  We present a quantum algorithm for combinatorial optimization using the cost
structure of the search states. Its behavior is illustrated for overconstrained
satisfiability and asymmetric traveling salesman problems. Simulations with
randomly generated problem instances show each step of the algorithm shifts
amplitude preferentially towards lower cost states, thereby concentrating
amplitudes into low-cost states, on average. These results are compared with
conventional heuristics for these problems.
