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It is well known that environmental decoherence is a crucial barrier in realizing various quantum information processing tasks; on the other hand, it plays a pivotal role in explaining how a quantum system's fragile state leads to a robust classical state. Zurek (Nat Phys 5(3):181–188, 2009) was the first to develop the theory which successfully describes the emergence of classical objectivity of quantum systems using decoherence, introduced by the environment. Here, we consider an n-qubit generalized quantum circuit for the quantum system–environment interaction model, where the first qubit represents the quantum system, and the rest are for the environmental fragments. This quantum circuit is implemented on ibmq_athens and ibmq_16_melbourne for n = 2 , 3 , 4 , 5 , 6 . Its accuracy is checked using quantum state tomography and enhanced using the quantum error mitigation procedure. The reconstructed density matrices are used to investigate quantum-classical correlation and the mutual information between the quantum system and the environment. The investigation proves the quantum Darwinism principle when the quantum circuits are executed on the noise-less simulator; however, it shows the unaccountable behavior when implemented on the real quantum devices. The results via the noise-less simulator successfully prove that the environmental fragment size and the interaction strength play a crucial role in the emergence of classicality. [ABSTRACT FROM AUTHOR]

Quantum machine learning has experienced significant progress in both software and hardware development in the recent years and has emerged as an applicable area of near-term quantum computers. In this work, we investigate the feasibility of utilizing quantum machine learning (QML) on real clinical datasets. We propose two QML algorithms for data classification on IBM quantum hardware: a quantum distance classifier (qDS) and a simplified quantum-kernel support vector machine (sqKSVM). We utilize these different methods using the linear time quantum data encoding technique ( log 2 N ) for embedding classical data into quantum states and estimating the inner product on the 15-qubit IBMQ Melbourne quantum computer. We match the predictive performance of our QML approaches with prior QML methods and with their classical counterpart algorithms for three open-access clinical datasets. Our results imply that the qDS in small sample and feature count datasets outperforms kernel-based methods. In contrast, quantum kernel approaches outperform qDS in high sample and feature count datasets. We demonstrate that the log 2 N encoding increases predictive performance with up to + 2% area under the receiver operator characteristics curve across all quantum machine learning approaches, thus, making it ideal for machine learning tasks executed in Noisy Intermediate Scale Quantum computers. [ABSTRACT FROM AUTHOR]

The noisy intermediate-scale quantum (NISQ) devices that have just emerged in recent years provide an opportunity for the physical realization of quantum circuits. As the first step of mapping quantum circuits to these devices, qubit allocation imposes a great impact on the number of additional quantum operations required throughout the mapping. The global optimization of qubit allocation is NP-hard, but since many current NISQ devices have very few qubits, it is possible to solve this problem exactly. In this paper, we propose an exact approach for qubit allocation, which takes advantage of the branch and bound algorithm as the basic framework. In order to further prune the considerably huge state space, three techniques have been proposed and integrated into the exact approach, including an optimized lower bound for quadratic assignment problem, prioritization of logical qubits, and branch pruning by structural symmetry of physical qubits. Moreover, based on the multiple optimal solutions obtained by the exact approach, we give an error-aware qubit allocation method. For problems that are too large to be solved by the exact approach, we also give a heuristic qubit allocation approach with polynomial time complexity. Experimental evaluations show that the exact approach proposed in this paper is feasible for the qubit allocation of current NISQ architectures. For all benchmarks considered, this exact approach can find multiple optimal solutions to the qubit allocation problem on IBM Melbourne, a 16-qubit NISQ architecture, in no more than 20 min. It can serve as a evaluation baseline of any heuristic approach. Experimental evaluations also show that the proposed heuristic approach is near-optimal in terms of routing cost. Both the exact approach and the heuristic approach can be integrated into existing quantum circuit mapping approaches. [ABSTRACT FROM AUTHOR]

Quantum secret sharing is a way to share secret messages among the clients in a group with complete security. For the first time, Hillery et al. (Phys Rev A 59:1829, 1999) proposed the quantum version of classical secret sharing protocol using GHZ states. Here, we implement the above quantum secret sharing protocol in 'IBM Q 5 Tenerife' quantum processor and compare the experimentally obtained results with the theoretically predicted ones. Further, a new quantum binary voting protocol is proposed and implemented in the 14-qubit 'IBM Q 14 Melbourne' quantum processor. The results are analyzed through the technique of quantum state tomography, and the fidelity of states is calculated for different number of executions made in the device. [ABSTRACT FROM AUTHOR]