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.
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.
As the field of artificial intelligence is pushed forward, the question arises of how fast autonomous machines can learn. Within artificial intelligence, an important paradigm is reinforcement learning, where agents - learning entities capable of decision making - interact with the world they are placed in, called an environment. Thanks to these interactions, agents receive feedback from the environment and thus progressively adjust their behaviour to accomplish a given goal. An important question in reinforcement learning is how fast agents can learn to fulfill their tasks. To answer this question we consider a novel reinforcement learning framework where quantum mechanics is used. In particular, we quantize the agent and the environment and grant them the possibility to also interact quantum-mechanically, that is, by using a quantum channel for their communication. We demonstrate that this feature enables a speed-up in the agent's learning process, and we further show that combining this scenario with classical communication enables the evaluation of such an improvement. This learning protocol is implemented on an integrated re-programmable photonic platform interfaced with photons at telecommunication wavelengths. Thanks to the full tunability of the device, this platform proves the best candidate for the implementation of learning protocols, where a continuous update of the learning process is required.
The quantum internet is one of the frontiers of quantum information science. It will revolutionize the way we communicate and do other tasks, and it will allow for tasks that are not possible using the current, classical internet. The backbone of a quantum internet is entanglement distributed globally in order to allow for such novel applications to be performed over long distances. Experimental progress is currently being made to realize quantum networks on a small scale, but much theoretical work is still needed in order to understand how best to distribute entanglement and to guide the realization of large-scale quantum networks, and eventually the quantum internet, especially with the limitations of near-term quantum technologies. This work provides an initial step towards this goal. The main contribution of this thesis is a mathematical framework for entanglement distribution protocols in a quantum network, which allows for discovering optimal protocols using reinforcement learning. We start with a general development of quantum decision processes, which is the theoretical backdrop of reinforcement learning. Then, we define the general task of entanglement distribution in a quantum network, and we present ground- and satellite-based quantum network architectures that incorporate practical aspects of entanglement distribution. We combine the theory of decision processes and the practical quantum network architectures into an overall entanglement distribution protocol. We also define practical figures of merit to evaluate entanglement distribution protocols, which help to guide experimental implementations.
Quantum teleportation have a central role in quantum information science and allows transferring of an unknown quantum state through entanglement and classical communication. Unfortunately, the interaction with external and internal noise severely affects the quality of teleportation and poses limitations on practical applications of quantum communication networks. In this work, instead of conventional Bell teleportation, we introduce a Machine Learned adaptive protocol for optimizing multiple components of Quantum Teleportation in order to achieve higher fidelity in various noise environments. In order to demonstrate the performance of the proposed scheme, we study three different noise models, including bit-flip, amplitude damping, and depolarizing noise, both in case of single-qubit and two-qubit channels. As a result, we observe substantial improvement in the teleportation fidelity in comparison to the classical Bell-state teleportation protocol in certain noise conditions. Furthermore, the machine-learned protocol reveals a nontrivial strategy for compensation of decoherence and information losses. In addition, obtained results indicate the flexibility and reliability of the proposed framework for implementing various adaptive quantum communications while shedding light on possibilities of discovery of optimal quantum algorithms by means of automated approache
Quantum communications are gaining momentum in finding applications in a wide range of domains, especially those require high-security data transmissions. On the other hand, machine learning has achieved numerous breakthrough successes in various application domains including networking. However, currently, machine learning is not as much utilized in quantum networking as in other areas. With such motivation, we propose a machine-learning-powered entanglement routing scheme for quantum networks that aims to accommodate maximum numbers of demands (source-destination pairs) within a time window. More specifically, we present a deep reinforcement routing scheme that is called Deep Quantum Routing Agent (DQRA). In short, DQRA utilizes an empirically designed deep neural network that observes the current network states to schedule the network’s demands which are then routed by a qubit-preserved shortest path algorithm. DQRA is trained towards the goal of maximizing the number of resolved requests in each routing window by using an explicitly designed reward function. Our experiment study shows that, on averse, DQRA is able to maintain a rate of successfully routed requests above 80% in a qubit-limited grid network, and about 60% in extreme conditions i.e. each node can act as a repeater exactly once within a window. Furthermore, we show that the complexity and the computational time of DQRA are polynomial in terms of the sizes of the quantum networks.
Quantum networks make use of the quantum entanglement as building block. When two qubits are entangled, their state changes exhibit non-classical correlations used to design new applications not possible with classical communication, such us quantum key distribution or distributed quantum computation. Unfortunately, quantum entanglement is a probabilistic process strongly dependent on the features of involved devices (optical fibers, lasers, quantum memories, …). The management decisions (i.e., the control policy) to set up and keep the entanglement as long as possible with the highest quality constitutes a stochastic control problem. This process can be modelled as Markov Decision Process (MDP) and solved via the Reinforcement Learning (RL) framework (a form of Machine Learning). In this work, we apply this RL framework to learn an entanglement management policy outperforming the State-of-the-Art policy when models characterising precisely the involved quantum devices are not known.
Quantum repeaters are integral systems to quantum computing and quantum communication, as they allow the transfer of information between qubits, particularly over long distances. Because of the “no-cloning theorem,” which says that entangled quantum states cannot be directly copied, one cannot perform signal amplification in the usual way. The standard approach uses entanglement swapping, in which quantum states are teleported from one (short) segment to the next, using at each step a shared entangled pair. This is the job of the repeater. However, this requires reliable quantum memories and shared entanglement resources, which are vulnerable to noise and decoherence. Using a swap gate is also possible; however, it is difficult to manually create and implement the quantum algorithm for this circuit as the size of the system increases. Here we propose a different approach: To use machine learning to train a repeater node. To demonstrate the feasibility of this method, the system is simulated in MATLAB. Training is then done for a system of 2 qubits. It is then scaled up, with no additional training, to systems of 4, 6, and 8 qubits using transfer learning. Finally, the systems are tested in noisy conditions. The results show that scale-up is very effective and relatively easy, and the effects of noise and decoherence are reduced as the size of the system increases.