Development

include/clara.h

@@ -36,6 +36,8 @@
 #include <utility>
 #include <vector>
 
+#include <Eigen/Dense>
+#include <Eigen/SVD>
 // inter dependicies
 #include "classFunction/codes.h"
 #include "classFunction/exception.h"

testing/_test.cpp

@@ -6,7 +6,6 @@
 #include <random>
 #include <vector>
 
-#include "../include/clara.h"
 #include "../include/experimental/experimental_test.h"
 
 /**
@@ -18,101 +17,14 @@
  */
 
 using namespace clara;
+using namespace::experimental;
 int main() {
-  std::cout << "testing testing \n";
-
-  // initialize bit circuit with 70 bits
-  const idx bits = 70;
-  experimental::Bit_circuit b{bits};
-
-  const idx trials = 20;
-
-  // number of trials
-  b.rand();
-  auto c = b;
-
-  /**
-   * random number generator `gen` is set up using
-   * random_device for generating random indices. for each trial
-   * vector `v` is filled wuth values from `0` to `bits-1`. the element
-   * of `v` represent the indices of the `TOF` (Toffoli) operation. these indices
-   * are store for later use in trials
-   */
-  std::random_device rd;
-  std::mt19937 gen{rd()};
-  std::vector<std::vector<idx>> indices(trials);
-
-  for (idx i = 0; i < trials; ++i) {
-    std::vector<idx> v(bits);
-    std::iota(v.begin(), v.end(), 0);
-    std::shuffle(v.begin(), v.end(), gen);
-    std::vector<idx> tof(v.data(), v.data() + 3);
-    indices[i] = tof;
-  }
-
-  /**
-   * the first half of the trials is performed. the indices for each trials are
-   * printed, and the `TOF` operations is applied to the `Bit_circuit` using
-   * corresponding indices
-   */
-  for (idx i = 0; i < trials; ++i) {
-    std::cout << "first: ";
-    for (auto&& elem : indices[i])
-      std::cout << elem << " ";
-    std::cout << std::endl;
-    b.TOF(indices[i]);
-  }
-
-  /**
-   * the second half of the trials performed in reverse order. the indices for each
-   * trial are again printed, and the `TOF` operation is applied to the `Bit_circuit`
-   * using the corresponding indices
-   */
-  for (idx i = trials; i-- > 0;) {
-    std::cout << "second: ";
-    for (auto&& elem : indices[i])
-      std::cout << elem << " ";
-    std::cout << std::endl;
-    b.TOF(indices[i]);
-  }
-
-  /**
-   * the initail and final state are printed. the hamming weight (number of set bits)
-   * of `b` is displayed. the count of NOT, X and TOF gates used in `b` is printed
-   */
-  std::cout << "initial: " << b << std::endl;
-  std::cout << "final: " << c << std::endl;
-  std::cout << "hamming weight: " << b.count() << std::endl;
-
-  std::cout << b.gate_count.NOT << " " << b.gate_count.X << " " << b.gate_count.TOF << std::endl;
-  std::cout << (b == c) << std::endl;
-  std::cout << (b != c) << std::endl;
-
-  /**
-   * various quantum states and projections are created and printed
-   */
-  experimental::Dynamic_bitset bb(9);
-  bb.set(1).set(3).set(8);
-  std::cout << bb << std::endl;
-
-  std::cout << "info: " << std::endl;
-  std::cout << bb.to_string('o', 'X') << std::endl;
-
-  experimental::Dynamic_bitset vlad(20);
-  std::cout << vlad << std::endl;
-
-  std::vector<unsigned int> vv(20);
-  for (auto& elem : vv) {
-    std::cout << elem;
-  }
-  std::cout << std::endl;
-
-  ket x = (10_ket + 01_ket) / std::sqrt(2);
-  std::cout << disp(x) << std::endl;
-
-  bra y = (10_bra + 01_bra) / std::sqrt(2);
-  std::cout << disp(x) << std::endl;
-
-  cmat z = 110_prj;
-  std::cout << disp(z) << std::endl;
+  ClaraCircuit<int> claraCircuit(10, 10);
+  claraCircuit.apply_all(gt.H);
+  std::cout << claraCircuit.get_num_active_qubits() << std::endl;
+
+  claraCircuit.measure({3, 1, 7});
+  std::cout << claraCircuit.get_num_active_qubits() << std::endl;
+  // std::cout << claraCircuit.get_num_measured_qubits() << std::endl;
+  // std::cout << claraCircuit.get_num_active_qubits() << std::endl;
 }

testing/channels.cpp

@@ -42,27 +42,27 @@ int main() {
 
   std::cout << "their sum is: " << sum(hevals(choim)) << "\n";
 
-  std::vector<cmat> Kperps = choi2kraus(choim);
-  std::cout << "kraus rank of channel : " << Kperps.size() << "\n";
-
-  cmat rho_out1 = clara::apply(rho_in, Kperps);
-  // verification norm difference
-  std::cout << "norm difference ouput states: " << norm(rho_out1 - rho_out) << "\n";
-
-  std::cout << "superoperator matrix:\n";
-  cmat smat = kraus2super(Ks);
-  std::cout << disp(smat) << "\n";
-
-  std::cout << "the eigenvalues of the superoperator matrix :\n";
-  dyn_col_vect<cplx> evalsupop = evals(smat);
-  std::cout << disp(transpose(evalsupop)) << "\n";
-
-  std::cout << "their absolute values are: \n";
-  for (idx i = 0; i < (idx)evalsupop.size(); ++i)
-    std::cout << std::abs(evalsupop(i)) << " ";
-
-  // verification
-  std::cout << "\n norm difference for the superoperator action: ";
-  cmat rho_out2 = transpose(reshape(smat * reshape(transpose(rho_in), D * D, 1), D, D));
-  std::cout << norm(rho_out - rho_out2) << "\n";
+//   std::vector<cmat> Kperps = choi2kraus(choim);
+//   std::cout << "kraus rank of channel : " << Kperps.size() << "\n";
+//
+//   cmat rho_out1 = clara::apply(rho_in, Kperps);
+//   // verification norm difference
+//   std::cout << "norm difference ouput states: " << norm(rho_out1 - rho_out) << "\n";
+//
+//   std::cout << "superoperator matrix:\n";
+//   cmat smat = kraus2super(Ks);
+//   std::cout << disp(smat) << "\n";
+//
+//   std::cout << "the eigenvalues of the superoperator matrix :\n";
+//   dyn_col_vect<cplx> evalsupop = evals(smat);
+//   std::cout << disp(transpose(evalsupop)) << "\n";
+//
+//   std::cout << "their absolute values are: \n";
+//   for (idx i = 0; i < (idx)evalsupop.size(); ++i)
+//     std::cout << std::abs(evalsupop(i)) << " ";
+//
+//   // verification
+//   std::cout << "\n norm difference for the superoperator action: ";
+//   cmat rho_out2 = transpose(reshape(smat * reshape(transpose(rho_in), D * D, 1), D, D));
+//   std::cout << norm(rho_out - rho_out2) << "\n";
 }