Quantum algorithm design using dynamic learning

TL;DR

This paper introduces a dynamic learning approach to design quantum algorithms by training control parameters in quantum systems, enabling the implementation of quantum gates and entanglement detection.

Contribution

It presents a novel quantum learning paradigm that optimizes control parameters for quantum operations, demonstrated on superconducting qubits and entanglement measurement.

Findings

Successfully learned classical gates XOR and XNOR

Achieved a CNOT-like gate with phase adjustments

Mapped entanglement onto correlation functions for pure and mixed states

Abstract

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 qubit system, such that the system at an initial time evolves to a state in which a given measurement corresponds to the desired operation. This can be thought of as a quantum neural network. We first apply the method to a system of two coupled superconducting quantum interference devices (SQUIDs), and demonstrate learning of both the classical gates XOR and XNOR. Training of the phase produces a gate congruent to the CNOT modulo a phase shift. Striking out for somewhat more interesting territory, we attempt learning of an entanglement witness for a two qubit system. Simulation shows a reasonably successful mapping of the entanglement at the initial time onto the correlation function at the final time for both pure and mixed states. For pure states this mapping requires knowledge of the phase relation between the two parts; however, given that knowledge, this method can be used to measure the entanglement of an otherwise unknown state. The method is easily extended to multiple qubits or to quNits.