There is an ongoing debate about the fundamental security of existing quantum key exchange schemes. This debate indicates not only that there is a problem with security but also that the meanings of perfect, imperfect, conditional and unconditional (information theoretic) security in physically secure key exchange schemes are often misunderstood. It has been shown recently that the use of two pairs of resistors with enhanced Johnsonnoise and a Kirchhoff-loop -i.e., a Kirchhoff-Law-Johnson-Noise (KLJN) protocol -for secure key distribution leads to information theoretic security levels superior to those of today's quantum key distribution. This issue is becoming particularly timely because of the recent full cracks of practical quantum communicators, as shown in numerous peer-reviewed publications. The KLJN system is briefly surveyed here with discussions about the essential questions such as (i) perfect and imperfect security characteristics of the key distribution, and (ii) how these two types of securities can be unconditional (or information theoretical).

We show two universal, Boolean, deterministic logic schemes based on binary noise timefunctions that can be realized without time-averaging units. The first scheme is based on a new bipolar random telegraph wave scheme and the second one makes use of the recent noise-based logic which is conjectured to be the brain's method of logic operations [Phys. Lett. A 373 (2009) 2338-2342. Error propagation and error removal issues are also addressed.

Neural networks have attracted much interest in the last two decades for their potential to realistically describe brain functions, but so far they have failed to provide models that can be simulated in a reasonable time on computers; rather they have been limited to toy models. Quantum computing is a possible candidate for improving the computational efficiency of neural networks. In this framework of quantum computing, the Qubit neuron model, proposed by Matsui and Nishimura, has shown a high efficiency in solving problems such as data compression. Simulations have shown that the Qubit model solves learning problems with significantly improved efficiency as compared to the classical model. In this paper, we confirm our previous results in further detail and investigate what contributes to the efficiency of our model through 4-bit and 6-bit parity check problems, which are known as basic benchmark tests. Our simulations suggest that the improved performance is due to the use of superposition of neural states and the use of probability interpretation in the observation of the output states of the model.

Current computers operate at enormous speeds of ~10^13 bits/s, but their principle of sequential logic operation has remained unchanged since the 1950s. Though our brain is much slower on a per-neuron base (~10^3 firings/s), it is capable of remarkable decision-making based on the collective operations of millions of neurons at a time in ever-evolving neural circuitry. Here we use molecular switches to build an assembly where each molecule communicates-like neurons-with many neighbors simultaneously. The assembly's ability to reconfigure itself spontaneously for a new problem allows us to realize conventional computing constructs like logic gates and Voronoi decompositions, as well as to reproduce two natural phenomena: heat diffusion and the mutation of normal cells to cancer cells. This is a shift from the current static computing paradigm of serial bit-processing to a regime in which a large number of bits are processed in parallel in dynamically changing hardware.Comment: 25 pages, 6 figure

Communicated by Zoltan GinglInstantaneous noise-based logic can avoid time-averaging, which implies significant potential for low-power parallel operations in beyond-Moore-law-chips. However, in its random-telegraph-wave representation, the complete uniform superposition (superposition of all N -bit binary numbers) will be zero with high probability, that is, non-zero with exponentially low probability, thus operations with the uniform superposition would require exponential time-complexity. To fix this deficiency, we modify the amplitudes of the signals of L and H states and achieve an exponential speedup compared to the old situation. Another improvement concerns the identification of a single product-string (hyperspace vector). We introduce "time shifted noise-based logic", which is constructed by shifting each reference signal with a small time delay. This modification implies an exponential speedup of single hyperspace vector identification compared to the former case and it requires the same, O(N ) complexity as in quantum computing.