From 024611c20031e73147cfd1be450356c43da9b87f Mon Sep 17 00:00:00 2001 From: Rochisha Agarwal Date: Wed, 1 Jan 2025 23:08:00 +0530 Subject: [PATCH] qutip.control to qutip_qtrl --- ...control-pulseoptim-CRAB-2qubitInerac.ipynb | 113 +++------- examples/control-pulseoptim-CRAB-QFT.ipynb | 61 ++--- examples/control-pulseoptim-Hadamard.ipynb | 97 +++----- examples/control-pulseoptim-Lindbladian.ipynb | 146 ++++-------- examples/control-pulseoptim-QFT.ipynb | 210 +++++++----------- examples/control-pulseoptim-symplectic.ipynb | 106 +++------ 6 files changed, 232 insertions(+), 501 deletions(-) diff --git a/examples/control-pulseoptim-CRAB-2qubitInerac.ipynb b/examples/control-pulseoptim-CRAB-2qubitInerac.ipynb index bb4c5ef..f2395e4 100644 --- a/examples/control-pulseoptim-CRAB-2qubitInerac.ipynb +++ b/examples/control-pulseoptim-CRAB-2qubitInerac.ipynb @@ -57,12 +57,9 @@ "source": [ "from qutip import Qobj, identity, sigmax, sigmaz, tensor\n", "import random\n", - "import qutip.logging_utils as logging\n", - "logger = logging.get_logger()\n", - "#Set this to None or logging.WARN for 'quiet' execution\n", - "log_level = logging.INFO\n", + "\n", "#QuTiP control modules\n", - "import qutip.control.pulseoptim as cpo\n", + "import qutip_qtrl.pulseoptim as cpo\n", "\n", "example_name = '2qubitInteract'" ] @@ -239,52 +236,7 @@ "cell_type": "code", "execution_count": 8, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:qutip.control.dynamics:Setting memory optimisations for level 0\n", - "INFO:qutip.control.dynamics:Internal operator data type choosen to be \n", - "INFO:qutip.control.dynamics:phased dynamics generator caching True\n", - "INFO:qutip.control.dynamics:propagator gradient caching True\n", - "INFO:qutip.control.dynamics:eigenvector adjoint caching True\n", - "INFO:qutip.control.dynamics:use sparse eigen decomp False\n", - "INFO:qutip.control.pulseoptim:System configuration:\n", - "Drift Hamiltonian:\n", - "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n", - "Qobj data =\n", - "[[ 1.67112549 0.68625416 0.90563968 0. ]\n", - " [ 0.68625416 -0.13810698 0. 0.90563968]\n", - " [ 0.90563968 0. 0.13810698 0.68625416]\n", - " [ 0. 0.90563968 0.68625416 -1.67112549]]\n", - "Control 1 Hamiltonian:\n", - "Quantum object: dims = [[2, 2], [2, 2]], shape = (4, 4), type = oper, isherm = True\n", - "Qobj data =\n", - "[[ 1. 0. 0. 0.]\n", - " [ 0. -1. 0. 0.]\n", - " [ 0. 0. -1. 0.]\n", - " [ 0. 0. 0. 1.]]\n", - "Initial state / operator:\n", - "Quantum object: dims = [[2, 2], [1, 1]], shape = (4, 1), type = ket\n", - "Qobj data =\n", - "[[ 1.]\n", - " [ 0.]\n", - " [ 0.]\n", - " [ 0.]]\n", - "Target state / operator:\n", - "Quantum object: dims = [[2, 2], [1, 1]], shape = (4, 1), type = ket\n", - "Qobj data =\n", - "[[ 0.]\n", - " [ 0.]\n", - " [ 0.]\n", - " [ 1.]]\n", - "INFO:qutip.control.pulseoptim:Initial amplitudes output to file: ctrl_amps_initial_2qubitInteract_n_ts100_ptypeDEF.txt\n", - "INFO:qutip.control.optimizer:Optimising pulse(s) using CRAB with 'fmin' (Nelder-Mead) method\n", - "INFO:qutip.control.pulseoptim:Final amplitudes output to file: ctrl_amps_final_2qubitInteract_n_ts100_ptypeDEF.txt\n" - ] - } - ], + "outputs": [], "source": [ "result = cpo.opt_pulse_crab_unitary(H_d, H_c, psi_0, psi_targ, n_ts, evo_time, \n", " fid_err_targ=fid_err_targ, \n", @@ -293,7 +245,7 @@ " method_params={'xtol':1e-3},\n", " guess_pulse_type=None, guess_pulse_action='modulate',\n", " out_file_ext=f_ext,\n", - " log_level=log_level, gen_stats=True)" + " gen_stats=True)" ] }, { @@ -327,43 +279,43 @@ "------------------------------------\n", "---- Control optimisation stats ----\n", "**** Timings (HH:MM:SS.US) ****\n", - "Total wall time elapsed during optimisation: 0:00:05.187298\n", - "Wall time computing Hamiltonians: 0:00:00.096692 (1.86%)\n", - "Wall time computing propagators: 0:00:04.988672 (96.17%)\n", - "Wall time computing forward propagation: 0:00:00.027030 (0.52%)\n", - "Wall time computing onward propagation: 0:00:00.019223 (0.37%)\n", + "Total wall time elapsed during optimisation: 0:00:01.686192\n", + "Wall time computing Hamiltonians: 0:00:00.056275 (3.34%)\n", + "Wall time computing propagators: 0:00:01.567838 (92.98%)\n", + "Wall time computing forward propagation: 0:00:00.014226 (0.84%)\n", + "Wall time computing onward propagation: 0:00:00.011270 (0.67%)\n", "Wall time computing gradient: 0:00:00 (0.00%)\n", "\n", "**** Iterations and function calls ****\n", - "Number of iterations: 125\n", - "Number of fidelity function calls: 192\n", - "Number of times fidelity is computed: 192\n", + "Number of iterations: 90\n", + "Number of fidelity function calls: 140\n", + "Number of times fidelity is computed: 140\n", "Number of gradient function calls: 0\n", "Number of times gradients are computed: 0\n", - "Number of times timeslot evolution is recomputed: 192\n", + "Number of times timeslot evolution is recomputed: 140\n", "\n", "**** Control amplitudes ****\n", - "Number of control amplitude updates: 191\n", - "Mean number of updates per iteration: 1.528\n", - "Number of timeslot values changed: 19099\n", - "Mean number of timeslot changes per update: 99.99476439790575\n", - "Number of amplitude values changed: 19099\n", - "Mean number of amplitude changes per update: 99.99476439790575\n", + "Number of control amplitude updates: 139\n", + "Mean number of updates per iteration: 1.5444444444444445\n", + "Number of timeslot values changed: 13899\n", + "Mean number of timeslot changes per update: 99.99280575539568\n", + "Number of amplitude values changed: 13899\n", + "Mean number of amplitude changes per update: 99.99280575539568\n", "------------------------------------\n", "Final evolution\n", - "Quantum object: dims = [[2, 2], [1, 1]], shape = (4, 1), type = ket\n", + "Quantum object: dims=[[2, 2], [1, 1]], shape=(4, 1), type='ket', dtype=Dense\n", "Qobj data =\n", - "[[ 0.03046450-0.00243049j]\n", - " [-0.01434436-0.00097183j]\n", - " [-0.00583341-0.00069853j]\n", - " [ 0.97780868-0.20667602j]]\n", + "[[ 0.00994679-0.00454319j]\n", + " [ 0.03854583-0.00168208j]\n", + " [-0.00965104+0.01581564j]\n", + " [ 0.9977848 +0.04973962j]]\n", "\n", "********* Summary *****************\n", - "Final fidelity error 0.0005877800057074722\n", + "Final fidelity error 0.0009762099390997481\n", "Final gradient normal 0.0\n", "Terminated due to Goal achieved\n", - "Number of iterations 125\n", - "Completed in 0:00:05.187298 HH:MM:SS.US\n" + "Number of iterations 90\n", + "Completed in 0:00:01.686192 HH:MM:SS.US\n" ] } ], @@ -395,14 +347,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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QYh7u+FwK/LOkqekEYbA9p171a97fkpUqppFVK+4GvBn4JWP7wf6QpO1S+9tp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\nRU5ErkJggg==\n", 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", 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SoftwareVersion
QuTiP4.1.0
Numpy1.11.3
SciPy0.18.1
matplotlib2.0.0
Cython0.25.2
Number of CPUs4
BLAS InfoINTEL MKL
IPython5.1.0
Python3.6.0 |Anaconda 4.3.1 (64-bit)| (default, Dec 23 2016, 12:22:00) \n", - "[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]
OSposix [linux]
Fri Jul 14 11:59:44 2017 BST
" + "
SoftwareVersion
QuTiP5.1.0.dev0+0b4260e
Numpy1.26.4
SciPy1.13.0
matplotlib3.9.0
Number of CPUs8
BLAS InfoGeneric
IPython8.25.0
Python3.12.3 | packaged by Anaconda, Inc. | (main, May 6 2024, 19:46:43) [GCC 11.2.0]
OSposix [linux]
Cython3.0.10
Wed Jan 01 22:54:12 2025 IST
" ], "text/plain": [ "" @@ -502,7 +453,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/examples/control-pulseoptim-CRAB-QFT.ipynb b/examples/control-pulseoptim-CRAB-QFT.ipynb index a70ed7a..011a04f 100644 --- a/examples/control-pulseoptim-CRAB-QFT.ipynb +++ b/examples/control-pulseoptim-CRAB-QFT.ipynb @@ -62,14 +62,11 @@ "outputs": [], "source": [ "from qutip import Qobj, identity, sigmax, sigmay, sigmaz, tensor\n", - "from qutip.qip.algorithms import qft\n", - "import qutip.logging_utils as logging\n", - "logger = logging.get_logger()\n", - "#Set this to None or logging.WARN for 'quiet' execution\n", - "log_level = logging.INFO\n", + "from qutip_qip.algorithms import qft\n", + "\n", "#QuTiP control modules\n", - "import qutip.control.pulseoptim as cpo\n", - "import qutip.control.pulsegen as pulsegen\n", + "import qutip_qtrl.pulseoptim as cpo\n", + "import qutip_qtrl.pulsegen as pulsegen\n", "\n", "example_name = 'QFT'\n" ] @@ -102,7 +99,7 @@ "# start point for the gate evolution\n", "U_0 = identity(4)\n", "# Target for the gate evolution - Quantum Fourier Transform gate\n", - "U_targ = qft.qft(2)" + "U_targ = qft(2)" ] }, { @@ -190,7 +187,7 @@ " dyn_type='UNIT', \n", " prop_type='DIAG', \n", " fid_type='UNIT', fid_params={'phase_option':'PSU'}, \n", - " log_level=log_level, gen_stats=True)\n", + " gen_stats=True)\n", " " ] }, @@ -207,23 +204,7 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:qutip.control.pulsegen:The number of CRAB coefficients per basis function has been estimated as 3, which means a total of 6 optimisation variables for this pulse. Based on the dimension (4) of the system\n", - "INFO:qutip.control.pulsegen:The number of CRAB coefficients per basis function has been estimated as 3, which means a total of 6 optimisation variables for this pulse. Based on the dimension (4) of the system\n", - "INFO:qutip.control.pulsegen:The number of CRAB coefficients per basis function has been estimated as 3, which means a total of 6 optimisation variables for this pulse. Based on the dimension (4) of the system\n", - "INFO:qutip.control.dynamics:Setting memory optimisations for level 0\n", - "INFO:qutip.control.dynamics:Internal operator data type choosen to be \n", - "INFO:qutip.control.dynamics:phased dynamics generator caching True\n", - "INFO:qutip.control.dynamics:propagator gradient caching True\n", - "INFO:qutip.control.dynamics:eigenvector adjoint caching True\n", - "INFO:qutip.control.dynamics:use sparse eigen decomp False\n" - ] - } - ], + "outputs": [], "source": [ "dyn = optim.dynamics\n", "\n", @@ -290,22 +271,8 @@ "output_type": "stream", "text": [ "Initial amplitudes output to file: ctrl_amps_initial_QFT_n_ts200.txt\n", - "***********************************\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:qutip.control.optimizer:Optimising pulse(s) using CRAB with 'fmin' (Nelder-Mead) method\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Starting pulse optimisation\n", - "Final amplitudes output to file: ctrl_amps_final_QFT_n_ts200.txt\n" + "***********************************\n", + "Starting pulse optimisation\n" ] } ], @@ -336,7 +303,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { "collapsed": false }, @@ -414,14 +381,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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cd17VZTbSg/pEqf1m9lxNFVRBQwQqC15MXv+X0xq4V5Ud4iYN5IvO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\nFcC1CZTvtBiLFi1i++23B2DIkCEsWpR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", "text/plain": [ "" ] @@ -462,7 +429,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { "collapsed": false }, @@ -520,7 +487,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/examples/control-pulseoptim-Hadamard.ipynb b/examples/control-pulseoptim-Hadamard.ipynb index a41a6ca..c502521 100644 --- a/examples/control-pulseoptim-Hadamard.ipynb +++ b/examples/control-pulseoptim-Hadamard.ipynb @@ -55,13 +55,10 @@ "outputs": [], "source": [ "from qutip import Qobj, identity, sigmax, sigmaz\n", - "from qutip.qip import hadamard_transform\n", - "import qutip.logging_utils as logging\n", - "logger = logging.get_logger()\n", - "#Set this to None or logging.WARN for 'quiet' execution\n", - "log_level = logging.INFO\n", + "from qutip_qip.operations import hadamard_transform\n", + "\n", "#QuTiP control modules\n", - "import qutip.control.pulseoptim as cpo\n", + "import qutip_qtrl.pulseoptim as cpo\n", "\n", "example_name = 'Hadamard'\n" ] @@ -117,7 +114,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -150,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -181,7 +178,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -198,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -224,50 +221,13 @@ "cell_type": "code", "execution_count": 9, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:qutip.control.dynamics:Setting memory optimisations for level 0\n", - "INFO:qutip.control.dynamics:Internal operator data type choosen to be \n", - "INFO:qutip.control.dynamics:phased dynamics generator caching True\n", - "INFO:qutip.control.dynamics:propagator gradient caching True\n", - "INFO:qutip.control.dynamics:eigenvector adjoint caching True\n", - "INFO:qutip.control.dynamics:use sparse eigen decomp False\n", - "INFO:qutip.control.pulseoptim:System configuration:\n", - "Drift Hamiltonian:\n", - "Quantum object: dims = [[2], [2]], shape = (2, 2), type = oper, isherm = True\n", - "Qobj data =\n", - "[[ 1. 0.]\n", - " [ 0. -1.]]\n", - "Control 1 Hamiltonian:\n", - "Quantum object: dims = [[2], [2]], shape = (2, 2), type = oper, isherm = True\n", - "Qobj data =\n", - "[[ 0. 1.]\n", - " [ 1. 0.]]\n", - "Initial state / operator:\n", - "Quantum object: dims = [[2], [2]], shape = (2, 2), type = oper, isherm = True\n", - "Qobj data =\n", - "[[ 1. 0.]\n", - " [ 0. 1.]]\n", - "Target state / operator:\n", - "Quantum object: dims = [[2], [2]], shape = (2, 2), type = oper, isherm = True\n", - "Qobj data =\n", - "[[ 0.70710678 0.70710678]\n", - " [ 0.70710678 -0.70710678]]\n", - "INFO:qutip.control.pulseoptim:Initial amplitudes output to file: ctrl_amps_initial_Hadamard_n_ts10_ptypeRND.txt\n", - "INFO:qutip.control.optimizer:Optimising pulse(s) using GRAPE with 'fmin_l_bfgs_b' method\n", - "INFO:qutip.control.pulseoptim:Final amplitudes output to file: ctrl_amps_final_Hadamard_n_ts10_ptypeRND.txt\n" - ] - } - ], + "outputs": [], "source": [ "result = cpo.optimize_pulse_unitary(H_d, H_c, U_0, U_targ, n_ts, evo_time, \n", " fid_err_targ=fid_err_targ, min_grad=min_grad, \n", " max_iter=max_iter, max_wall_time=max_wall_time, \n", " out_file_ext=f_ext, init_pulse_type=p_type, \n", - " log_level=log_level, gen_stats=True)" + " gen_stats=True)" ] }, { @@ -301,12 +261,12 @@ "------------------------------------\n", "---- Control optimisation stats ----\n", "**** Timings (HH:MM:SS.US) ****\n", - "Total wall time elapsed during optimisation: 0:00:00.079766\n", - "Wall time computing Hamiltonians: 0:00:00.000455 (0.57%)\n", - "Wall time computing propagators: 0:00:00.014075 (17.65%)\n", - "Wall time computing forward propagation: 0:00:00.000148 (0.19%)\n", - "Wall time computing onward propagation: 0:00:00.000145 (0.18%)\n", - "Wall time computing gradient: 0:00:00.000529 (0.66%)\n", + "Total wall time elapsed during optimisation: 0:00:00.013250\n", + "Wall time computing Hamiltonians: 0:00:00.000508 (3.84%)\n", + "Wall time computing propagators: 0:00:00.009398 (70.93%)\n", + "Wall time computing forward propagation: 0:00:00.000083 (0.63%)\n", + "Wall time computing onward propagation: 0:00:00.000075 (0.57%)\n", + "Wall time computing gradient: 0:00:00.000456 (3.44%)\n", "\n", "**** Iterations and function calls ****\n", "Number of iterations: 5\n", @@ -325,17 +285,17 @@ "Mean number of amplitude changes per update: 10.0\n", "------------------------------------\n", "Final evolution\n", - "Quantum object: dims = [[2], [2]], shape = (2, 2), type = oper, isherm = False\n", + "Quantum object: dims=[[2], [2]], shape=(2, 2), type='oper', dtype=Dense, isherm=False\n", "Qobj data =\n", - "[[ 3.10467786e-08+0.70710743j 5.97691111e-07+0.70710614j]\n", - " [ -5.97691111e-07+0.70710614j 3.10467785e-08-0.70710743j]]\n", + "[[-4.71133235e-07+0.70710536j -2.26948034e-06+0.70710821j]\n", + " [ 2.26948034e-06+0.70710821j -4.71133235e-07-0.70710536j]]\n", "\n", "********* Summary *****************\n", - "Final fidelity error 5.941913627793838e-13\n", - "Final gradient normal 6.91241029719096e-05\n", + "Final fidelity error 4.71733763163229e-12\n", + "Final gradient normal 2.2839300353539612e-05\n", "Terminated due to Goal achieved\n", "Number of iterations 5\n", - "Completed in 0:00:00.079766 HH:MM:SS.US\n" + "Completed in 0:00:00.013250 HH:MM:SS.US\n" ] } ], @@ -367,14 +327,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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OJuko4CiAkSPdFdXMbHVXpJPEhpKWz+SZn4HasHEhvcMDpBEsPgCcR5quu0cR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\nZi3JCcrMzFrS/wfsJbzr2Op3OgAAAABJRU5ErkJggg==\n", 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", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -411,20 +371,19 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
SoftwareVersion
QuTiP4.1.0
Numpy1.11.3
SciPy0.18.1
matplotlib2.0.0
Cython0.25.2
Number of CPUs4
BLAS InfoINTEL MKL
IPython5.1.0
Python3.6.0 |Anaconda 4.3.1 (64-bit)| (default, Dec 23 2016, 12:22:00) \n", - "[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]
OSposix [linux]
Fri Jul 14 16:45:36 2017 BST
" + "
SoftwareVersion
QuTiP5.1.0.dev0+0b4260e
Numpy1.26.4
SciPy1.13.0
matplotlib3.9.0
Number of CPUs8
BLAS InfoGeneric
IPython8.25.0
Python3.12.3 | packaged by Anaconda, Inc. | (main, May 6 2024, 19:46:43) [GCC 11.2.0]
OSposix [linux]
Cython3.0.10
Wed Jan 01 22:51:38 2025 IST
" ], "text/plain": [ "" ] }, - "execution_count": 13, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -466,7 +425,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/examples/control-pulseoptim-Lindbladian.ipynb b/examples/control-pulseoptim-Lindbladian.ipynb index 766af19..3dc15b0 100644 --- a/examples/control-pulseoptim-Lindbladian.ipynb +++ b/examples/control-pulseoptim-Lindbladian.ipynb @@ -54,14 +54,11 @@ "outputs": [], "source": [ "from qutip import Qobj, identity, sigmax, sigmay, sigmaz, sigmam, tensor\n", - "from qutip.superoperator import liouvillian, sprepost\n", - "from qutip.qip import hadamard_transform\n", - "import qutip.logging_utils as logging\n", - "logger = logging.get_logger()\n", - "#Set this to None or logging.WARN for 'quiet' execution\n", - "log_level = logging.INFO\n", + "from qutip import liouvillian, sprepost\n", + "from qutip_qip.operations import hadamard_transform\n", + "\n", "#QuTiP control modules\n", - "import qutip.control.pulseoptim as cpo\n", + "import qutip_qtrl.pulseoptim as cpo\n", "\n", "example_name = 'Lindblad'" ] @@ -204,61 +201,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:qutip.control.dynamics:Setting memory optimisations for level 0\n", - "INFO:qutip.control.dynamics:Internal operator data type choosen to be \n", - "INFO:qutip.control.dynamics:phased dynamics generator caching True\n", - "INFO:qutip.control.dynamics:propagator gradient caching True\n", - "INFO:qutip.control.dynamics:eigenvector adjoint caching True\n", - "INFO:qutip.control.dynamics:use sparse eigen decomp False\n", - "INFO:qutip.control.pulseoptim:System configuration:\n", - "Drift dynamics generator:\n", - "Quantum object: dims = [[[2], [2]], [[2], [2]]], shape = (4, 4), type = super, isherm = False\n", - "Qobj data =\n", - "[[-0.10+0.j 0.00-0.05j 0.00+0.05j 0.00+0.j ]\n", - " [ 0.00-0.05j -0.05+1.j 0.00+0.j 0.00+0.05j]\n", - " [ 0.00+0.05j 0.00+0.j -0.05-1.j 0.00-0.05j]\n", - " [ 0.10+0.j 0.00+0.05j 0.00-0.05j 0.00+0.j ]]\n", - "Control 1 dynamics generator:\n", - "Quantum object: dims = [[[2], [2]], [[2], [2]]], shape = (4, 4), type = super, isherm = False\n", - "Qobj data =\n", - "[[ 0.+0.j 0.+0.j 0.+0.j 0.+0.j]\n", - " [ 0.+0.j 0.+2.j 0.+0.j 0.+0.j]\n", - " [ 0.+0.j 0.+0.j 0.-2.j 0.+0.j]\n", - " [ 0.+0.j 0.+0.j 0.+0.j 0.+0.j]]\n", - "Control 2 dynamics generator:\n", - "Quantum object: dims = [[[2], [2]], [[2], [2]]], shape = (4, 4), type = super, isherm = False\n", - "Qobj data =\n", - "[[ 0.+0.j 0.-1.j 0.+1.j 0.+0.j]\n", - " [ 0.-1.j 0.+0.j 0.+0.j 0.+1.j]\n", - " [ 0.+1.j 0.+0.j 0.+0.j 0.-1.j]\n", - " [ 0.+0.j 0.+1.j 0.-1.j 0.+0.j]]\n", - "Initial state / operator:\n", - "Quantum object: dims = [[[2], [2]], [[2], [2]]], shape = (4, 4), type = super, isherm = True\n", - "Qobj data =\n", - "[[ 1. 0. 0. 0.]\n", - " [ 0. 1. 0. 0.]\n", - " [ 0. 0. 1. 0.]\n", - " [ 0. 0. 0. 1.]]\n", - "Target state / operator:\n", - "Quantum object: dims = [[[2], [2]], [[2], [2]]], shape = (4, 4), type = super, isherm = True\n", - "Qobj data =\n", - "[[ 0.5 0.5 0.5 0.5]\n", - " [ 0.5 -0.5 0.5 -0.5]\n", - " [ 0.5 0.5 -0.5 -0.5]\n", - " [ 0.5 -0.5 -0.5 0.5]]\n", - "INFO:qutip.control.pulseoptim:Initial amplitudes output to file: ctrl_amps_initial_Lindblad_n_ts10_ptypeRND.txt\n", - "INFO:qutip.control.optimizer:Optimising pulse(s) using GRAPE with 'fmin_l_bfgs_b' method\n", - "INFO:qutip.control.pulseoptim:Final amplitudes output to file: ctrl_amps_final_Lindblad_n_ts10_ptypeRND.txt\n" - ] - } - ], + "outputs": [], "source": [ "# Note that this call will take the defaults\n", "# dyn_type='GEN_MAT'\n", @@ -275,7 +220,7 @@ " fid_err_targ=fid_err_targ, min_grad=min_grad, \n", " max_iter=max_iter, max_wall_time=max_wall_time, \n", " out_file_ext=f_ext, init_pulse_type=p_type, \n", - " log_level=log_level, gen_stats=True)" + " gen_stats=True)" ] }, { @@ -287,7 +232,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -298,48 +243,48 @@ "------------------------------------\n", "---- Control optimisation stats ----\n", "**** Timings (HH:MM:SS.US) ****\n", - "Total wall time elapsed during optimisation: 0:00:01.340385\n", - "Wall time computing Hamiltonians: 0:00:00.020057 (1.50%)\n", - "Wall time computing propagators: 0:00:01.178558 (87.93%)\n", - "Wall time computing forward propagation: 0:00:00.005692 (0.42%)\n", - "Wall time computing onward propagation: 0:00:00.004413 (0.33%)\n", - "Wall time computing gradient: 0:00:00.091266 (6.81%)\n", + "Total wall time elapsed during optimisation: 0:00:00.832299\n", + "Wall time computing Hamiltonians: 0:00:00.015355 (1.84%)\n", + "Wall time computing propagators: 0:00:00.696612 (83.70%)\n", + "Wall time computing forward propagation: 0:00:00.003450 (0.41%)\n", + "Wall time computing onward propagation: 0:00:00.002888 (0.35%)\n", + "Wall time computing gradient: 0:00:00.060922 (7.32%)\n", "\n", "**** Iterations and function calls ****\n", - "Number of iterations: 201\n", - "Number of fidelity function calls: 265\n", - "Number of times fidelity is computed: 265\n", - "Number of gradient function calls: 265\n", - "Number of times gradients are computed: 265\n", - "Number of times timeslot evolution is recomputed: 265\n", + "Number of iterations: 200\n", + "Number of fidelity function calls: 249\n", + "Number of times fidelity is computed: 249\n", + "Number of gradient function calls: 249\n", + "Number of times gradients are computed: 249\n", + "Number of times timeslot evolution is recomputed: 249\n", "\n", "**** Control amplitudes ****\n", - "Number of control amplitude updates: 264\n", - "Mean number of updates per iteration: 1.3134328358208955\n", - "Number of timeslot values changed: 2640\n", + "Number of control amplitude updates: 248\n", + "Mean number of updates per iteration: 1.24\n", + "Number of timeslot values changed: 2480\n", "Mean number of timeslot changes per update: 10.0\n", - "Number of amplitude values changed: 5280\n", + "Number of amplitude values changed: 4960\n", "Mean number of amplitude changes per update: 20.0\n", "------------------------------------\n", "Final evolution\n", - "Quantum object: dims = [[[2], [2]], [[2], [2]]], shape = (4, 4), type = super, isherm = False\n", + "Quantum object: dims=[[[2], [2]], [[2], [2]]], shape=(4, 4), type='super', dtype=Dense, isherm=False\n", "Qobj data =\n", - "[[ 0.49398354 -9.35884799e-17j 0.43895309 +4.70500365e-04j\n", - " 0.43895309 -4.70500365e-04j 0.50438676 +1.16197332e-16j]\n", - " [ 0.43683752 -2.24436864e-03j -0.44201112 -3.02814020e-03j\n", - " 0.43197985 +3.42846832e-03j -0.43695905 +4.27534789e-03j]\n", - " [ 0.43683752 +2.24436864e-03j 0.43197985 -3.42846832e-03j\n", - " -0.44201112 +3.02814020e-03j -0.43695905 -4.27534789e-03j]\n", - " [ 0.50601646 +1.18358316e-16j -0.43895309 -4.70500365e-04j\n", - " -0.43895309 +4.70500365e-04j 0.49561324 -1.33018845e-16j]]\n", + "[[ 0.502043 +4.84597585e-17j 0.43195294-5.62284905e-04j\n", + " 0.43195294+5.62284905e-04j 0.49339757+2.10705838e-17j]\n", + " [ 0.43684307+5.93052270e-03j -0.43899019+5.26221162e-03j\n", + " 0.44784665-7.61987345e-03j -0.43834568-5.71314331e-03j]\n", + " [ 0.43684307-5.93052270e-03j 0.44784665+7.61987345e-03j\n", + " -0.43899019-5.26221162e-03j -0.43834568+5.71314331e-03j]\n", + " [ 0.497957 -2.00768725e-17j -0.43195294+5.62284905e-04j\n", + " -0.43195294-5.62284905e-04j 0.50660243+1.68717425e-17j]]\n", "\n", "********* Summary *****************\n", - "Initial fidelity error 0.7448841629400895\n", - "Final fidelity error 0.005876671350497931\n", - "Final gradient normal 0.00012944172414442896\n", + "Initial fidelity error 0.6007937056650776\n", + "Final fidelity error 0.0059237473190501405\n", + "Final gradient normal 0.0004697512373275826\n", "Terminated due to Iteration or fidelity function call limit reached\n", - "Number of iterations 201\n", - "Completed in 0:00:01.340385 HH:MM:SS.US\n" + "Number of iterations 200\n", + "Completed in 0:00:00.832299 HH:MM:SS.US\n" ] } ], @@ -364,14 +309,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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ljUrXQHRp7NixTJ/+NwdkZmZWAZK6G5XlLXma+E4HdiG7qPJKsutczlj30NYS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\nzMwqyQnKzMwqyQnKzMwq6f8D/LrX+ZmFOMwAAAAASUVORK5CYII=\n", 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", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -409,20 +354,19 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
SoftwareVersion
QuTiP4.2.0
Numpy1.13.1
SciPy0.19.1
matplotlib2.0.2
Cython0.26
Number of CPUs4
BLAS InfoINTEL MKL
IPython6.1.0
Python3.6.2 |Continuum Analytics, Inc.| (default, Jul 20 2017, 13:51:32) \n", - "[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]
OSposix [linux]
Thu Apr 05 11:50:14 2018 BST
" + "
SoftwareVersion
QuTiP5.1.0.dev0+0b4260e
Numpy1.26.4
SciPy1.13.0
matplotlib3.9.0
Number of CPUs8
BLAS InfoGeneric
IPython8.25.0
Python3.12.3 | packaged by Anaconda, Inc. | (main, May 6 2024, 19:46:43) [GCC 11.2.0]
OSposix [linux]
Cython3.0.10
Wed Jan 01 22:49:39 2025 IST
" ], "text/plain": [ "" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -460,7 +404,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.2" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/examples/control-pulseoptim-QFT.ipynb b/examples/control-pulseoptim-QFT.ipynb index a40ebf6..c06dfec 100644 --- a/examples/control-pulseoptim-QFT.ipynb +++ b/examples/control-pulseoptim-QFT.ipynb @@ -68,14 +68,11 @@ "outputs": [], "source": [ "from qutip import Qobj, identity, sigmax, sigmay, sigmaz, tensor\n", - "from qutip.qip.algorithms import qft\n", - "import qutip.logging_utils as logging\n", - "logger = logging.get_logger()\n", - "#Set this to None or logging.WARN for 'quiet' execution\n", - "log_level = logging.INFO\n", + "from qutip_qip.algorithms import qft\n", + "\n", "#QuTiP control modules\n", - "import qutip.control.pulseoptim as cpo\n", - "import qutip.control.pulsegen as pulsegen\n", + "import qutip_qtrl.pulseoptim as cpo\n", + "import qutip_qtrl.pulsegen as pulsegen\n", "\n", "example_name = 'QFT'" ] @@ -96,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -115,7 +112,7 @@ "# start point for the gate evolution\n", "U_0 = identity(4)\n", "# Target for the gate evolution - Quantum Fourier Transform gate\n", - "U_targ = qft.qft(2)" + "U_targ = qft(2)" ] }, { @@ -136,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -162,7 +159,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -195,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -214,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -240,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -255,7 +252,7 @@ " dyn_type='UNIT', \n", " fid_params={'phase_option':'PSU'},\n", " init_pulse_type=p_type, \n", - " log_level=log_level, gen_stats=True)\n", + " gen_stats=True)\n", "\n", "# **** get handles to the other objects ****\n", "optim.test_out_files = 0\n", @@ -281,24 +278,11 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:qutip.control.dynamics:Setting memory optimisations for level 0\n", - "INFO:qutip.control.dynamics:Internal operator data type choosen to be \n", - "INFO:qutip.control.dynamics:phased dynamics generator caching True\n", - "INFO:qutip.control.dynamics:propagator gradient caching True\n", - "INFO:qutip.control.dynamics:eigenvector adjoint caching True\n", - "INFO:qutip.control.dynamics:use sparse eigen decomp False\n", - "INFO:qutip.control.optimizer:Optimising pulse(s) using GRAPE with 'fmin_l_bfgs_b' method\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -309,109 +293,71 @@ "+++++++++++++++++++++++++++++++++++\n", "Starting pulse optimisation for T=1\n", "+++++++++++++++++++++++++++++++++++\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:qutip.control.dynamics:Setting memory optimisations for level 0\n", - "INFO:qutip.control.dynamics:Using operator data type \n", - "INFO:qutip.control.dynamics:phased dynamics generator caching True\n", - "INFO:qutip.control.dynamics:propagator gradient caching True\n", - "INFO:qutip.control.dynamics:eigenvector adjoint caching True\n", - "INFO:qutip.control.dynamics:use sparse eigen decomp False\n", - "INFO:qutip.control.optimizer:Optimising pulse(s) using GRAPE with 'fmin_l_bfgs_b' method\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "\n", "Final amplitudes output to file: ctrl_amps_final_QFT_n_ts20_ptypeLIN.txt\n", "\n", "------------------------------------\n", "---- Control optimisation stats ----\n", "**** Timings (HH:MM:SS.US) ****\n", - "Total wall time elapsed during optimisation: 0:00:00.277534\n", - "Wall time computing Hamiltonians: 0:00:00.015000 (5.40%)\n", - "Wall time computing propagators: 0:00:00.191946 (69.16%)\n", - "Wall time computing forward propagation: 0:00:00.002414 (0.87%)\n", - "Wall time computing onward propagation: 0:00:00.002215 (0.80%)\n", - "Wall time computing gradient: 0:00:00.044586 (16.07%)\n", + "Total wall time elapsed during optimisation: 0:00:00.239246\n", + "Wall time computing Hamiltonians: 0:00:00.014204 (5.94%)\n", + "Wall time computing propagators: 0:00:00.177386 (74.14%)\n", + "Wall time computing forward propagation: 0:00:00.001417 (0.59%)\n", + "Wall time computing onward propagation: 0:00:00.001277 (0.53%)\n", + "Wall time computing gradient: 0:00:00.025678 (10.73%)\n", "\n", "**** Iterations and function calls ****\n", - "Number of iterations: 60\n", - "Number of fidelity function calls: 64\n", - "Number of times fidelity is computed: 64\n", - "Number of gradient function calls: 64\n", - "Number of times gradients are computed: 64\n", - "Number of times timeslot evolution is recomputed: 64\n", + "Number of iterations: 56\n", + "Number of fidelity function calls: 58\n", + "Number of times fidelity is computed: 58\n", + "Number of gradient function calls: 58\n", + "Number of times gradients are computed: 58\n", + "Number of times timeslot evolution is recomputed: 58\n", "\n", "**** Control amplitudes ****\n", - "Number of control amplitude updates: 63\n", - "Mean number of updates per iteration: 1.05\n", - "Number of timeslot values changed: 701\n", - "Mean number of timeslot changes per update: 11.126984126984127\n", - "Number of amplitude values changed: 1343\n", - "Mean number of amplitude changes per update: 21.317460317460316\n", + "Number of control amplitude updates: 57\n", + "Mean number of updates per iteration: 1.0178571428571428\n", + "Number of timeslot values changed: 630\n", + "Mean number of timeslot changes per update: 11.052631578947368\n", + "Number of amplitude values changed: 1261\n", + "Mean number of amplitude changes per update: 22.12280701754386\n", "------------------------------------\n", "Final evolution\n", - "Quantum object: dims = [[4], [4]], shape = (4, 4), type = oper, isherm = False\n", + "Quantum object: dims=[[4], [4]], shape=(4, 4), type='oper', dtype=Dense, isherm=False\n", "Qobj data =\n", - "[[-0.16998421+0.48769402j 0.00937949+0.31030265j -0.78519291+0.0495552j\n", - " -0.06609584+0.11632679j]\n", - " [ 0.01430120+0.29484354j 0.12448164-0.48737042j -0.07997799-0.24264738j\n", - " 0.76748036+0.07440979j]\n", - " [-0.79272858+0.02269328j -0.03496912-0.24748526j 0.08419464+0.50446146j\n", - " 0.04342906-0.21245808j]\n", - " [-0.03607690+0.12604581j 0.76668361-0.01797591j 0.00951480-0.23255432j\n", - " -0.20301010-0.54708215j]]\n", + "[[-0.18496187+0.47646074j 0.00650274+0.32387897j -0.78128529+0.06667085j\n", + " -0.08600938+0.10763398j]\n", + " [ 0.0091788 +0.3089635j 0.15052907-0.4653102j -0.10161082-0.24956273j\n", + " 0.76676768+0.06888927j]\n", + " [-0.7898436 +0.03568447j -0.0602883 -0.2559864j 0.11247485+0.50171211j\n", + " 0.04752862-0.19770026j]\n", + " [-0.06513334+0.11774096j 0.76595754+0.00573387j 0.02496387-0.21806685j\n", + " -0.21183752-0.54965389j]]\n", "\n", "********* Summary *****************\n", - "Final fidelity error 0.3436799944999004\n", - "Final gradient normal 0.015892258406309603\n", + "Final fidelity error 0.34374119125360847\n", + "Final gradient normal 0.014751686098976114\n", "Terminated due to function converged\n", - "Number of iterations 60\n", - "Completed in 0:00:00.277534 HH:MM:SS.US\n", + "Number of iterations 56\n", + "Completed in 0:00:00.239246 HH:MM:SS.US\n", "Initial amplitudes output to file: ctrl_amps_initial_QFT_n_ts20_ptypeLIN.txt\n", "***********************************\n", "\n", "+++++++++++++++++++++++++++++++++++\n", "Starting pulse optimisation for T=3\n", "+++++++++++++++++++++++++++++++++++\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:qutip.control.dynamics:Setting memory optimisations for level 0\n", - "INFO:qutip.control.dynamics:Using operator data type \n", - "INFO:qutip.control.dynamics:phased dynamics generator caching True\n", - "INFO:qutip.control.dynamics:propagator gradient caching True\n", - "INFO:qutip.control.dynamics:eigenvector adjoint caching True\n", - "INFO:qutip.control.dynamics:use sparse eigen decomp False\n", - "INFO:qutip.control.optimizer:Optimising pulse(s) using GRAPE with 'fmin_l_bfgs_b' method\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ + "\n", "Final amplitudes output to file: ctrl_amps_final_QFT_n_ts20_ptypeLIN.txt\n", "\n", "------------------------------------\n", "---- Control optimisation stats ----\n", "**** Timings (HH:MM:SS.US) ****\n", - "Total wall time elapsed during optimisation: 0:00:00.390896\n", - "Wall time computing Hamiltonians: 0:00:00.023207 (5.94%)\n", - "Wall time computing propagators: 0:00:00.279448 (71.49%)\n", - "Wall time computing forward propagation: 0:00:00.003514 (0.90%)\n", - "Wall time computing onward propagation: 0:00:00.003310 (0.85%)\n", - "Wall time computing gradient: 0:00:00.068349 (17.49%)\n", + "Total wall time elapsed during optimisation: 0:00:00.373056\n", + "Wall time computing Hamiltonians: 0:00:00.022471 (6.02%)\n", + "Wall time computing propagators: 0:00:00.286680 (76.85%)\n", + "Wall time computing forward propagation: 0:00:00.002560 (0.69%)\n", + "Wall time computing onward propagation: 0:00:00.002160 (0.58%)\n", + "Wall time computing gradient: 0:00:00.043716 (11.72%)\n", "\n", "**** Iterations and function calls ****\n", "Number of iterations: 29\n", @@ -430,7 +376,7 @@ "Mean number of amplitude changes per update: 239.71428571428572\n", "------------------------------------\n", "Final evolution\n", - "Quantum object: dims = [[4], [4]], shape = (4, 4), type = oper, isherm = False\n", + "Quantum object: dims=[[4], [4]], shape=(4, 4), type='oper', dtype=Dense, isherm=False\n", "Qobj data =\n", "[[-0.18922387+0.46120317j -0.19352353+0.46212201j -0.19278561+0.46360969j\n", " -0.18771204+0.46167283j]\n", @@ -442,11 +388,11 @@ " -0.46156574-0.19135913j]]\n", "\n", "********* Summary *****************\n", - "Final fidelity error 9.093450580754947e-06\n", - "Final gradient normal 0.00021946915669970356\n", + "Final fidelity error 9.09345058086597e-06\n", + "Final gradient normal 0.00021946915681973\n", "Terminated due to Goal achieved\n", "Number of iterations 29\n", - "Completed in 0:00:00.390896 HH:MM:SS.US\n", + "Completed in 0:00:00.373056 HH:MM:SS.US\n", "Initial amplitudes output to file: ctrl_amps_initial_QFT_n_ts20_ptypeLIN.txt\n", "***********************************\n", "\n", @@ -459,12 +405,12 @@ "------------------------------------\n", "---- Control optimisation stats ----\n", "**** Timings (HH:MM:SS.US) ****\n", - "Total wall time elapsed during optimisation: 0:00:00.650126\n", - "Wall time computing Hamiltonians: 0:00:00.041716 (6.42%)\n", - "Wall time computing propagators: 0:00:00.468142 (72.01%)\n", - "Wall time computing forward propagation: 0:00:00.005879 (0.90%)\n", - "Wall time computing onward propagation: 0:00:00.005721 (0.88%)\n", - "Wall time computing gradient: 0:00:00.115748 (17.80%)\n", + "Total wall time elapsed during optimisation: 0:00:00.634567\n", + "Wall time computing Hamiltonians: 0:00:00.038307 (6.04%)\n", + "Wall time computing propagators: 0:00:00.495125 (78.03%)\n", + "Wall time computing forward propagation: 0:00:00.004598 (0.72%)\n", + "Wall time computing onward propagation: 0:00:00.003715 (0.59%)\n", + "Wall time computing gradient: 0:00:00.072676 (11.45%)\n", "\n", "**** Iterations and function calls ****\n", "Number of iterations: 24\n", @@ -483,9 +429,9 @@ "Mean number of amplitude changes per update: 480.0\n", "------------------------------------\n", "Final evolution\n", - "Quantum object: dims = [[4], [4]], shape = (4, 4), type = oper, isherm = False\n", + "Quantum object: dims=[[4], [4]], shape=(4, 4), type='oper', dtype=Dense, isherm=False\n", "Qobj data =\n", - "[[ 0.46207580+0.19122715j 0.46151636+0.19126417j 0.46241052+0.19094127j\n", + "[[ 0.4620758 +0.19122715j 0.46151636+0.19126417j 0.46241052+0.19094127j\n", " 0.46213112+0.19102767j]\n", " [ 0.46199149+0.19151454j -0.19197958+0.46204899j -0.46180318-0.19115039j\n", " 0.19122368-0.46170712j]\n", @@ -495,11 +441,11 @@ " -0.19139753+0.46188943j]]\n", "\n", "********* Summary *****************\n", - "Final fidelity error 2.4336489157228414e-07\n", - "Final gradient normal 0.0006194193096196838\n", + "Final fidelity error 2.433648946809086e-07\n", + "Final gradient normal 0.0006194193096311807\n", "Terminated due to Goal achieved\n", "Number of iterations 24\n", - "Completed in 0:00:00.650126 HH:MM:SS.US\n" + "Completed in 0:00:00.634567 HH:MM:SS.US\n" ] } ], @@ -582,16 +528,16 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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jkjZLeibNm5vZmZIukzRN0r+4+6WR9635/hJJ+yT9Ic01ACRBfKmRlJpjhEVn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SoftwareVersion
QuTiP4.1.0
Numpy1.11.3
SciPy0.18.1
matplotlib2.0.0
Cython0.25.2
Number of CPUs4
BLAS InfoINTEL MKL
IPython5.1.0
Python3.6.0 |Anaconda 4.3.1 (64-bit)| (default, Dec 23 2016, 12:22:00) \n", - "[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]
OSposix [linux]
Fri Jul 14 17:13:52 2017 BST
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SoftwareVersion
QuTiP5.1.0.dev0+0b4260e
Numpy1.26.4
SciPy1.13.0
matplotlib3.9.0
Number of CPUs8
BLAS InfoGeneric
IPython8.25.0
Python3.12.3 | packaged by Anaconda, Inc. | (main, May 6 2024, 19:46:43) [GCC 11.2.0]
OSposix [linux]
Cython3.0.10
Wed Jan 01 22:48:30 2025 IST
" ], "text/plain": [ "" ] }, - "execution_count": 11, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -664,6 +609,13 @@ "\n", "version_table()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -682,7 +634,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.12.3" } }, "nbformat": 4, diff --git a/examples/control-pulseoptim-symplectic.ipynb b/examples/control-pulseoptim-symplectic.ipynb index 7a4cfed..099b21a 100644 --- a/examples/control-pulseoptim-symplectic.ipynb +++ b/examples/control-pulseoptim-symplectic.ipynb @@ -57,14 +57,11 @@ "outputs": [], "source": [ "from qutip import Qobj, identity, sigmax, sigmay, sigmaz, tensor\n", - "from qutip.qip import hadamard_transform\n", - "import qutip.logging_utils as logging\n", - "logger = logging.get_logger()\n", - "#Set this to None or logging.WARN for 'quiet' execution\n", - "log_level = logging.INFO\n", + "from qutip_qip.operations import hadamard_transform\n", + "\n", "#QuTiP control modules\n", - "import qutip.control.pulseoptim as cpo\n", - "import qutip.control.symplectic as sympl\n", + "import qutip_qtrl.pulseoptim as cpo\n", + "import qutip_qtrl.symplectic as sympl\n", "\n", "example_name = 'Symplectic'" ] @@ -211,52 +208,7 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:qutip.control.dynamics:Setting memory optimisations for level 0\n", - "INFO:qutip.control.dynamics:Internal operator data type choosen to be \n", - "INFO:qutip.control.dynamics:phased dynamics generator caching True\n", - "INFO:qutip.control.dynamics:propagator gradient caching True\n", - "INFO:qutip.control.dynamics:eigenvector adjoint caching True\n", - "INFO:qutip.control.dynamics:use sparse eigen decomp False\n", - "INFO:qutip.control.pulseoptim:System configuration:\n", - "Drift dynamics generator:\n", - "Quantum object: dims = [[4], [4]], shape = (4, 4), type = oper, isherm = True\n", - "Qobj data =\n", - "[[ 1. 0. 0.5 0. ]\n", - " [ 0. 1. 0. 0.5]\n", - " [ 0.5 0. 1. 0. ]\n", - " [ 0. 0.5 0. 1. ]]\n", - "Control 1 dynamics generator:\n", - "Quantum object: dims = [[4], [4]], shape = (4, 4), type = oper, isherm = True\n", - "Qobj data =\n", - "[[ 1. 0. 0. 0.]\n", - " [ 0. 1. 0. 0.]\n", - " [ 0. 0. 0. 0.]\n", - " [ 0. 0. 0. 0.]]\n", - "Initial state / operator:\n", - "Quantum object: dims = [[4], [4]], shape = (4, 4), type = oper, isherm = True\n", - "Qobj data =\n", - "[[ 1. 0. 0. 0.]\n", - " [ 0. 1. 0. 0.]\n", - " [ 0. 0. 1. 0.]\n", - " [ 0. 0. 0. 1.]]\n", - "Target state / operator:\n", - "Quantum object: dims = [[4], [4]], shape = (4, 4), type = oper, isherm = False\n", - "Qobj data =\n", - "[[ 0.54030231 0. 0. 0.84147098]\n", - " [ 0. 0.54030231 -0.84147098 0. ]\n", - " [ 0. 0.84147098 0.54030231 0. ]\n", - " [-0.84147098 0. 0. 0.54030231]]\n", - "INFO:qutip.control.pulseoptim:Initial amplitudes output to file: ctrl_amps_initial_Symplectic_n_ts1000_ptypeZERO.txt\n", - "INFO:qutip.control.optimizer:Optimising pulse(s) using GRAPE with 'fmin_l_bfgs_b' method\n", - "INFO:qutip.control.pulseoptim:Final amplitudes output to file: ctrl_amps_final_Symplectic_n_ts1000_ptypeZERO.txt\n" - ] - } - ], + "outputs": [], "source": [ "# Note that this call uses\n", "# dyn_type='SYMPL'\n", @@ -275,7 +227,7 @@ " max_iter=max_iter, max_wall_time=max_wall_time, \n", " dyn_type='SYMPL', \n", " out_file_ext=f_ext, init_pulse_type=p_type, \n", - " log_level=log_level, gen_stats=True)\n" + " gen_stats=True)\n" ] }, { @@ -300,12 +252,12 @@ "------------------------------------\n", "---- Control optimisation stats ----\n", "**** Timings (HH:MM:SS.US) ****\n", - "Total wall time elapsed during optimisation: 0:00:02.998303\n", - "Wall time computing Hamiltonians: 0:00:00.112098 (3.74%)\n", - "Wall time computing propagators: 0:00:02.615654 (87.24%)\n", - "Wall time computing forward propagation: 0:00:00.022306 (0.74%)\n", - "Wall time computing onward propagation: 0:00:00.019775 (0.66%)\n", - "Wall time computing gradient: 0:00:00.222838 (7.43%)\n", + "Total wall time elapsed during optimisation: 0:00:01.655505\n", + "Wall time computing Hamiltonians: 0:00:00.067677 (4.09%)\n", + "Wall time computing propagators: 0:00:01.383888 (83.59%)\n", + "Wall time computing forward propagation: 0:00:00.015537 (0.94%)\n", + "Wall time computing onward propagation: 0:00:00.013701 (0.83%)\n", + "Wall time computing gradient: 0:00:00.142473 (8.61%)\n", "\n", "**** Iterations and function calls ****\n", "Number of iterations: 8\n", @@ -324,19 +276,19 @@ "Mean number of amplitude changes per update: 1000.0\n", "------------------------------------\n", "Final evolution\n", - "Quantum object: dims = [[4], [4]], shape = (4, 4), type = oper, isherm = False\n", + "Quantum object: dims=[[4], [4]], shape=(4, 4), type='oper', dtype=Dense, isherm=False\n", "Qobj data =\n", - "[[ 5.40298994e-01 -1.56965600e-06 -7.01444121e-06 8.41473111e-01]\n", - " [ 1.56965600e-06 5.40298994e-01 -8.41473111e-01 -7.01444122e-06]\n", - " [ -7.01444163e-06 8.41473111e-01 5.40298994e-01 1.05774201e-05]\n", - " [ -8.41473111e-01 -7.01444163e-06 -1.05774201e-05 5.40298994e-01]]\n", + "[[ 5.40298994e-01 -1.56965600e-06 -7.01444086e-06 8.41473111e-01]\n", + " [ 1.56965600e-06 5.40298994e-01 -8.41473111e-01 -7.01444087e-06]\n", + " [-7.01444198e-06 8.41473111e-01 5.40298994e-01 1.05774201e-05]\n", + " [-8.41473111e-01 -7.01444198e-06 -1.05774201e-05 5.40298994e-01]]\n", "\n", "********* Summary *****************\n", - "Final fidelity error 6.093073931117445e-11\n", - "Final gradient normal 8.705152868582485e-06\n", + "Final fidelity error 6.093073931039467e-11\n", + "Final gradient normal 8.70515286864752e-06\n", "Terminated due to Goal achieved\n", "Number of iterations 8\n", - "Completed in 0:00:02.998303 HH:MM:SS.US\n" + "Completed in 0:00:01.655505 HH:MM:SS.US\n" ] } ], @@ -360,16 +312,16 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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2+Lw847Z3pvPkZ3MLtlWvksPkm39F1cr+/LRzbvttzc2jx9CPWL5m28hi53Tf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\nZmsI/jA1jTeq9JLUDDgWeDzuWOIgqS5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+ "image/png": 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", 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