The Distribution of Reversible Functions is Normal

The distribution of reversible programs tends to a limit as their size increases. For problems with a Hamming distance fitness function the limiting distribution is binomial with an exponentially small chance (but non-zero) chance of perfect solution. Sufficiently good reversible circuits are more common. Expected RMS error is also calculated. Random unitary matrices may suggest possible extension to quantum computing. Using the genetic programming (GP) bench-mark, the six multiplexor, circuits of Toffoli gates are shown to give a fitness landscape amenable to evolutionary search. Minimal CCNOT solutions to the six multiplexer are found but larger circuits are more evolvable.

[1]  W. B. Langdon,et al.  Genetic Programming and Data Structures , 1998, The Springer International Series in Engineering and Computer Science.

[2]  Tommaso Toffoli,et al.  Reversible Computing , 1980, ICALP.

[3]  Jeffrey S. Rosenthal,et al.  Convergence Rates for Markov Chains , 1995, SIAM Rev..

[4]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[5]  Feller William,et al.  An Introduction To Probability Theory And Its Applications , 1950 .

[6]  William B. Langdon,et al.  How Many Good Programs are There? How Long are They? , 2002, FOGA.

[7]  R. Landauer,et al.  The Fundamental Physical Limits of Computation. , 1985 .

[8]  Riccardo Poli,et al.  Sub-machine-code genetic programming , 1999 .

[9]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[10]  Peter G. Bishop Using reversible computing to achieve fail-safety , 1997, Proceedings The Eighth International Symposium on Software Reliability Engineering.

[11]  T. Toffoli,et al.  Conservative logic , 2002, Collision-Based Computing.

[12]  Riccardo Poli,et al.  Foundations of Genetic Programming , 1999, Springer Berlin Heidelberg.

[13]  Frank E. Grubbs,et al.  An Introduction to Probability Theory and Its Applications , 1951 .

[14]  William B. Langdon,et al.  Convergence Rates For The Distribution Of Program Outputs , 2002, GECCO.