Machine Learning for Designing Fast Quantum Gates

Date
2016-01-26
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Abstract
Fault-tolerant quantum computing requires encoding the quantum information into logical qubits and performing the quantum information processing in a code-space. Quantum error correction codes, then, can be employed to diagnose and remove the possible errors in the quantum information, thereby avoiding the loss of information. Although a series of single- and two-qubit gates can be employed to construct a quantum error correcting circuit, however this decomposition approach is not practically desirable because it leads to circuits with long operation times. An alternative approach to designing a fast quantum circuit is to design quantum gates that act on a multi-qubit gate. Here I devise quantum control schemes to design high-fidelity single-shot multi-qubit (up to three) quantum gates. Quantum control task is to steer quantum dynamics towards closely realizing specific quantum operation by varying the external control parameters (external field) such that the resultant evolution closely approximates the desired evolution. A set of instructions that determines the control parameters, and hence the effectiveness of the control scheme, is called a policy. Machine learning algorithms can be employed to find successful policies for designing quantum gates. In particular, we employ supervised machine learning techniques to generate these successful policies. Finding successful policies is a feasibility problem for which optimization algorithms can be employed. Greedy algorithms are at the heart of machine learning techniques. They converge faster onto a successful policy and require less-computational resource than non-greedy algorithms. However, there is no guarantee that greedy algorithms succeed to a feasible solution when there exist constraints on i) gate operation time ii) computational resources, and iii) experimental resources. Our results show the failure of standard greedy machine learning algorithms and the superiority of non-greedy machine learning algorithms over greedy ones for designing quantum logic gates, when there exist constraints on the quantum system. We have also observed the failure of existing greedy and non-greedy techniques for designing high-fidelity three-qubit gates. Hence, we devised our machine learning technique called Subspace-Selective Self-adaptive Differential Evolution (SuSSADE). Each three-qubit gate designed by SuSSADE operates as fast as an entangling two-qubit gate under the same experimental constraints.
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Physics--Theory
Citation
Zahedinejad, E. (2016). Machine Learning for Designing Fast Quantum Gates (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/26805