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.
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.
Quantum communication between quantum processors offers new capabilities and applications in quantum computing. However, Noisy Intermediate-Scale Quantum (NISQ) devices face challenges such as decoherence, entanglement distillation latency, high communication-to-data qubit ratio, quantum error correction, and scalability. Inspired by distributed systems concepts, this paper presents two solutions for optimizing quantum communication: advanced quantum repeaters and machine learning for quantum network optimization. Advanced quantum repeaters will leverage topological quantum states to improve entanglement generation, swapping, and distillation efficiency. Concurrently, machine learning techniques using multi-armed bandit algorithms will dynamically allocate quantum processing resources across distributed quantum networks. This optimization enhances the efficiency of quantum teleportation protocols and reduces computational costs. By integrating advanced quantum repeaters with machine learning optimization, the proposed solutions aim to address the challenges in quantum communication.
Noise and decoherence are two major obstacles to the implementation of large-scale quantum computing. Because of the no-cloning theorem, which says we cannot make an exact copy of an arbitrary quantum state, simple redundancy will not work in a quantum context, and unwanted interactions with the environment can destroy coherence and thus the quantum nature of the computation. Because of the parallel and distributed nature of classical neural networks, they have long been successfully used to deal with incomplete or damaged data. In this work, we show that our model of a quantum neural network (QNN) is similarly robust to noise, and that, in addition, it is robust to decoherence. Moreover, robustness to noise and decoherence is not only maintained but improved as the size of the system is increased. Noise and decoherence may even be of advantage in training, as it helps correct for overfitting. We demonstrate the robustness using entanglement as a means for pattern storage in a qubit array. Our results provide evidence that machine learning approaches can obviate otherwise recalcitrant problems in quantum computing.
Noise and decoherence are two major obstacles to the implementation of large-scale quantum computing. Because of the no-cloning theorem, which says we cannot make an exact copy of an arbitrary quantum state, simple redundancy will not work in a quantum context, and unwanted interactions with the environment can destroy coherence and thus the quantum nature of the computation. Because of the parallel and distributed nature of classical neural networks, they have long been successfully used to deal with incomplete or damaged data. In this work, we show that our model of a quantum neural network (QNN) is similarly robust to noise, and that, in addition, it is robust to decoherence. Moreover, robustness to noise and decoherence is not only maintained but improved as the size of the system is increased. Noise and decoherence may even be of advantage in training, as it helps correct for overfitting. We demonstrate the robustness using entanglement as a means for pattern storage in a qubit array. Our results provide evidence that machine learning approaches can obviate otherwise recalcitrant problems in quantum computing.
Most quantum communication tasks need to rely on the transmission of quantum signals over long distances. Unfortunately, transmission of such signals is most often limited by losses in the channel, the same issue that affects classical communication. Simple signal amplification provides an elegant solution for the classical world, but this is not possible in the quantum world, as the no-cloning theorem forbids such an operation and, thus, an alternative approach, a quantum repeater, is needed. Quantum repeaters enable one to create a known maximally entangled state between the end points of the network by first segmenting the network into pieces, creating entanglement between the segments, and then, connecting those entanglement to create the required long range entanglement. Quantum teleportation then allows an unknown quantum message to be transmitted between them using the long-range entangled state. This form of quantum communication will be at the heart of the future quantum Internet. In this review, we will detail various approaches to quantum repeaters, and discuss their expected performance and limitations.
Abstract The distribution of quantum entanglement is essential for quantum communication. Contrary to classical communication, the signal in quantum communication is impossible to copy due to the basic principles of quantum mechanics, i.e. , quantum state collapse and no-cloning theorem. However, the communication range is limited by inevitable photon loss. To counteract this issue, one can insert several repeater stations between two remote end nodes to divide the full distance into several shorter segments and successively operate entanglement swapping between adjacent repeater stations to enable long-distance quantum communication. Up to now, one can classify quantum repeaters (QRs) into three generations based on the methods of reducing errors resulted from photon loss and imperfect operations. In this paper, we will briefly review advances for each generation of QRs and discuss the advantages and challenges of each generation. Moreover, we also discuss some efficient proposals of building blocks to overcome the bottlenecks for QRs.
Major obstacles remain to the implementation of macroscopic quantum computing: hardware problems of noise, decoherence, and scaling; software problems of error correction; and, most important, algorithm construction. Finding truly quantum algorithms is quite difficult, and many of these genuine quantum algorithms, like Shor's prime factoring or phase estimation, require extremely long circuit depth for any practical application, which necessitates error correction. In contrast, we show that machine learning can be used as a systematic method to construct algorithms, that is, to non-algorithmically “program” quantum computers. Quantum machine learning enables us to perform computations without breaking down an algorithm into its gate “building blocks”, eliminating that difficult step and potentially increasing efficiency by simplifying and reducing unnecessary complexity. In addition, our non-algorithmic machine learning approach is robust to both noise and to decoherence, which is ideal for running on inherently noisy NISQ devices which are limited in the number of qubits available for error correction. We demonstrate this using a fundamentally nonclassical calculation: experimentally estimating the entanglement of an unknown quantum state. Results from this have been successfully ported to the IBM hardware and trained using a hybrid reinforcement learning method.