Most modern computing systems follow the so-called Von Neumann architecture. This architecture separates memory, control and logic. This widely successful model includes inherent performance limits that researchers have recently sought to overcome. Quantum computing represents one branch of research that seeks to provide massive parallelism in departure from the traditional model. Practical quantum computing systems still have many technological hurdles to overcome, and large scale quantum computing schemes have not yet been demonstrated to outperform traditional CMOS large scale integrations.
Memristors have been attracted a lot of attention due to the prospects of non-volatile information storage. See, Chua, L. O. & Kang, S. M., “Memristive devices and systems. Proc. IEEE 64, 209-223 (1976); the International Technology Roadmap for Semiconductors, 2011 Edition (http://www.itrs.net/). Memristors have been recently proposed to enable “stateful” logic operations via material implication. See, J. Borghetti et. al., “‘Memristive switches enable ‘stateful’ logic operations via material implication,” Nature, 464, 873, (2010). Memristive based systems employ intrinsically dissipative memristive devices, which consume power at a very high level.
Recent efforts, including research by some of the present inventors and colleagues, have provided information storage based upon capacitors or inductors with memory (together with memristors collectively called memelements). See, Di Ventra, M., Pershin, Y. V. & Chua, L. O., “Circuit elements with memory: memristors, memcapacitive systems and meminductors,” Proceedings of the IEEE 97, 1717-24 (2009). Computation has been demonstrated only with memristors (but not memcapacitors or meminductors) as stated in the previous paragraph where J. Borghetti et. al. has been cited. The computation demonstrate with the memristors was limited only to the logic operation “IMP” (material implication).
Some of memelements are readily fabricated with current technology and can be integrated with CMOS. Pershin, Y. V. & Di Ventra, M., “Memory effects in complex materials and nanoscale systems,” Advances in Physics 60, 145-227 (2011); Jo, S. H., Kim, K-H. & Lu, W., “High-Density Crossbar Arrays Based on a Si Memristive System,”. Nano Lett. 9, 870874 (2009). These past efforts focused mainly on models and realizations of memcapacitors and meminductors but fail to provide any basis for computing (including binary logic operations) based on these elements.