What IBM’s result means for quantum computing

Hasan Yetış, Mehmet Karaköse

Today, all the implemented quantum computers are in Noisy Intermediate-Scale Quantum (NISQ) level. In such quantum computers, when circuit length and size increase, the results become less reliable because of the increasing effect of noise. The noise is an important factor that should be handled in NISQ level quantum computers. In this study, we investigate the noise factor on 5 qubit IBM-Q computers for basic circuits. For this purpose, existing 5 qubit IBM-Q computers with different architectures are examined. Then quantum circuit equivalents corresponding to basic logic gates such as XOR, AND, and OR are presented. Quantum circuits created for XOR, AND, and OR are run 10 times on different quantum computers of 5 qubits named Santiago, Athens, Valencia, Vigo, Ourance, and IBMQX2. Statistical information such as std., mean is obtained from the results. The consistency of the results obtained and their difference from the optimum result are discussed. As a result of the study, there is no contradiction in the results obtained with quantum volume. Studies show that even small growths in XOR, AND, and OR circuits can lead to a big deflection in results.

Jeff Kabachinski

Kornélia Sára Szatmáry

This paper explores the transformative capabilities of quantum computers in optimizing complex tasks. Specifically, it examines the runtime efficiency of a Python program performing k-means clustering on two quantum computer variants: D-Wave annealing and IBM universal. The primary objective is to evaluate their effectiveness in light of their distinct operational principles. Quantum computing offers unparalleled potential for tackling computationally demanding tasks, marking a substantial leap beyond conventional Neumann-based architectures. By scrutinizing the runtime performance of k-means clustering on these quantum platforms, I aim to discern their comparative advantages and limitations. This investigation sheds light on the practical implications of leveraging different types of quantum computers for optimization tasks, informing future advancements in quantum computing research and application development.

Anas N. Al‐Rabadi

Kimleang Kea, Dongmin Kim, Chansreynich Huot +2 more

The stock markets have become a popular topic within machine learning (ML) communities, with one particular application being stock price prediction. However, accurately predicting the stock market is a challenging task due to the various factors within financial markets. With the introduction of ML, prediction techniques have become more efficient but computationally demanding for classical computers. Given the rise of quantum computing (QC), which holds great promise for being exponentially faster than current classical computers, it is natural to explore ML within the QC domain. In this study, we leverage a hybrid quantum-classical ML approach to predict a company's stock price. We integrate classical long short-term memory (LSTM) with QC, resulting in a new variant called QLSTM. We initially validate the proposed QLSTM model by leveraging an IBM quantum simulator running on a classical computer, after which we conduct predictions using an IBM real quantum computer. Thereafter, we evaluate the performance of our model using the root mean square error (RMSE) and prediction accuracy. Additionally, we perform a comparative analysis, evaluating the prediction performance of the QLSTM model against several other classical models. Further, we explore the impacts of hyperparameters on the QLSTM model to determine the best configuration. Our experimental results demonstrate that while the classical LSTM model achieved an RMSE of 0.0693 and a prediction accuracy of 0.8815, the QLSTM model exhibited superior performance, achieving values of 0.0602 and 0.9736, respectively. Furthermore, the QLSTM outperformed other classical models in both metrics.

Jeremy J. Ramsden