Abstract: Traditional approaches for recommending optimum combination of quantum circuits are experimentation based approaches, and require manual efforts or are cumbersome, effort intensive and iterative processes. Method and system disclosed herein generally relates to quantum experimentation, and, more particularly, for recommending optimum combination of quantum circuits. In this approach, a high-level combination of experiments are initially generated, which are further prioritized using a graph based approach, which then forms a training data. The training data is then used for generating a GNN data model, which is further used for recommending optimum combination of quantum circuits.

Abstract: This disclosure relates generally to adaptive learning based systems and methods for optimization of unsupervised clustering. The embodiments of present disclosure herein address unresolved problem of involving manual intervention in data preparation, annotating or labelling training data to train classifiers, and taking a number of clusters directly as an input from the users for data classification. The method of the present disclosure provides a fully unsupervised optimized approach for auto clustering of input data that automatically determines the number of clusters for the input data by leveraging concepts of graph theory and maximizing a cost function. The method of present disclosure is capable of handling a new data by continuously and incrementally improving the clusters. The method of present disclosure is domain agnostic, scalable, provides expected level of accuracy for real-world data, and helps in minimizing utilization of powerful processors leading to reduced overall cost.

Abstract: Quantum Information Processing (QIP) with the availability of Noisy Intermediate-Scale Quantum (NISQ) device(s) are available to work on quantum algorithms. Different problems, which are hard to solve by classical computation, but can be sped up (significantly in some cases) are also being populated. However, current approaches solve only two cluster max-cut problems. Mining the two cluster Max-Cut problem within the framework of quantum Ising model, embodiments of the present disclosure solve Quadratic Unconstrained D-ary Optimization (QUDO) problems by quantum computing with the identification of an appropriate Hamiltonian. More specifically, the problem is mapped to an Ising model to obtain a d-ray Quantum Ising Hamiltonian. The d-ray Quantum Ising Hamiltonian is then executed on one or more qudit processors, to obtain one or more resultant quantum states which are measured in a qudit computational basis to obtain at least one solution.