| As the complexity of quantum computers increases, typical architectural issues such as communication, layout, and efficient design need to be addressed. This document addresses some basic architectural ideas for quantum computers.; To be useful in reasonable calculations, quantum states will need constant error correction. This need guides how best to lay out physical components to minimize error-correction overhead. I propose a layout that minimizes communication overhead, and discuss the implementation of error-correction algorithms on that layout. I compare the cost overhead of purely local communication to communication by teleportation, and calculate the break-even point at which teleportation becomes efficient.; Additionally, the overhead of error correction can be reduced by using a memory hierarchy to more efficiently store data not currently being computed on. The main requirement is the same as for a classical computer's cache: temporal locality. I show that an important quantum routine can be rearranged to take advantage of a small quantum memory cache, and compute the achieved savings.; In a quantum system, complex operators are built up from the basic operators allowed by a given technology model. I show that the set of operators required to implement any complex operator in an error-corrected system can be approximated to arbitrary precision, given two elementary operators. I give results for all the operators in the set.; Finally, I examine methods to initialize a quantum system. Quantum operators are reversible, so data cannot simply be written over. Instead, initialization entails compressing the entropy of a set of quantum bits into a small subset of those bits, leaving the rest of the bits in a known, non-random state. I examine three such compression algorithms. The best of these itself requires a pool of known states, and so cannot be used directly. The other two algorithms, however, produce less-than-optimal results. I explore why they produce suboptimal results, and propose that one of the suboptimal algorithms be used to compress the entropy of a subsystem, using the resulting known state to run the optimal algorithm. |
