Heart of quantum

Ciarán M. Lee

Huping Hu, Maoxin Wu

We report here our experimental findings of new nonlocal biological effect measured objectively and quantitatively under blind conditions. The method used includes the steps of providing two parts of quantum-entangled medium, applying one part to a biological system such as a human, contacting the other part with a desired substance such as a medication, and detecting change of a biological parameter with a detecting device. Using this method, we have found that after consumption by a test subject of one part of the quantum entangled water, the subject’s heart rate was non-locally increased under blind conditions by adding to the second part of the quantum-entangled water an over-the-counter medication Primatene which contains the heart stimulant ephedrine. The said increase of heart rate is measurable with a heart rate monitor, statistically significant and consistently reproducible. Key Words: quantum entanglement, biological non-local effect, heart rate, heart stimulant.

Hanif Heidari, Gerhard Hellstern, Murugappan, Murugappan

Heart disease morbidity and mortality rates are increasing, which has a negative impact on public health and the global economy. Early detection of heart disease reduces the incidence of heart mortality and morbidity. Recent research has utilized quantum computing methods to predict heart disease with more than 5 qubits and are computationally intensive. Despite the higher number of qubits, earlier work reports a lower accuracy in predicting heart disease, have not considered the outlier effects, and requires more computation time and memory for heart disease prediction. To overcome these limitations, we propose hybrid random forest quantum neural network (HQRF) using a few qubits (two to four) and considered the effects of outlier in the dataset. Two open-source datasets, Cleveland and Statlog, are used in this study to apply quantum networks. The proposed algorithm has been applied on two open-source datasets and utilized two different types of testing strategies such as 10-fold cross validation and 70-30 train/test ratio. We compared the performance of our proposed methodology with our earlier algorithm called hybrid quantum neural network (HQNN) proposed in the literature for heart disease prediction. HQNN and HQRF outperform in 10-fold cross validation and 70/30 train/test split ratio, respectively. The results show that HQNN requires a large training dataset while HQRF is more appropriate for both large and small training dataset. According to the experimental results, the proposed HQRF is not sensitive to the outlier data compared to HQNN. Compared to earlier works, the proposed HQRF achieved a maximum area under the curve (AUC) of 96.43% and 97.78% in predicting heart diseases using Cleveland and Statlog datasets, respectively with HQNN. The proposed HQRF is highly efficient in detecting heart disease at an early stage and will speed up clinical diagnosis.

Frederik F. Flöther

Rohan Landge, Shreni Bandgar, Rohit Salke +3 more

Heart diseases are a set of heart and blood-vessel-related disorders, arising from a variety of lifestyle, environmental, and genetic factors. Even with advancements in medical technology, accurately forecasting heart attack risk remains challenging, as today’s diagnosis methods fail to catch the nonlinearity and complexity in clinical data. Classical machine learning algorithms, such as k-nearest neighbors and support vector machine, when mixed with synthetic minority oversampling technique, show promise for heart attack risk prediction. These models for real-world scenarios struggle with high-dimensional, imbalanced, nonlinear datasets, which limits the effectiveness of models. Quantum machine learning offers a computational approach and takes benefit of concepts like superposition and entanglement to uncover insightful patterns. In this work, we compare classical and quantum models for heart attack risk prediction using a heart attack prediction dataset with 14 clinical features and 1888 patient records. The outcomes demonstrate that the classical support vector machine, when combined with synthetic minority oversampling technique, achieves the highest predictive accuracy of 92.00%, with minimal run time. k-nearest neighbors also works well, with an accuracy of 83.33%. Among quantum methods, quantum support vector machine with quantum Fourier transform achieves a 74.00% accuracy, while quantum clustering reaches 67.20%. However, both approaches required higher computational power. The quantum Fourier transform–based approach improves quantum model performance, particularly in recognizing cyclic trends in clinical data. However, classical approaches remain superior with respect to accuracy and efficiency on current hardware. This work demonstrates the strength of quantum machine learning in handling complex data while also recognizing the present challenges with quantum hardware and simulators. It suggests that upcoming research should focus on testing these models on actual quantum devices. Adopting standardized performance metrics can improve both classical and quantum-based decision support systems for heart disease risk prediction.