Machine Learning for Long-Distance Quantum Communication

Abstract

Machine learning can help us in solving problems in the context of big-data analysis and classification, as well as in playing complex games such as Go. But can it also be used to find novel protocols and algorithms for applications such as large-scale quantum communication? Here we show that machine learning can be used to identify central quantum protocols, including teleportation, entanglement purification, and the quantum repeater. These schemes are of importance in long-distance quantum communication, and their discovery has shaped the field of quantum information processing. However, the usefulness of learning agents goes beyond the mere reproduction of known protocols; the same approach allows one to find improved solutions to long-distance communication problems, in particular when dealing with asymmetric situations where the channel noise and segment distance are nonuniform. Our findings are based on the use of projective simulation, a model of a learning agent that combines reinforcement learning and decision making in a physically motivated framework. The learning agent is provided with a universal gate set, and the desired task is specified via a reward scheme. From a technical perspective, the learning agent has to deal with stochastic environments and reactions. We utilize an idea reminiscent of hierarchical skill acquisition, where solutions to subproblems are learned and reused in the overall scheme. This is of particular importance in the development of long-distance communication schemes, and opens the way to using machine learning in the design and implementation of quantum networks.

Optimizing entanglement generation and distribution using genetic algorithms

“…We have introduced a methodology for the optimization of entanglement generation and distribution in repeater chains using GAs. In contrast with previous work in this area [18][19][20][21][22], our methodology is systematic, modular and broadly applicable. We validated it by benchmarking our GAs on functions commonly used for this purpose and by applying it to a repeater chain generating Werner states.…”

“…This allows us to answer questions such as what are the worst possible repeaters satisfying target benchmarks. Contrasting with previous work on repeater chain optimization [18][19][20][21][22][23], our methodology constitutes a systematic and modular approach to this problem, successfully integrating simulation and optimization tools, as well as allowing for the use of high-performance computing (HPC) clusters. A high-level overview of how a user interfaces with this process is shown in figure 1.…”

Emerging Complexity in Distributed Intelligent Systems

“…At present, due to various technological limitations, the use of quantum communication channels in large-scale complex networks is minimal; see Table 1 . Quantum communication networks, which are currently starting to implement classical AIA facilities, generally are realized at the principle of proofs level [ 95 ]. Their complexity and capabilities are not yet known in detail [ 96 ].…”

Solving nonlinear differential equations with differentiable quantum circuits

“…The rise of VQE has launched further development of variational quantum algorithms (VQAs) [67][68][69][70][71], targeting generic data analysis and machine learning tasks. Performed by parametrized quantum circuits, also dubbed as quantum neural networks (QNNs) [67], VQAs rapidly expand applications to include classification [72][73][74][75][76][77][78], regression [73], generative modeling [79][80][81][82][83][84][85][86][87], clustering [88,89], reinforcement learning [90][91][92][93][94][95], quantum simulation [96][97][98][99], quantum metrology [100][101][102], and other tasks. The power of VQAs for machine learning (ML) tasks comes from the high expressibility of quantum circuits, where data points are mapped to quantum states.…”