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An elementary review on principles of qubits and their prospects for quantum computing is provided. Due to its rapid development, quantum computing has attracted considerable attention as a core technology for the next generation and has demonstrated its potential in simulations of exotic materials, molecular structures, and theoretical computer science. To achieve fully error-corrected quantum computers, building a logical qubit from multiple physical qubits is crucial. The number of physical qubits needed depends on their error rates, making error reduction in physical qubits vital. Numerous efforts to reduce errors are ongoing in both existing and emerging quantum systems. Here, the principle and development of qubits, as well as the current status of the field, are reviewed to provide information to researchers from various fields and give insights into this promising technology.

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Josephson digital or analog ancillary circuits are an essential part of a large number of modern quantum processors. The natural candidate for the basis of tuning, coupling, and neromorphic co-processing elements for processors based on flux qubits is the adiabatic (reversible) superconducting logic cell. Using the simplest implementation of such a cell as an example, we have investigated the conditions under which it can optionally operate as an auxiliary qubit while maintaining its "classical" neural functionality. The performance and temperature regime estimates obtained confirm the possibility of practical use of a single-contact inductively shunted interferometer in a quantum mode in adjustment circuits for q-processors.

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We have theoretically studied the transport properties of the SIsNSOF structure consisting of thick (S) and thin (s) films of superconductor, an insulator layer (I), a thin film of normal metal with spin-orbit interaction (SOI) (NSO), and a monodomain ferromagnetic layer (F). The interplay between superconductivity, ferromagnetism, and spin-orbit interaction allows the critical current of this Josephson junction to be smoothly varied over a wide range by rotating the magnetization direction in the single F-layer. We have studied the amplitude of the spin valve effect and found the optimal ranges of parameters.

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Quantum computers are special purpose machines that are expected to be particularly useful in simulating strongly correlated chemical systems. The quantum computer excels at treating a moderate number of orbitals within an active space in a fully quantum mechanical manner. We present a quantum phase estimation calculation on F2 in a (2,2) active space on Rigetti's Aspen-11 QPU. While this is a promising start, it also underlines the need for carefully selecting the orbital spaces treated by the quantum computer. In this work, a scheme for selecting such an active space automatically is described and simulated results obtained using both the quantum phase estimation (QPE) and variational quantum eigensolver (VQE) algorithms are presented and combined with a subtractive method to enable accurate description of the environment. The active occupied space is selected from orbitals localized on the chemically relevant fragment of the molecule, while the corresponding virtual space is chosen based on the magnitude of interactions with the occupied space calculated from perturbation theory. This protocol is then applied to two chemical systems of pharmaceutical relevance: the enzyme [Fe] hydrogenase and the photosenzitizer temoporfin. While the sizes of the active spaces currently amenable to a quantum computational treatment are not enough to demonstrate quantum advantage, the procedure outlined here is applicable to any active space size, including those that are outside the reach of classical computation.

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Quantum computers are reaching one crucial milestone after another. Motivated by their progress in quantum chemistry, we performed an extensive series of simulations of quantum-computer runs that were aimed at inspecting the best-practice aspects of these calculations. In order to compare the performance of different setups, the ground-state energy of the hydrogen molecule was chosen as a benchmark for which the exact solution exists in the literature. Applying the variational quantum eigensolver (VQE) to a qubit Hamiltonian obtained by the Bravyi-Kitaev transformation, we analyzed the impact of various computational technicalities. These included (i) the choice of the optimization methods, (ii) the architecture of the quantum circuits, as well as (iii) the different types of noise when simulating real quantum processors. On these, we eventually performed a series of experimental runs as a complement to our simulations. The simultaneous perturbation stochastic approximation (SPSA) and constrained optimization by linear approximation (COBYLA) optimization methods clearly outperformed the Nelder-Mead and Powell methods. The results obtained when using the Ry variational form were better than those obtained when the RyRz form was used. The choice of an optimum entangling layer was sensitively interlinked with the choice of the optimization method. The circular entangling layer was found to worsen the performance of the COBYLA method, while the full-entangling layer improved it. All four optimization methods sometimes led to an energy that corresponded to an excited state rather than the ground state. We also show that a similarity analysis of measured probabilities can provide a useful insight.

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Enormous advances in technology and science have provided outstanding innovations including the development of quantum computers (QCs) capable of performing various tasks much more efficiently and quickly than the classical computers. Integrating and analyzing gigantic amounts of data, ultra-rapid calculations, solving intractable problems, secure communications, providing novel insights into the material design or biosystems, advanced simulations, rapid genome analysis and sequencing, early cancer detection, identifying novel drug applications, accelerated discovery of new molecules, targets, or theranostic agents and evaluation of their behaviors, and acquiring a deeper knowledge about the complex data patterns, formation of proteins, or mechanism of disease progression and evolution by QCs may indeed revolutionize conventional technologies and strategies. Application of quantum computing and machine learning for accelerated analysis of the biological or medical data, uncovering the mechanisms of chemical reactions or action of drug candidates, and creation of patient-specific treatment strategies using genomics data can result in the development of more effective and less toxic drugs or personalized therapy. This article highlights the importance of QCs in designing drugs and delivery systems, limitations, and possible solutions.

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The use of quantum computers and Artificial Intelligence (AI) is imperative for use in space exploration and astrobiology investigations. Considerable progress has been made since the commencement of origin of life laboratory and theoretical studies in the mid 20th century. However, the sheer amount of data amassed to date in all these studies including exoplanetary and astrobiological studies is enormous and increasing steadily. Thus, there is the need for AI and quantum computers. As AI develops, it will become crucial in the development of the statistical and database programs that are indispensable to analyze the huge quantity of cumulative data. Diverse biotic and geochemical processes have been shown to produce methane on the Earth. Elsewhere in the solar system, on other planets (e.g. Mars) and moons (e.g. Titan), as well as on exoplanets, abiotic processes are considered the primary sources of methane. Astronomers and astro-biologists infer that the presence of methane supports the possibility of the presence of at least microbial life. In addition, on the Earth, there are also degradative reactions that include smog-related compounds and hazes that are produced as artefacts of intrinsic methane geochemistry as well as due to human footprint. Astronomers and astro-biologists envision life, away from the Earth, elsewhere in the solar system and on exoplanets, to occur under conditions similar or related to terrestrial life (goldilocks zone) conditions. These properties that are compatible with life as we know it on the Earth, include planetary orbits, gravitation, star radiant energy, presence of liquid water, and compatible temperatures and pressures, found on Earth. Generally, extraterrestrial life is also considered to resemble the biochemistry, molecular biology, and physiology of life on Earth - thus the focus on detection of supposed biosignatures of microbial life that resemble the Earth's. Nevertheless a crucial factor is absent in these deliberations - viruses. On the Earth, viruses that infect Archaea and bacteria form local and widespread global ecosystems. These viruses play a crucial role and facilitate the molecular transfer of host genes among various hosts. This essential function is underestimated in evolutionary as well as astrobiological speculations. Thus, it is of substantial importance to consider the roles that viruses may have played during the origin of life as well as in any exobiology.

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From the start, Kurt Godel observed that computer and brain paradigms were considered on a par by researchers and that researchers had misunderstood his theorems. He hailed with displeasure that the brain transcends computers. In this brief article, we point out that Artificial Intelligence (AI) comprises multitudes of human-made methodologies, systems, and languages, and implemented with computer technology. These advances enhance development in the electron and quantum realms. In the biological realm, animal neurons function, also utilizing electron flow, and are products of evolution. Mirror neurons are an important paradigm in neuroscience research. Moreover, the paradigm shift proposed here - 'hall of mirror neurons' - is a potentially further productive research tactic. These concepts further expand AI and brain research.

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Artificial Intelligence (AI), robotics, co-robotics (cobots), quantum computers (QC), include surges of scientific endeavor to produce machines (mechanical and software) among numerous types and constructions that are accelerating progress to defeat infectious diseases. There is a plethora of additional applications and uses of these methodologies and technologies for the understanding of biomedicine through bioinformation discovery. Therefore, we briefly outline the use of such techniques in virology.

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Minimizing the resources required to build logic gates into useful processing circuits is key to realizing quantum computers. Although the salient features of a quantum computer have been shown in proof-of-principle experiments, difficulties in scaling quantum systems have made more complex operations intractable. This is exemplified in the classical Fredkin (controlled-SWAP) gate for which, despite theoretical proposals, no quantum analog has been realized. By adding control to the SWAP unitary, we use photonic qubit logic to demonstrate the first quantum Fredkin gate, which promises many applications in quantum information and measurement. We implement example algorithms and generate the highest-fidelity three-photon Greenberger-Horne-Zeilinger states to date. The technique we use allows one to add a control operation to a black-box unitary, something that is impossible in the standard circuit model. Our experiment represents the first use of this technique to control a two-qubit operation and paves the way for larger controlled circuits to be realized efficiently.

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