A fast quantum mechanical algorithm for database search
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer
SIAM Journal on Computing
Simulations of quantum neural networks
Information Sciences—Informatics and Computer Science: An International Journal - Special Issue on Quantum Computing and Neural Information Processing
A polynomial quantum algorithm for approximating the Jones polynomial
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
Quantum Computation and Quantum Information: 10th Anniversary Edition
Quantum Computation and Quantum Information: 10th Anniversary Edition
Multiqubit entanglement of a general input state
Quantum Information & Computation
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We present a dynamic learning paradigm for "programming" a general quantum computer.A learning algorithm is used to find the control parameters for a coupled qubitsystem, such that the system at an initial time evolves to a state in which a given measurementcorresponds to the desired operation. This can be thought of as a quantumneural network. We first apply the method to a system of two coupled superconductingquantum interference devices (SQUIDs), and demonstrate learning of both the classicalgates XOR and XNOR. Training of the phase produces a gate congruent to the CNOTmodulo a phase shift. Striking out for somewhat more interesting territory, we attemptlearning of an entanglement witness for a two qubit system. Simulation shows a reasonablysuccessful mapping of the entanglement at the initial time onto the correlationfunction at the final time for both pure and mixed states. For pure states this mappingrequires knowledge of the phase relation between the two parts; however, giventhat knowledge, this method can be used to measure the entanglement of an otherwiseunknown state. The method is easily extended to multiple qubits or to quNits.