VQE supports initialization by computer (#263)

* VQE supports initialization by computer

Author: JonathonMisiewicz

src/qforte/abc/algorithm.py

@@ -93,9 +93,31 @@ def __init__(
             self._refprep = build_refprep(self._ref)
             self._Uprep = reference
 
+        elif self._state_prep_type == "computer":
+            if not isinstance(reference, qf.Computer):
+                raise ValueError("computer reference must be a Computer.")
+            if not fast:
+                raise ValueError(
+                    "`self._fast = False` specifies not to skip steps, but `self._state_prep_type = computer` specifies to skip state initialization. That's inconsistent."
+                )
+            if reference.get_nqubit() != len(system.hf_reference):
+                raise ValueError(
+                    f"Computer needs {len(system.hf_reference)} qubits, found {reference.get_nqubit()}."
+                )
+            if (
+                not hasattr(self, "computer_initializable")
+                or not self.computer_initializable
+            ):
+                raise ValueError("Class cannot be initialized with a computer.")
+
+            self._ref = system.hf_reference
+            self._refprep = build_refprep(self._ref)
+            self._Uprep = qf.Circuit()
+            self.computer = reference
+
         else:
             raise ValueError(
-                "QForte only suppors references as occupation lists and Circuits."
+                "QForte only supports references as occupation lists, Circuits, or Computers."
             )
 
         self._nqb = len(self._ref)
@@ -284,21 +306,26 @@ def fill_pool(self):
             len(operator.jw_transform().terms()) for _, operator in self._pool_obj
         ]
 
-    def measure_energy(self, Ucirc):
+    def measure_energy(self, Ucirc, computer=None):
         """
         This function returns the energy expectation value of the state
-        Uprep|0>.
+        Ucirc|Ψ>.
 
         Parameters
         ----------
         Ucirc : Circuit
             The state preparation circuit.
         """
         if self._fast:
-            myQC = qforte.Computer(self._nqb)
-            myQC.apply_circuit(Ucirc)
-            val = np.real(myQC.direct_op_exp_val(self._qb_ham))
+            if computer is None:
+                computer = qf.Computer(self._nqb)
+            computer.apply_circuit(Ucirc)
+            val = np.real(computer.direct_op_exp_val(self._qb_ham))
         else:
+            if compute is not None:
+                raise TypeError(
+                    "measure_energy in slow mode does not support custom Computer."
+                )
             Exp = qforte.Experiment(self._nqb, Ucirc, self._qb_ham, 2000)
             val = Exp.perfect_experimental_avg()
 
@@ -431,8 +458,8 @@ def __init__(
     def energy_feval(self, params):
         """
         This function returns the energy expectation value of the state
-        Uprep(params)|0>, where params are parameters that can be optimized
-        for some purpouse such as energy minimizaiton.
+        Uprep(params)|Ψ>, where params are parameters that can be optimized
+        for some purpouse such as energy minimization.
 
         Parameters
         ----------
@@ -441,7 +468,13 @@ def energy_feval(self, params):
             the state preparation circuit.
         """
         Ucirc = self.build_Uvqc(amplitudes=params)
-        Energy = self.measure_energy(Ucirc)
+        Energy = self.measure_energy(Ucirc, self.get_initial_computer())
 
         self._curr_energy = Energy
         return Energy
+
+    def get_initial_computer(self) -> qf.Computer:
+        if hasattr(self, "computer"):
+            return qf.Computer(self.computer)
+        else:
+            return qf.Computer(self._nqb)

src/qforte/abc/uccvqeabc.py

@@ -26,7 +26,7 @@ class UCCVQE(UCC, VQE):
     .. math::
         E(\\mathbf{t}) = \\langle \\Phi_0 | \\hat{U}^\\dagger(\\mathbf{\\mathbf{t}}) \\hat{H} \\hat{U}(\\mathbf{\\mathbf{t}}) | \\Phi_0 \\rangle
 
-    using a disentagled UCC type ansatz
+    using a disentangled UCC type ansatz
 
     .. math::
         \\hat{U}(\\mathbf{t}) = \\prod_\\mu e^{t_\\mu (\\hat{\\tau}_\\mu - \\hat{\\tau}_\\mu^\\dagger)},
@@ -71,6 +71,7 @@ class UCCVQE(UCC, VQE):
     """
 
     def __init__(self, *args, **kwargs):
+        self.computer_initializable = True
         super().__init__(*args, **kwargs)
 
     @abstractmethod
@@ -104,7 +105,7 @@ def measure_operators(self, operators, Ucirc, idxs=[]):
         """
 
         if self._fast:
-            myQC = qforte.Computer(self._nqb)
+            myQC = self.get_initial_computer()
             myQC.apply_circuit(Ucirc)
             if not idxs:
                 grads = myQC.direct_oppl_exp_val(operators)
@@ -120,7 +121,8 @@ def measure_operators(self, operators, Ucirc, idxs=[]):
 
     def measure_gradient(self, params=None):
         """Returns the disentangled (factorized) UCC gradient, using a
-        recursive approach.
+        recursive approach, as described in Section D of the Appendix of
+        10.1038/s41467-019-10988-2
 
         Parameters
         ----------
@@ -140,18 +142,11 @@ def measure_gradient(self, params=None):
         else:
             Utot = self.build_Uvqc(params)
 
-        qc_psi = qforte.Computer(
-            self._nqb
-        )  # build | sig_N > according ADAPT-VQE analytical grad section
+        qc_psi = self.get_initial_computer()
         qc_psi.apply_circuit(Utot)
-        qc_sig = qforte.Computer(
-            self._nqb
-        )  # build | psi_N > according ADAPT-VQE analytical grad section
-        psi_i = copy.deepcopy(qc_psi.get_coeff_vec())
-        qc_sig.set_coeff_vec(
-            copy.deepcopy(psi_i)
-        )  # not sure if copy is faster or reapplication of state
+        qc_sig = qf.Computer(qc_psi)
         qc_sig.apply_operator(self._qb_ham)
+        qc_temp = qf.Computer(qc_psi)
 
         mu = M - 1
 
@@ -161,15 +156,13 @@ def measure_gradient(self, params=None):
         )
         Kmu_prev.mult_coeffs(self._pool_obj[self._tops[mu]][0])
 
-        qc_psi.apply_operator(Kmu_prev)
+        qc_temp.apply_operator(Kmu_prev)
         grads[mu] = 2.0 * np.real(
-            np.vdot(qc_sig.get_coeff_vec(), qc_psi.get_coeff_vec())
+            np.vdot(qc_sig.get_coeff_vec(), qc_temp.get_coeff_vec())
         )
 
-        # reset Kmu_prev |psi_i> -> |psi_i>
-        qc_psi.set_coeff_vec(copy.deepcopy(psi_i))
-
         for mu in reversed(range(M - 1)):
+            qc_temp = qf.Computer(qc_psi)
             # mu => N-1 => M-2
             # mu+1 => N => M-1
             # Kmu => KN-1
@@ -243,15 +236,14 @@ def measure_gradient(self, params=None):
 
             qc_sig.apply_circuit(Umu)
             qc_psi.apply_circuit(Umu)
-            psi_i = copy.deepcopy(qc_psi.get_coeff_vec())
+            qc_temp = qf.Computer(qc_psi)
 
-            qc_psi.apply_operator(Kmu)
+            qc_temp.apply_operator(Kmu)
             grads[mu] = 2.0 * np.real(
-                np.vdot(qc_sig.get_coeff_vec(), qc_psi.get_coeff_vec())
+                np.vdot(qc_sig.get_coeff_vec(), qc_temp.get_coeff_vec())
             )
 
             # reset Kmu |psi_i> -> |psi_i>
-            qc_psi.set_coeff_vec(copy.deepcopy(psi_i))
             Kmu_prev = Kmu
 
         np.testing.assert_allclose(np.imag(grads), np.zeros_like(grads), atol=1e-7)
@@ -269,25 +261,22 @@ def measure_gradient3(self):
             raise ValueError("self._fast must be True for gradient measurement.")
 
         Utot = self.build_Uvqc()
-        qc_psi = qforte.Computer(self._nqb)
+        qc_psi = self.get_initial_computer()
         qc_psi.apply_circuit(Utot)
-        psi_i = copy.deepcopy(qc_psi.get_coeff_vec())
 
-        qc_sig = qforte.Computer(self._nqb)
-        # TODO: Check if it's faster to recompute psi_i or copy it.
-        qc_sig.set_coeff_vec(copy.deepcopy(psi_i))
+        qc_sig = qforte.Computer(qc_psi)
         qc_sig.apply_operator(self._qb_ham)
 
         grads = np.zeros(len(self._pool_obj))
 
         for mu, (coeff, operator) in enumerate(self._pool_obj):
+            qc_temp = qf.Computer(qc_psi)
             Kmu = operator.jw_transform(self._qubit_excitations)
             Kmu.mult_coeffs(coeff)
-            qc_psi.apply_operator(Kmu)
+            qc_temp.apply_operator(Kmu)
             grads[mu] = 2.0 * np.real(
-                np.vdot(qc_sig.get_coeff_vec(), qc_psi.get_coeff_vec())
+                np.vdot(qc_sig.get_coeff_vec(), qc_temp.get_coeff_vec())
             )
-            qc_psi.set_coeff_vec(copy.deepcopy(psi_i))
 
         np.testing.assert_allclose(np.imag(grads), np.zeros_like(grads), atol=1e-7)
 

tests/test_computer_init.py

@@ -0,0 +1,46 @@
+import numpy as np
+from pytest import approx
+from qforte import ADAPTVQE, UCCNVQE
+from qforte import Circuit, Computer, gate, system_factory
+
+import os
+
+THIS_DIR = os.path.dirname(os.path.abspath(__file__))
+data_path = os.path.join(THIS_DIR, "H4-sto6g-075a.json")
+
+
+class TestComputerInit:
+    # @mark.skip(reason="long")
+    def test_H4_VQE(self):
+        mol = system_factory(
+            system_type="molecule",
+            build_type="external",
+            basis="sto-6g",
+            filename=data_path,
+        )
+        nqubits = len(mol.hf_reference)
+        fci_energy = -2.162897881184882
+
+        computer = Computer(nqubits)
+        coeff_vec = np.zeros(2**nqubits)
+        coeff_vec[int("00001111", 2)] = 1
+        coeff_vec[int("00110011", 2)] = 0.2
+        coeff_vec[int("00111100", 2)] = 0.1
+        coeff_vec[int("11001100", 2)] = 0.04
+        coeff_vec /= np.linalg.norm(coeff_vec)
+        computer.set_coeff_vec(coeff_vec)
+
+        # Analytic and fin dif gradients agree
+        analytic = UCCNVQE(mol, reference=computer, state_prep_type="computer")
+        analytic.run(use_analytic_grad=False, pool_type="SD")
+        findif = UCCNVQE(mol, reference=computer, state_prep_type="computer")
+        findif.run(use_analytic_grad=True, pool_type="SD")
+        assert analytic.get_gs_energy() == approx(findif.get_gs_energy(), abs=1.0e-8)
+
+        # Computer-based and non-compute based agree
+        hf = ADAPTVQE(mol)
+        hf.run(use_analytic_grad=True, pool_type="GSD", avqe_thresh=1e-5)
+        comp = ADAPTVQE(mol, reference=computer, state_prep_type="computer")
+        comp.run(use_analytic_grad=True, pool_type="GSD", avqe_thresh=1e-5)
+        assert hf.get_gs_energy() == approx(comp.get_gs_energy(), abs=1.0e-8)
+        assert hf.get_gs_energy() == approx(fci_energy, abs=1.0e-8)