Benchmarking Quantum Algorithms with Neural Networks.

Nozomu Kobayashi, Yoshiyuki Suimon, Koichi Miyamoto +1 more

Abstract In this paper, we investigate the application of quantum and quantum-inspired machine learning algorithms to stock return predictions. Specifically, we evaluate the performance of quantum neural network, an algorithm suited for noisy intermediate-scale quantum computers, and tensor network, a quantum-inspired machine learning algorithm, against classical models such as linear regression and neural networks. To evaluate their abilities, we construct portfolios based on their predictions and measure investment performances. The empirical study on the Japanese stock market shows the tensor network model achieves superior performance compared to classical benchmark models, including linear and neural network models. Though the quantum neural network model attains the lowered risk-adjusted excess return than the classical neural network models over the whole period, both the quantum neural network and tensor network models have superior performances in the latest market environment, which suggests capability of model’s capturing non-linearity between input features.

Sanjay Gupta, Royce K. P. Zia

Davide Pastorello

Om Prakash Patel, Aruna Tiwari

In this paper a quantum based binary neural network algorithm is proposed, named as Advance Quantum based Binary Neural Network Learning Algorithm (AQ-BNN). It forms neural network structure constructively by adding neurons at hidden layer. The connection weights and separability parameter are decided using quantum computing concept. Constructive way of deciding network not only eliminates over-fitting and underfitting problem but also saves time. The connection weights have been decided by quantum way, it gives large space to select optimal weights. A new parameter that is quantum separability is introduced here which find optimal separability plane to classify input sample in quantum way. For each connection weights it searches for optimal separability plane. Thus the best separability plane is found out with respect to connection weights. This algorithm is tested with three benchmark data set and produces improved results than existing quantum inspired and other classification approaches.

Panchi Li, Hong Yu Xiao

To enhance the approximation and generalization ability of classical artificial neural network (ANN) by employing the principles of quantum computation, a quantum-inspired neuron based on controlled-rotation gate is proposed. In the proposed model, the discrete sequence input is represented by the qubits, which, as the control qubits of the controlled-rotation gate after being rotated by the quantum rotation gates, control the target qubit for rotation. The model output is described by the probability amplitude of state |1〉 in the target qubit. Then a quantum-inspired neural network with sequence input (QNNSI) is designed by employing the quantum-inspired neurons to the hidden layer and the classical neurons to the output layer. An algorithm of QNNSI is derived by employing the Levenberg–Marquardt algorithm. Experimental results of some benchmark problems show that, under a certain condition, the QNNSI is obviously superior to the ANN.

Yuxuan Du, Xinbiao Wang, Naixu Guo +6 more