Polynomial Simulations of Decohered Quantum Computers
  We define formally decohered quantum computers (using density matrices), and present a simulation of them by a probabalistic classical Turing Machine. We study the slowdown of the simulation for two cases: (1) sequential quantum computers, or quantum Turing machines(QTM), and (2) parallel quantum computers, or quantum circuits. This paper shows that the computational power of decohered quantum computers depends strongly on the amount of parallelism in the computation.   The expected slowdown of the simulation of a QTM is polynomial in time and space of the quantum computation, for any non zero decoherence rate. This means that a QTM subjected to any amount of noise is worthless. For decohered quantum circuits, the situation is more subtle and depends on the decoherence rate, eta. We find that our simulation is efficient for circuits with decoherence rate higher than some constant, but exponential for general circuits with decoherence rate lower than some other constant. Using computer experiments, we show that the transition from exponential cost to polynomial cost happens in a short range of decoherence rates, and exhibit the phase transitions in various quantum circuits. 
Dorit Aharonov (Physics and computer science, Hebrew Univ.) and   Michael Ben-Or (Computer science, Hebrew univ.)