Matrix multiplication using quantum-dot cellular automata to implement conventional microelectronics

Quantum-dot cellular automata (QCA) shows promise as a post silicon CMOS, low power computational technology. Nevertheless, to generalize QCA for next-generation digital devices, the ability to implement conventional programmable circuits based on NOR, AND, and OR gates is necessary. To this end, we devise a new QCA structure, the QCA matrix multiplier (MM), employing the standard Coulomb blocked, five quantum dot (QD) QCA cell and quasi-adiabatic switching for sequential data latching in the QCA cells. Our structure can multiply two N x M matrices, using one input and one bidirectional input/output data line. The calculation is highly parallelizable, and it is possible to achieve reduced calculation time in exchange for increasing numbers of parallel matrix multiplier units. We show convergent, ab initio simulation results using the Intercellular Hartree Approximation for one, three, and nine matrix multiplier units. The structure can generally implement any programmable logic array (PLA) or any matrix multiplication based operation.Comment: 14 pages, 9 figures, supplemental informatio

Quantum Cellular Neural Networks

We have previously proposed a way of using coupled quantum dots to construct digital computing elements - quantum-dot cellular automata (QCA). Here we consider a different approach to using coupled quantum-dot cells in an architecture which, rather that reproducing Boolean logic, uses a physical near-neighbor connectivity to construct an analog Cellular Neural Network (CNN).Comment: 7 pages including 3 figure