
Suppose that we are given a quantum computer programmed ready to perform a computation if it is switched on. Counterfactual computation is a process by which the result of the computation may be learnt without actually running the computer. Such processes are possible within quantum physics and to achieve this effect, a computer embodying the possibility of running the computation must be available, even though the computation is, in fact, not run. We study the possibilities and limitations of general protocols for the counterfactual computation of decision problems (where the result r is either 0 or 1). If p(r) denotes the probability of learning the result r ``for free'' in a protocol then one might hope to design a protocol which simultaneously has large p(0) and p(1). However we prove that p(0)+p(1) never exceeds 1 in any protocol and we derive further constraints on p(0) and p(1) in terms of N, the number of times that the computer is not run. In particular we show that any protocol with p(0)+p(1)=1-epsilon must have N tending to infinity as epsilon tends to 0. These general results are illustrated with some explicit protocols for counterfactual computation. We show that "interaction-free" measurements can be regarded as counterfactual computations, and our results then imply that N must be large if the probability of interaction is to be close to zero. Finally, we consider some ways in which our formulation of counterfactual computation can be generalised.
19 pages. LaTex, 2 figures. Revised version has some new sections and expanded explanations
Quantum Physics, quantum computation, counterfactual, Quantum computation, FOS: Physical sciences, measurement, Models of computation (Turing machines, etc.), probability of learning, Quantum Physics (quant-ph), interaction-free, General topics in artificial intelligence
Quantum Physics, quantum computation, counterfactual, Quantum computation, FOS: Physical sciences, measurement, Models of computation (Turing machines, etc.), probability of learning, Quantum Physics (quant-ph), interaction-free, General topics in artificial intelligence
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