We all have been using classical computers for a long time. Quantum computing uses the phenomena of quantum mechanics like superposition and entanglement. Quantum computations can help achieve for the breakthroughs we have been looking for in science, machine learning, financial planning, medicine, etc., where classical computers’ computing power is not enough. It was not long back when quantum computing's applications in our life were all just theoretical. However, to utilise the power of quantum computations for real-life applications, several recent developments have been made. Keeping that in mind, this study aims to explore the existing and upcoming applications of quantum computing. In this study, they start with an introduction of quantum computing fundamentals, following which, they give a brief overview of various applications of quantum computing in several significant areas of computer science, such as cryptography, machine learning, deep learning, and quantum simulations. They also cover various real-life scenarios such as risk analysis, logistics, and satellite communication.

Quantum computing is currently a topic of interest that harnesses the phenomena of quantum mechanics. It can address several scientific challenges and generate new business opportunities. Recently, for the first time in the history of quantum computing, the authors are starting to see practical applications. Keeping this in mind, this article is designed to explore the field without any required prerequisites. The authors start with a brief overview of the fundamentals of quantum computing and also outline several applications. The timeline for widespread adoption cannot be predicted, but quite a few organisations have built the first generation of quantum computers using various hardware technologies. The authors have briefly covered the wide landscape of hardware technologies. The first generation of quantum computers can be programmed using available software development kits and accessed using online cloud services. Furthermore, the growing trend in investments and patents in the field of quantum computing is also presented. A major reason for this trend is the threat that quantum computers pose against cryptography.

Quantum communication is one of the cutting-edge research areas today, where the scheme of remote state preparation (RSP) has drawn significant attention of researchers. The authors propose here a hierarchical RSP protocol for sending a two-qubit entangled state using a seven-qubit highly entangled state derived from Brown et al. state. They have also studied here the effects of two well-known noise models namely amplitude damping (AD) and phase damping (PD). An investigation on the variation of the fidelity of the state with respect to the noise operator and receiver is made. PD noise is found to affect the fidelity more than the AD noise. Furthermore, the higher power receiver obtains the state with higher fidelity than the lower power receiver under the effect of noise. To the best of their knowledge, they believe that they have achieved the highest fidelity for the higher power receiver, 0.89 in the presence of maximum AD noise and 0.72 in the presence of maximum PD noise, compared to all the previously proposed RSP protocols in noisy environments. The study of noise is described in a very pedagogical manner for a better understanding of the application of noise models to a communication protocol.

Multiple linear regression assumes an imperative role in supervised machine learning. In 2009, Harrow et al. [Phys. Rev. Lett. 103, 150502 (2009)] showed that their Harrow Hassidim Lloyd (HHL) algorithm can be used to sample the solution of a linear system exponentially faster than any existing classical algorithm. The entire field of quantum machine learning gained considerable traction after the discovery of this celebrated algorithm. However, effective practical applications and experimental implementations of HHL are still sparse in the literature. Here, the authors demonstrate a potential practical utility of HHL, in the context of regression analysis, using the remarkable fact that there exists a natural reduction of any multiple linear regression problem to an equivalent linear systems problem. They put forward a 7-qubit quantum circuit design, motivated from an earlier work by Cao et al. [Mol. Phys. 110, 1675 (2012)], to solve a three-variable regression problem, using only elementary quantum gates. They also implement the group leaders optimisation algorithm (GLOA) [Mol. Phys. 109 (5), 761 (2011)] and elaborate on the advantages of using such stochastic algorithms in creating low-cost circuit approximations for the Hamiltonian simulation. Further, they discuss their Qiskit simulation and explore certain generalisations to the circuit design.

This study presents a framework for the enhancement of parameter estimation issues by computing the time average of several quantum observables measured under the evolution of the Hamiltonian system. These time-cum-quantum averages are expressed as a quadratic function of the orthogonal projections on the eigen subspaces of the system. Estimation of the parameters is done using the approximate time-independent perturbation theory assuming the other parameters to be small with respect to one of the parameters that is normalised to unity in the presence of noise. The study also proves that the derived expression for the mean square parameter estimation error attains the quantum limit. When the measurement noise is classical and for general positive operator-valued measure successive time, the authors derive an expression for the quantum Cramèr–Rao lower bound.

Quantum-dot cellular automata (QCA) are the potential alternative to complementary metal–oxide–semiconductor-based technology. Ideally, zero power dissipation can be achieved with the help of reversible computing. In this study, a novel design of a reversible priority encoder based on QCA is proposed. The basic building blocks for the design are Toffoli and BJN gates. The proposed design is verified by the QCA designer simulator. The performance analysis of the reversible priority encoder is performed based on the simulation results. The proposed encoder not only overcomes the problem of the messy code but also declines the amount of heat energy dissipation through reversible logic. The proposed reversible circuit could be a major component in future wireless communication because reversible logic accounts for zero loss of information. The estimation of power consumption by proposed QCA circuits is explored that implies QCA can be an ideal platform to implement reversible circuits. The relationship with recently proposed work is also discussed.