Mitsunaga Kinjo

Implementation of a new neurochip using stochastic logic

Even though many neurochips have been developed and investigated, the best suitable way for implementation has not been known clearly. Our approach is to exploit stochastic logic for various operations required for neural functions. The advantage of stochastic logic is that complex operations can be implemented with a few ordinary logic gates. On the other hand, the operation speed is not so fast since stochastic logic requires certain accumulation time for averaging. However, a huge integration can be achieved and its reliability is high because all of operations are done on digital circuits. Furthermore, we propose a nonmonotonic neuron realized by stochastic logic, since the nonmonotonic property is efficient for the performance enhancement in association and learning. In this paper, we show the circuit design and measurement results of a neurochip comprising 50 neurons are shown. The advantages of nonmonotonic and stochastic properties are shown clearly.

An Approach for Quantum Computing using Adiabatic Evolution Algorithm

A quantum computer employing a single quantum as a qubit executes real parallel computation and has various applications. Several algorithms have been proposed for quantum computation. However, these algorithms are applicable only to a limited number of applications. Therefore, a general purpose algorithm should be studied and developed for practical use in the near future. In this paper, we focus on the adiabatic evolution algorithm for general purpose quantum computation and discuss how to use this algorithm for solving an optimization problem. We show a new algorithm incorporating an artificial neural network (ANN)-like method in order to compose another Hamiltonian. The new algorithm is helpful for reducing computation cost and is easy to implement. Successful simulation results are shown.

Quantum adiabatic evolution algorithm for a quantum neural network

In this paper, a new quantum algorithm for solving the combinatorial optimization problems is discussed. It is based on the quantum adiabatic evolution algorithm. We propose a new method for synthesizing a Hamiltonian inspired by a Hopfield network in order to improve calculation cost. The quantum system given by a new Hamiltonian has neuron-like interactions and shows quantum behavior. We present simulation results of the new algorithm for the 4-queen problem.

A study on neuromorphic quantum computation

A quantum computer employing a single quantum as a qubit executes real parallel computation. Several algorithms have been proposed for quantum computation. However, these algorithms are applicable only to a limited number of applications. Therefore, a general purpose algorithm should be studied and developed for practical use in the near future. We focus on the adiabatic evolution algorithm in order to incorporate an artificial neural network(ANN)-like method and discuss how to use this algorithm for solving an optimization problem.

A Study on Learning with a Quantum Neural Network

A quantum neural network based on the adiabatic quantum computation is one of candidates to overcome the difficulty for developing a quantum computation algorithm. In this paper, we propose a new learning method for a quantum neural network inspired by Hebb learning. Preliminary but successful results by numerical simulations have been shown. The results indicate that a quantum learning like Hebb rule can be implemented.

Study of macroscopic quantum tunnelling in Bi2Sr2CaCu2O8+δ intrinsic Josephson junctions

We report an experimental observation of the macroscopic quantum tunnelling (MQT) in a d-wave high-temperature superconductor (HTSC) Bi2Sr2CaCu2O8+δ intrinsic Josephson junction (IJJs). It is well known that the current-biased Josephson tunnel junction provides an ideal stage for studying a variety of macroscopic quantum phenomena such as MQT. They have been studied and observed in low-temperature superconductor (LTSC) Josephson junctions since the early 1980s and have been established well by now. On the other hand, in the case of HTSC the d-wave pairing symmetry allows for dissipative quasiparticle excitations within the superconducting energy gap, which is expected to severely interfere with an observation of MQT. Contrary to this naive conception, our experimental results have explained that the effects of the nodal quasiparticles are not strong enough to obscure the observation of the MQT. Furthermore, we found that the MQT in IJJs was observed at approximately 1 K, which was ten times higher than that of LTSC Josephson junctions. This higher classical-to-quantum crossover temperature is due to their high plasma frequency. We also discuss the resonant activation of IJJs in the presence of microwave radiation.

Basic property of a quantum neural network composed of Kane's qubits

It has been known a variety of optimization problems can be solved with a neural network, and a quantum computer executes real parallel computation. A quantum neural network has been proposed in order to incorporate quantum dynamics. In this paper, we test the possibility of real implementation of a quantum neural network with a nuclear spin as a qubit. First, we introduce the relation between spin and neuron, then describe the adiabatic Hamiltonian evolution applied for the state change. Next, we describe a real spin quantum system and show the simulation results. A nuclear spin system proposed by Kane behaves as a neuron with inhibitory interactions as expected in analogy to a Hopfield network.

A study on DBM network with non-monotonic neurons

In this paper, we report a study on learning ability of a deterministic Boltzmann machine (DBM) with neurons which have a nonmonotonic activation function. We use an end-cut-off-type function with a threshold parameter /spl theta/ as the nonmonotonic function. Numerical simulations of learning nonlinear problems, such as the XOR problem and the ADD problem, show that the DBM network with nonmonotonic neurons has higher learning ability compared to the network with monotonic neurons, and that the nonmonotonic neural network has novel effects which adjust the number of neurons. We have designed an integrated circuit of the 2-3-1 DBM network. The use of the nonmonotonic neurons make it possible to integrate a large scale neural network because of the simple circuit design.

Characteristics of small scale nonmonotonic neuron networks having large potentiality for learning

We report a study on learning ability of a deterministic Boltzmann machine (DBM) with neurons which have a nonmonotonic activation function. We use an end-cut-off-type function with a threshold parameter /spl theta/ as the nonmonotonic function. Numerical simulations of nonlinear problems, such as the 2-parity problem and the 4-parity problem, show that the DBM network with nonmonotonic neurons has higher learning ability compared to the network with monotonic neurons.