt interface.\n", + "We will now discuss how to calculate filter functions with the qopt interface.\n", "\n", "Any noise on the electrical control $\\epsilon(t)\n", "\\rightarrow \\epsilon(t) + \\delta \\epsilon(t)$ couples to the qubit via the\n", - "noise hamiltonian\n", + "noise Hamiltonian\n", "\n", "\\begin{equation}\n", "H_n= \\delta \\epsilon(t) \\frac{\\partial J(\\epsilon(t))}{\\partial \\epsilon}\\sigma_x.\n", diff --git a/doc/source/qopt_features/monte_carlo_experiments.ipynb b/doc/source/qopt_features/monte_carlo_experiments.ipynb index 2099380..acfdef9 100644 --- a/doc/source/qopt_features/monte_carlo_experiments.ipynb +++ b/doc/source/qopt_features/monte_carlo_experiments.ipynb @@ -44,7 +44,7 @@ "## Quasi Static Noise\n", "\n", "The noise realizations required for the Monte Carlo simulations are created\n", - "in the `NoiseTraceGenerator` classes. Let us consider the case of quasi static noise\n", + "in the `NoiseTraceGenerator` classes. Let us consider the case of quasi-static noise\n", "first:\n", "\n" ] @@ -88,9 +88,9 @@ "\n", "The other implemented sampling mode is monte_carlo, where the noise traces are\n", "created with help of random number generators and all random variables are\n", - "sampled at once.\n", + "sampled simultaneously.\n", "\n", - "Next, we optimize the pulse subjected to quasi static noise. We begin with the\n", + "Next, we optimize the pulse subjected to quasi-static noise. We begin with the\n", "same setup as in the noiseless case:" ], "metadata": { @@ -129,7 +129,7 @@ { "cell_type": "markdown", "source": [ - "But we use a different solver." + "But we use a Monte Carlo solver." ], "metadata": { "collapsed": false, @@ -165,18 +165,19 @@ "For the cost function, we have multiple options. We can use a cost function\n", "which averages the entanglement infidelity\n", "$F_e$ over the noise realizations to calculate the infidelity caused by noise\n", - "$_{n}F_e$:\n", + "$\\bar{F}_e$:\n", "\n", "\\begin{equation}\n", - "_nF_e = \\frac{1}{n_\\text{traces}}\\sum_{\\delta_\\omega} F\n", + "\\bar{F}_e = \\frac{1}{n_\\text{traces}}\\sum_{\\delta_\\omega} F_e(\\delta_\\omega),\n", "\\end{equation}\n", + "where $F_e(\\delta_\\omega)$ is the infidelity evaluated for the evolution with the noise value $\\delta_\\omega$.\n", "\n", "This includes the stochastic (caused by noise) and the systematic (also\n", "appearing in the absence of noise) deviations.\n", "However, there are\n", "certain advantages, if the stochastic and the systematic deviations are split.\n", "It gives us a feeling for the cause of infidelities in our system, and the\n", - "optimization algorithms aquires the information, that it optimizes two cost\n", + "optimization algorithms acquires the information, that it optimizes two cost\n", "functions at once, if a vector valued optimization algorithm is chosen." ], "metadata": { diff --git a/doc/source/qopt_features/pulse_parameterization.ipynb b/doc/source/qopt_features/pulse_parameterization.ipynb index c7c5511..6cdae05 100644 --- a/doc/source/qopt_features/pulse_parameterization.ipynb +++ b/doc/source/qopt_features/pulse_parameterization.ipynb @@ -30,7 +30,7 @@ "\n", "where $\\omega_0$ is the resonance frequency, $A(t) = v_1(t)$ the driving amplitude and\n", "$\\delta=v_2(t)$ the phase shift of the driving signal.\n", - "We transform the Hamiltonian into the rotating frame to yield:\n", + "We transform the Hamiltonian into the rotating frame and apply the Rotating wave approximation to yield:\n", "\n", "\\begin{equation}\n", "H = \\frac{ A}{2} \\text{cos} (\\delta ) \\sigma_x\n", @@ -38,7 +38,7 @@ "\\end{equation}\n", "\n", "Now we identify the control amplitudes $u_1(t) = \\frac{ A}{2} \\text{cos} (\\delta )$ and $u_2(t) = \\frac{ A}{2} \\text{sin}$. We can use the class\n", - "`AmplitudeFunction` to implement the relation between the optimization/ pulse parameters and the control amplitudes.\n" + "`AmplitudeFunction` to implement the relation between the pulse parameters and the control amplitudes.\n" ] }, { diff --git a/doc/source/qopt_features/transfer_function.ipynb b/doc/source/qopt_features/transfer_function.ipynb index 09f4380..bea91e4 100644 --- a/doc/source/qopt_features/transfer_function.ipynb +++ b/doc/source/qopt_features/transfer_function.ipynb @@ -6,7 +6,7 @@ "# Transfer Functions\n", "\n", "Transfer functions can be used to include the effects of classical electronics providing the control pulses. They can for example describe a limited bandwidth or finite rise times of voltage values.\n", - "Transfer functions are by definition linear and therefore can be represented as a matrix multiplication. This is implemented in qopt by the classes inheriting from `MatrixTF`. However, this representation becomes inefficient if the simulation contains a large number of time steps and only few correlations described by the transfer function. In this case, the transfer matrix becomes sparse and alternative representations like a convolution with a specific kernel becomes more efficient.\n", + "Transfer functions are by definition linear and therefore can be represented as a matrix multiplication. This is implemented in qopt by the classes inheriting from `MatrixTF`. However, this representation becomes inefficient if the simulation contains a large number of time steps and only few correlations described by the transfer function. In this case, the transfer matrix becomes sparse and alternative representations like a convolution with a specific kernel become more efficient.\n", "\n", "Let's start with a matrix transfer function that simulates the low pass filtering behavior of an RC element:\n" ], @@ -24,7 +24,7 @@ { "data": { "text/plain": "
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\n" 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\n" }, "metadata": { "needs_background": "light" @@ -33,6 +33,8 @@ } ], "source": [ + "import matplotlib.pyplot as plt\n", + "\n", "from qopt import *\n", "import numpy as np\n", "n_time_steps = 6\n", @@ -51,7 +53,9 @@ "# If we use the transfer function on its own, we have to set\n", "# the time steps manually.\n", "exponential_tf.set_times(delta_t * np.ones(n_time_steps))\n", - "exponential_tf.plot_pulse(random_pulse)\n" + "exponential_tf.plot_pulse(random_pulse)\n", + "\n", + "\n" ], "metadata": { "collapsed": false, @@ -63,7 +67,7 @@ { "cell_type": "markdown", "source": [ - "The oversampling creates a pulse on smaller time steps. We can also introduce a boundary behaviour to our pulses to simulate the behavior of our system in a longer algorithm.\n", + "The optimization parameters before the application of the transfer function are plotted as transparent bars with black lines. The transferred parameters / control amplitudes after the application of the transfer function are plotted as blue bars. The oversampling creates a pulse on smaller time steps controlled by the oversampling parameter. We can also introduce a boundary behaviour to our pulses to simulate the dynamics after the application of the pulse, which can include bleedthrough, which occurs when signals have an effect beyond the pulse time. This is of particular interest, when more than one pulse is applied in an experiment.\n", "\n", "Setting the bound type to `bound_type=('n', 5)` means that we simulate five additional time step after the oversampling. Other possible bound types include adding time steps only to the end of the pulse with `bound_type=('right_n', 5)` or to add time steps before the oversampling `bound_type=('x', 5)`." ], @@ -81,7 +85,7 @@ { "data": { "text/plain": "
", - "image/png": 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\n" 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\n" 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chj6M9pn7DPCGJFckeRFwI3B2i+sc1SjjeZz+41WTvBz4beCxLa2yW4uWCeuaNA8WdsZfVReT3Ak8QO/shPuq6kySO/r7j9A7S+QAsAr8jN7sZW6NOKYPAi8FPtafJV+sOb3j4IjjWRijjKeqzib5AvAw8HPg3qpa81S8WRvx9/Mh4ONJvkNvmeSuqprbWxsn+SfgjcDOJBeAvwFeAIuZCSOMZ6I88JYNktSYRV7qkSRNwOCXpMYY/JLUGINfkhpj8EtSYwx+SWqMwS9Jjfl/VrQqxyAylnMAAAAASUVORK5CYII=\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" + "image/png": 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\n" }, "metadata": {}, "output_type": "display_data" diff --git a/qopt/optimization_data.py b/qopt/optimization_data.py index 57aec55..72d5264 100644 --- a/qopt/optimization_data.py +++ b/qopt/optimization_data.py @@ -143,7 +143,7 @@ def to_dict(self): """ return {'final_cost': self.final_cost, 'indices': self.indices, - 'final_amps': self.final_parameters, + 'final_parameters': self.final_parameters, 'final_grad_norm': self.final_grad_norm, 'init_parameters': self.init_parameters, 'num_iter': self.num_iter, diff --git a/qopt/plotting.py b/qopt/plotting.py index 1546159..ff39a5e 100644 --- a/qopt/plotting.py +++ b/qopt/plotting.py @@ -39,7 +39,6 @@ import numpy as np import matplotlib.pyplot as plt -from mpl_toolkits import mplot3d from unittest import mock from warnings import warn from typing import Sequence @@ -99,7 +98,7 @@ def plot_bloch_vector_evolution( figsize = bloch_kwargs.pop('figsize', [5, 5]) view = bloch_kwargs.pop('view', [-60, 30]) fig = plt.figure(figsize=figsize) - axes = mplot3d.Axes3D(fig, azim=view[0], elev=view[1]) + axes = fig.add_subplot(projection='3d', azim=view[0], elev=view[1]) bloch_kwargs.setdefault('view', [-150, 30]) b = qt.Bloch(fig=fig, axes=axes, **bloch_kwargs) diff --git a/qopt/transfer_function.py b/qopt/transfer_function.py index 87d598f..d1ccc38 100644 --- a/qopt/transfer_function.py +++ b/qopt/transfer_function.py @@ -444,7 +444,10 @@ def set_absolute_times(self, absolute_y_times: np.ndarray) -> None: self._absolute_y_times = absolute_y_times self.set_times(np.diff(absolute_y_times)) - def plot_pulse(self, y: np.array) -> None: + def plot_pulse(self, + y: np.array, + xlabel='Time (a.u.)', + ylabel='Ctrl. Amplitude (a.u.)') -> None: """ Plot the control amplitudes corresponding to the given optimisation variables. @@ -454,6 +457,12 @@ def plot_pulse(self, y: np.array) -> None: y: array, shape (num_y, num_par) Raw optimization parameters. + xlabel: string + X-Label of the plot. + + ylabel: string + Y-Label of the plot + """ x = self(y) @@ -467,6 +476,8 @@ def plot_pulse(self, y: np.array) -> None: - self.x_times[n_padding_start], y_per_control, self._y_times[0], fill=False) + plt.xlabel(xlabel) + plt.ylabel(ylabel) plt.show() def _check_dimensions_datatype(self, y: np.array) -> None: diff --git a/setup.py b/setup.py index 99740b8..34aa9ef 100644 --- a/setup.py +++ b/setup.py @@ -10,7 +10,8 @@ author_email='j.teske@fz-juelich.de', description='Qubit Simulation and Optimal Control for Quantum Systems', package_dir={'qopt': 'qopt'}, - install_requires=['numpy', 'scipy', 'matplotlib', 'filter_functions>=1.1.2'], + install_requires=['numpy', 'scipy', 'matplotlib', + 'filter_functions>=1.1.2'], extras_require={ 'doc': ['ipython', 'ipykernel', 'nbsphinx', 'numpydoc', 'sphinx', 'jupyter_client', 'sphinx_rtd_theme'],