+ -.. raw:: html - -
- -.. raw:: html - -- -A PyTorch Library for Quantum Simulation and Quantum Machine Learning - -.. raw:: html - -
- -.. raw:: html - - - -.. raw:: html - -- -Faster, Scalable, Easy Debugging, Easy Deployment on Real Machine - -.. raw:: html - -
- -.. raw:: html - - - -|MIT License| |Read the Docs| |Discourse status| |Website| 👋 Welcome ========== -What it is doing -^^^^^^^^^^^^^^^^ +What it does +^^^^^^^^^^^^ Quantum simulation framework based on PyTorch. It supports statevector simulation and pulse simulation (coming soon) on GPUs. It can scale up -to the simulation of 30+ qubits with multiple GPUs. #### Who will -benefit +to the simulation of 30+ qubits with multiple GPUs. + +Who will benefit +^^^^^^^^^^^^^^^^ Researchers on quantum algorithm design, parameterized quantum circuit training, quantum optimal control, quantum machine learning, quantum -neural networks. #### Differences from Qiskit/Pennylane +neural networks. + +Differences from Qiskit/Pennylane +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Dynamic computation graph, automatic gradient computation, fast GPU support, batch model tersorized processing. diff --git a/examples/qaoa/max_cut_paramshift.py b/examples/qaoa/max_cut_paramshift.py index c9046f69..c0f1f3ea 100644 --- a/examples/qaoa/max_cut_paramshift.py +++ b/examples/qaoa/max_cut_paramshift.py @@ -1,13 +1,9 @@ import torch import torchquantum as tq -import torchquantum.functional as tqf import random import numpy as np -from torchquantum.functional import mat_dict - -from torchquantum.plugins import tq2qiskit, qiskit2tq from torchquantum.measurement import expval_joint_analytical seed = 0 @@ -15,6 +11,9 @@ np.random.seed(seed) torch.manual_seed(seed) +from torchquantum.plugins import QiskitProcessor, op_history2qiskit + + class MAXCUT(tq.QuantumModule): """computes the optimal cut for a given graph. @@ -125,7 +124,7 @@ def circuit(self, qdev): self.mixer(qdev, self.betas[i], i) self.entangler(qdev, self.gammas[i], i) - def forward(self, measure_all=False): + def forward(self, use_qiskit): """ Apply the QAOA ansatz and only measure the edge qubit on z-basis. Args: @@ -133,42 +132,37 @@ def forward(self, measure_all=False): """ qdev = tq.QuantumDevice(n_wires=self.n_wires, device=self.betas.device) - self.circuit(qdev) # print(tq.measure(qdev, n_shots=1024)) # compute the expectation value # print(qdev.get_states_1d()) - if measure_all is False: + + if not use_qiskit: + self.circuit(qdev) expVal = 0 for edge in self.input_graph: pauli_string = self.edge_to_PauliString(edge) expv = expval_joint_analytical(qdev, observable=pauli_string) expVal += 0.5 * expv - # print(pauli_string, expv) - # print(expVal) - return expVal else: - return tq.measure(qdev, n_shots=1024, draw_id=0) + # use qiskit to compute the expectation value + expVal = 0 + for edge in self.input_graph: + pauli_string = self.edge_to_PauliString(edge) + with torch.no_grad(): + self.circuit(qdev) + circ = op_history2qiskit(qdev.n_wires, qdev.op_history) -def main(): - # create a input_graph - input_graph = [(0, 1), (0, 3), (1, 2), (2, 3)] - n_wires = 4 - n_layers = 3 - model = MAXCUT(n_wires=n_wires, input_graph=input_graph, n_layers=n_layers) - # model.to("cuda") - # model.to(torch.device("cuda")) - # circ = tq2qiskit(tq.QuantumDevice(n_wires=4), model) - # print(circ) - # print("The circuit is", circ.draw(output="mpl")) - # circ.draw(output="mpl") - # use backprop - # backprop_optimize(model, n_steps=300, lr=0.01) - # use parameter shift rule - param_shift_optimize(model, n_steps=500, step_size=0.01) + expv = self.qiskit_processor.process_circs_get_joint_expval([circ], pauli_string)[0] + expVal += 0.5 * expv + expVal = torch.Tensor([expVal]) + return expVal + -def shift_and_run(model, use_qiskit=False): + + +def shift_and_run(model, use_qiskit): # flatten the parameters into 1D array grad_betas = [] @@ -207,7 +201,7 @@ def shift_and_run(model, use_qiskit=False): return model(use_qiskit), [grad_betas, grad_gammas] -def param_shift_optimize(model, n_steps=10, step_size=0.1): +def param_shift_optimize(model, n_steps=10, step_size=0.1, use_qiskit=False): """finds the optimal cut where parameter shift rule is used to compute the gradient""" # optimize the parameters and return the optimal values # print( @@ -218,7 +212,7 @@ def param_shift_optimize(model, n_steps=10, step_size=0.1): n_layers = model.n_layers for step in range(n_steps): with torch.no_grad(): - loss, grad_list = shift_and_run(model) + loss, grad_list = shift_and_run(model, use_qiskit=use_qiskit) # param_list = list(model.parameters()) # print( # "The initial parameters are betas = {} and gammas = {}".format( @@ -257,8 +251,33 @@ def param_shift_optimize(model, n_steps=10, step_size=0.1): """ + +def main(use_qiskit): + # create a input_graph + input_graph = [(0, 1), (0, 3), (1, 2), (2, 3)] + n_wires = 4 + n_layers = 1 + model = MAXCUT(n_wires=n_wires, input_graph=input_graph, n_layers=n_layers) + + # set the qiskit processor + processor_simulation = QiskitProcessor(use_real_qc=False, n_shots=10000) + model.set_qiskit_processor(processor_simulation) + + # firstly perform simulate + # model.to("cuda") + # model.to(torch.device("cuda")) + # circ = tq2qiskit(tq.QuantumDevice(n_wires=4), model) + # print(circ) + # print("The circuit is", circ.draw(output="mpl")) + # circ.draw(output="mpl") + # use backprop + # backprop_optimize(model, n_steps=300, lr=0.01) + # use parameter shift rule + param_shift_optimize(model, n_steps=500, step_size=0.01, use_qiskit=use_qiskit) + + if __name__ == "__main__": # import pdb # pdb.set_trace() - - main() + use_qiskit = False + main(use_qiskit)