In this study, an overloaded large-scale distributed antenna network is considered, for which the number of active users is larger than that of antennas distributed in a base station coverage area (called a cell). To avoid overload, users in each cell are divided into multiple user groups, and, to reduce the computational complexity required for multi-user multiple-input and multiple-output (MU-MIMO), users in each user group are grouped into multiple user clusters so that cluster-wise distributed MU-MIMO can be performed in parallel in each user group. However, as the network size increases, conventional computational methods may not be able to solve combinatorial optimization problems, such as user scheduling and user clustering, which are required for performing cluster-wise distributed MU-MIMO in a finite amount of time. In this study, we apply quantum computing to solve the combinatorial optimization problems of user scheduling and clustering for an overloaded distributed antenna network and propose a quantum computing-based user scheduling and clustering method. The results of computer simulations indicate that as the technology of quantum computers and their related algorithms evolves in the future, the proposed method can realize large-scale dense wireless systems and realize real-time optimization with a short optimization execution cycle.

The recent rapid increase in the scale of superconducting quantum computing systems greatly increases the demand for qubit control by digital circuits operating at qubit temperatures. In this paper, superconducting digital circuits, such as single-flux quantum and adiabatic quantum flux parametron circuits are described, that are promising candidates for this purpose. After estimating their energy consumption and speed, a conceptual overview of the superconducting electronics for controlling a multiple-qubit system is provided, as well as some of its component circuits.

There is increasing interest in quantum computing, because of its enormous computing potential. A small number of powerful quantum algorithms have been proposed to date; however, the development of new quantum algorithms for practical use remains essential. Parallel computing with a neural network has successfully realized certain unique functions such as learning and recognition; therefore, the introduction of certain neural computing techniques into quantum computing to enlarge the quantum computing application field is worthwhile. In this paper, a novel quantum associative memory (QuAM) is proposed, which is achieved with a quantum neural network by employing adiabatic Hamiltonian evolution. The memorization and retrieval procedures are inspired by the concept of associative memory realized with an artificial neural network. To study the detailed dynamics of our QuAM, we examine two types of Hamiltonians for pattern memorization. The first is a Hamiltonian having diagonal elements, which is known as an Ising Hamiltonian and which is similar to the cost function of a Hopfield network. The second is a Hamiltonian having non-diagonal elements, which is known as a neuro-inspired Hamiltonian and which is based on interactions between qubits. Numerical simulations indicate that the proposed methods for pattern memorization and retrieval work well with both types of Hamiltonians. Further, both Hamiltonians yield almost identical performance, although their retrieval properties differ. The QuAM exhibits new and unique features, such as a large memory capacity, which differs from a conventional neural associative memory.

Reversible logic is becoming more and more popular due to the fact that many novel technologies such as quantum computing, low power CMOS circuit design or quantum optical computing are becoming more and more realistic. In quantum computing, reversible computing is the main venue for the realization and design of classical functions and circuits. We present a new approach to synthesis of reversible circuits using Kronecker Functional Lattice Diagrams (KFLD). Unlike many of contemporary algorithms for synthesis of reversible functions that use n×n Toffoli gates, our method synthesizes functions using 3×3 Toffoli gates, Feynman gates and NOT gates. This reduces the quantum cost of the designed circuit but adds additional ancilla bits. The resulting circuits are always regular in a 4-neighbor model and all connections are predictable. Consequently resulting circuits can be directly mapped in to a quantum device such as quantum FPGA [14]. This is a significant advantage of our method, as it allows us to design optimum circuits for a given quantum technology.

A method has been developed for deriving the approximate global optimum of a nonlinear objective function. First, the objective function is expanded into a linear equation for a moment vector, and the optimization problem is reduced to an eigen analysis problem in the wave coefficient space. Next, the process of the optimization is expressed using a Schrodinger-type equation, so global optimization is equivalent to eigen analysis of the Hamiltonian of a Schrodinger-type equation. Computer simulation of this method demonstrated that it produces a good approximation of the global optimum. An example optimization problem was solved using a Hamiltonian constructed by combining Hamiltonians for other optimization problems, demonstrating that various types of applications can be solved by combining simple Hamiltonians.

A method was developed for deriving the approximate global optimum of a nonlinear objective function with multiple local optimums. The objective function is expanded into a linear wave coefficient equation, so the problem of maximizing the objective function is reduced to that of maximizing a quadratic function with respect to the wave coefficients. Because a wave function expressed by the wave coefficients is used in the algorithm for maximizing the quadratic function, the algorithm is equivalent to a full search algorithm, i.e., one that searches in parallel for the global optimum in the whole domain of definition. Therefore, the global optimum is always derived. The method was evaluated for various objective functions, and computer simulation showed that a good approximation of the global optimum for each objective function can always be obtained.

Quantum random access coding (QRAC) is one of the basic tools in quantum computing. It uses a quantum state for encoding the sender's bit string so that the receiver can recover any single bit of the bit string with high probability. This article surveys recent developments of QRAC, with some concrete examples of QRAC using one quantum bit, and its applications, focusing on communication complexity and locally decodable codes.

Synthesis of quantum circuits is essential for building quantum computers. It is important to verify that the circuits designed perform the correct functions. In this paper, we propose an algorithm which can be used to verify the quantum circuits synthesized by any method. The proposed algorithm is based on BDD (Binary Decision Diagram) and is called X-decomposition Quantum Decision Diagram (XQDD). In this method, quantum operations are modeled using a graphic method and the verification process is based on comparing these graphic diagrams. We also develop an algorithm to verify reversible circuits even if they have a different number of garbage qubits. In most cases, the number of nodes used in XQDD is less than that in other representations. In general, the proposed method is more efficient in terms of space and time and can be used to verify many quantum circuits in polynomial time.

This paper considers the quantum query complexity of ε-biased oracles that return the correct value with probability only 1/2 + ε. In particular, we show a quantum algorithm to compute N-bit OR functions with O(/ε) queries to ε-biased oracles. This improves the known upper bound of O(/ε2) and matches the known lower bound; we answer the conjecture raised by the paper [1] affirmatively. We also show a quantum algorithm to cope with the situation in which we have no knowledge about the value of ε. This contrasts with the corresponding classical situation, where it is almost hopeless to construct a bounded error algorithm without knowing the value of ε.

It is known that the original Grover Search (GS) can be modified to use a general value for the phase θ of the diffusion transform. Then, if the number of answers is relatively large, this modified GS can find one of the answers with probability one in a single iteration. However, such a quick and error-free GS can only be possible if we can initially adjust the value of θ correctly against the number of answers, and this seems very hard in usual occasions. A natural question now arises: Can we enjoy a merit even if GS is used without such an adjustment? In this paper, we give a positive answer using the balls-and-bins game in which the random sampling of bins is replaced by the quantum sampling, i.e., a single round of modified GS. It is shown that by using the quantum sampling: (i) The maximum load can be improved quadratically for the static model of the game and this improvement is optimal. (ii) That is also improved to O(1) for the continuous model if we have a certain knowledge about the total number of balls in the bins after the system becomes stable.

Quantum dots are one of the possible building blocks for the quantum computing device. We discuss on use of carbon nanotubes for fabrication of the quantum dot, in terms of their unique physical properties and energy scales which might be advantageous for functionalities of the quantum computing device. Simple schemes of a charge qubit and a spin qubit are described, followed by the current status of the fabrication and transport measurements of the nanotube quantum dot. Based on the basic properties and the estimated energy scales of the dot, we discuss advantages and problems of the carbon nanotube for the quantum computing device. The nanotube quantum dot may have a great advantage for the spin qubit.

The main purpose of this paper is to show that we can exploit the difference (l1-norm and l2-norm) in the probability calculation between quantum and probabilistic computations to claim the difference in their space efficiencies. It is shown that there is a finite language L which contains sentences of length up to O(nc+1) such that: (i) There is a one-way quantum finite automaton (qfa) of O(nc+4) states which recognizes L. (ii) However, if we try to simulate this qfa by a probabilistic finite automaton (pfa) using the same algorithm, then it needs Ω(n2c+4) states. It should be noted that we do not prove real lower bounds for pfa's but show that if pfa's and qfa's use exactly the same algorithm, then qfa's need much less states.

A two-qubit system made of electrons running along coupled pairs of quantum wires is described and numerically analyzed. A brief review of the basic gates is given first, based on preliminary investigations, followed by the description of the electron dynamics. A detailed analysis of a conditional phase shifter is carried out by means of a time-dependent Schrodinger solver applied to a two-particle system. A quantum network suitable for creating entanglement is simulated, and results are shown. The physical structure of the proposed network is within the reach of a solid-state implementation. The physical parameters used in the computations have been chosen with reference to silicon quantum wires embedded in silicon dioxide.

This paper describes new methematical tools, taken from quantum field theory (QFT), which may make it possible to characterize localized excitations (including solitons, but also including chaotic modes) generated by PDE systems. The significance to computer hardware and neurocomputing is also discussed. This mathematics--IF further developed--may also have the potential to reorganize and simplify our understanding of QFT itself--a topic of very great intellectual and practical importance. The paper concludes by describing three new possibilities for research, which will be very important to achieving these goals.