D-Wave quantum computer appears to run faster than classical model

Samuel L. Smith

Many natural and artificial processes in the universe can be modeled as searches for some unique value. The range of possibilities examined for the potential answer can all be assigned a numerical value ranking how well they match the criteria for the search. Such a range of values is called a fitness landscape. In the field of quantum computation, quantum searches have received a fair amount of study in the past 20 years. Typical quantum searches rely on the principle that the fitness value for any incorrect answer to the search is zero and the single correct answer is a one. The quantum search has been shown to be immensely faster than equivalent classical searches. There has not been much study yet on the quantum search of a fitness landscape consisting of a range of values. Our work is focused on understanding how quantum searches can be used on more advanced fitness landscapes and whether or not these searches are still faster than their classical counterparts. Thus far, there appears to be evidence that the quantum search is better at finding a particular point in a landscape than the classical, although not particularly faster than it. We've also observed that the quantum tends to have a faster mixing time than its classical counterpoint when in a constrained landscape. This leads us to believe that the quantum search would probably be better suited to finding optimum points in a landscape than the classical search. For the most part, our research has been carried out using the current applicable literature and run experimentally on the computer. If there is significant evidence a quantum search searches a fitness landscape better than a classical one, then we hope to show the application of our results to molecular evolution.

P. Aaron Lott

Tarun Kumar, Dilip Kumar, Gurmohan Singh

Quantum machine learning (QML) is an evolving field which is capable of surpassing the classical machine learning in solving classification and clustering problems. The enormous growth in data size started creating barrier for classical machine learning techniques. QML stand out as a best solution to handle big and complex data. In this paper quantum support vector machine (QSVM) based models for the classification of three benchmarking datasets namely, Iris species, Pumpkin seed and Raisin has been constructed. These QSVM based classification models are implemented on real-time superconducting quantum computers/simulators. The performance of these classification models is evaluated in the context of execution time and accuracy and compared with the classical support vector machine (SVM) based models. The kernel based QSVM models for the classification of datasets when run on IBMQ_QASM_simulator appeared to be 232, 207 and 186 times faster than the SVM based classification model. The results indicate that quantum computers/algorithms deliver quantum speed-up.

Kamil Ravilevich Khadiev, Liliya Safina

In this work, we consider the performance of using a quantum algorithm to predict the result of a binary classification problem when a machine learning model is an ensemble of any simple classifiers. This approach is faster than classical prediction and uses quantum and classical computing, but it is based on a probabilistic algorithm. Let N be the number of classifiers from an ensemble model and O(T) be the running time of prediction of one classifier. In classical case, the final result is obtained by ”averaging” outcomes of all ensemble model’s classifiers. The running time in classical case is O (N · T). We propose an algorithm that works in O (√N · T ).