WrightSim.hamiltonian package¶
Submodules¶
WrightSim.hamiltonian.TRSF_default module¶
WrightSim.hamiltonian.default module¶
- class WrightSim.hamiltonian.default.Hamiltonian(rho=None, tau=None, mu=None, omega=None, w_central=7000.0, coupling=0, propagator=None, phase_cycle=False, labels=['00', '01 -2', "10 2'", '10 1', "20 1+2'", '11 1-2', "11 2'-2", "10 1-2+2'", "21 1-2+2'"], time_orderings=[1, 2, 3, 4, 5, 6], recorded_indices=[7, 8])[source]¶
Bases:
object- cuda_matrix_source = "\n /**\n * Hamiltonian_matrix: Computes the Hamiltonian matrix for an indevidual time step.\n * NOTE: This differs from the Python implementation, which computes the full time \n * dependant hamiltonian, this only computes for a single time step\n * (to conserve memory).\n * \n * Parameters\n * ----------\n * Hamiltonian ham: A struct which represents a hamiltonian,\n * containing orrays omega, mu, and Gamma\n * cmplx* efields: A pointer to an array containg the complex valued\n * electric fields to use for evaluation\n * double time: the current time step counter\n *\n * Output\n * -------\n * cmplx* out: an N x N matrix containing the transition probabilities\n *\n */\n __device__ void Hamiltonian_matrix(Hamiltonian ham, pycuda::complex<double>* efields,\n double time, pycuda::complex<double>* out)\n {\n // Define state energies\n double wag = ham.omega[1];\n double w2aa = ham.omega[8];\n\n // Define dipoles\n //TODO: don't assume one, generalize\n pycuda::complex<double> mu_ag = 1.;//ham.mu[0];\n pycuda::complex<double> mu_2aa = 1.;//ham.mu[1];\n\n // Define the electric field values\n pycuda::complex<double> E1 = efields[0];\n pycuda::complex<double> E2 = efields[1];\n pycuda::complex<double> E3 = efields[2];\n\n // Define helpful variables\n pycuda::complex<double> A_1 = 0.5 * I * mu_ag * E1 * pycuda::exp(-1. * I * wag * time);\n pycuda::complex<double> A_2 = 0.5 * I * mu_ag * E2 * pycuda::exp(I * wag * time);\n pycuda::complex<double> A_2prime = 0.5 * I * mu_ag * E3 * pycuda::exp(-1. * I * wag * time);\n pycuda::complex<double> B_1 = 0.5 * I * mu_2aa * E1 * pycuda::exp(-1. * I * w2aa * time);\n pycuda::complex<double> B_2 = 0.5 * I * mu_2aa * E2 * pycuda::exp(I * w2aa * time);\n pycuda::complex<double> B_2prime = 0.5 * I * mu_2aa * E3 * pycuda::exp(-1. * I * w2aa * time);\n\n //TODO: zero once, take this loop out of the inner most loop\n for (int i=0; i<ham.nStates * ham.nStates; i++) out[i] = pycuda::complex<double>();\n\n // Fill in appropriate matrix elements\n if(ham.time_orderings[2] || ham.time_orderings[4])\n out[1*ham.nStates + 0] = -1. * A_2;\n if(ham.time_orderings[3] || ham.time_orderings[5])\n out[2*ham.nStates + 0] = A_2prime;\n if(ham.time_orderings[0] || ham.time_orderings[1])\n out[3*ham.nStates + 0] = A_1;\n if(ham.time_orderings[2])\n out[5*ham.nStates + 1] = A_1;\n if(ham.time_orderings[4])\n out[6*ham.nStates + 1] = A_2prime;\n if(ham.time_orderings[3])\n out[4*ham.nStates + 2] = B_1;\n if(ham.time_orderings[5])\n out[6*ham.nStates + 2] = -1. * A_2;\n if(ham.time_orderings[0])\n out[4*ham.nStates + 3] = B_2prime;\n if(ham.time_orderings[1])\n out[5*ham.nStates + 3] = -1. * A_2;\n if(ham.time_orderings[1] || ham.time_orderings[3])\n {\n out[7*ham.nStates + 4] = B_2;\n out[8*ham.nStates + 4] = -1. * A_2;\n }\n if(ham.time_orderings[0] || ham.time_orderings[2])\n {\n out[7*ham.nStates + 5] = -2. * A_2prime;\n out[8*ham.nStates + 5] = B_2prime;\n }\n if(ham.time_orderings[4] || ham.time_orderings[5])\n {\n out[7*ham.nStates + 6] = -2. * A_1;\n out[8*ham.nStates + 6] = B_1;\n }\n\n // Put Gamma along the diagonal\n for(int i=0; i<ham.nStates; i++) out[i*ham.nStates + i] = -1. * ham.Gamma[i];\n }\n"¶
- cuda_mem_size = 64¶
- cuda_struct = '\n #include <pycuda-complex.hpp>\n #define I pycuda::complex<double>(0,1)\n\n struct Hamiltonian {\n int nStates;\n int nMu;\n int nTimeOrderings;\n int nRecorded;\n pycuda::complex<double>* rho;\n pycuda::complex<double>* mu;\n double* omega;\n double* Gamma;\n\n char* time_orderings;\n int* recorded_indices;\n };\n '¶
- matrix(efields, time)[source]¶
Generate the time dependant Hamiltonian Coupling Matrix.
- Parameters
efields (ndarray<Complex>) – Contains the time dependent electric fields. Shape (M x T) where M is number of electric fields, and T is number of timesteps.
time (1-D array <float64>) – The time step values
- Returns
Shape T x N x N array with the full Hamiltonian at each time step. N is the number of states in the Density vector.
- Return type
ndarray <Complex>