Quantum Internet: Networking Challenges in Distributed Quantum Computing

Cacciapuoti, Angela Sara; Caleffi, Marcello; Tafuri, F.; Cataliotti, F. S.; Gherardini, Stefano; Bianchi, Giuseppe

doi:10.1109/mnet.001.1900092

Cited by 284 publications

(230 citation statements)

References 16 publications

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“…Focusing on gate-model quantum computer architectures is motivated by the successful demonstration of the practical implementations of gate-model quantum computers [7][8][9][10][11], and several important developments for near-term gate-model quantum computations are currently in progress. Another important aspect is the application of gate-model quantum computations in the near-term quantum devices of the quantum Internet [25,29,31,[51][52][53][54][55][56][57][58][59][60][61][62][63][64].…”

Section: Introductionmentioning

confidence: 99%

Training Optimization for Gate-Model Quantum Neural Networks

Gyöngyösi

Imre

2019

Sci Rep

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Gate-based quantum computations represent an essential to realize near-term quantum computer architectures. A gate-model quantum neural network (QNN) is a QNN implemented on a gate-model quantum computer, realized via a set of unitaries with associated gate parameters. Here, we define a training optimization procedure for gate-model QNNs. By deriving the environmental attributes of the gate-model quantum network, we prove the constraint-based learning models. We show that the optimal learning procedures are different if side information is available in different directions, and if side information is accessible about the previous running sequences of the gate-model QNN. The results are particularly convenient for gate-model quantum computer implementations.

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Section: Introductionmentioning

confidence: 99%

Training Optimization for Gate-Model Quantum Neural Networks

Gyöngyösi

Imre

2019

Sci Rep

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“…Quantum networks promise to be one of the first applications of an upcoming quantum technology [1,2] (see also e.g. [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]). They are the quantum counterpart of classical networks, where quantum information rather than classical information is distributed, stored and processed.…”

Section: Introductionmentioning

confidence: 99%

Delocalized information in quantum networks

Miguel-Ramiro

Dür

2020

New J. Phys.

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We consider entanglement-based quantum networks where information is stored in a delocalized way within regions or the whole network. This offers a natural protection against failure of network nodes, loss and decoherence, and has built-in security features. Quantum information is transmitted within the network by performing local measurements on individual nodes only. Information can be localized within regions or at a specific node by collaborative actions using only entanglement within a region, or sometimes even without entanglement. We discuss several examples based on error correction stabilizer codes, Dicke states and correlation space encodings. We show how to design fully functional networks using encoded states or correlation space resources. the approach we follow here the failure of one (or several) individual nodes may have only a very limited influence, thereby minimizing the influence of network node failures. (ii) With respect to security of the stored information, the accessible information per node is bounded and can be made arbitrarily small. This implies that multiple nodes need to cooperate in order to access the information, while it is protected against malicious parties. In this context it is also interesting to note that the entanglement shared between an individual party and the rest of the network can be small [33], significantly less than one ebit. (iii) Since information is no longer stored in its bare form, one has to think of encoding, decoding and processing of information. Ideally, this should be done by local operations on individual nodes only, however it may also require some (restricted) amount of shared entanglement. In fact we find that processing of information, in particular transport among an entanglement-based network, is always possible using local operations only [34,35].We consider two different scenarios: (a) storage networks, where quantum information is stored in a distributed way among all or multiple nodes, and (b) generic networks with full functionality, including transport, that are comprised of different connected regions. Each region consists of multiple network nodes and corresponds to a single logical qubit. Regarding (a), we analyze several kind of encodings, where the logical basis states are given by codewords of error correction stabilizer codes, Dicke states [36][37][38], or resource states that have been discussed in the context of quantum computation in correlation space [33][34][35]. The usage of error correction codes for storage is well known and has been widely discussed. Such an approach offers protection against noise or loss, however requires active error correction. When used in a distributed scenario as we consider here, entanglement or non-local operations are required to detect and correct errors. Dicke state encodings in contrast have passive, built in protection features. Even without active error correction, quantum information is only slightly disturbed by loss, decoherence and node failures of a restricted amount of parties. Furtherm...

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“…However, while quantum repeaters can be realized without the necessity of quantum memories [1,3], these units, in fact, are required for guaranteeing an optimal performance in any high-performance quantum networking scenario [3, 4, 8-22, 27-30, 32-39]. Therefore, the utilization of quantum memories still represents a fundamental problem in the quantum Internet [41][42][43][44][45][126][127][128][129][130], since the near-term quantum devices (such as quantum repeaters [5,6,9,39,76,77,79,82,84]) and gate-model quantum computers [23][24][25][26][47][48][49][50][51][52][53][54] have to store the quantum states in their local quantum memories [55,. The main problem here is the efficient readout of the stored quantum systems and the low retrieval efficiency of these systems from the quantum registers of the quantum memory.…”

Section: Introductionmentioning

confidence: 99%

Optimizing High-Efficiency Quantum Memory with Quantum Machine Learning for Near-Term Quantum Devices

Gyöngyösi

Imre

2020

Sci Rep

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Quantum memories are a fundamental of any global-scale quantum Internet, high-performance quantum networking and near-term quantum computers. A main problem of quantum memories is the low retrieval efficiency of the quantum systems from the quantum registers of the quantum memory. Here, we define a novel quantum memory called high-retrieval-efficiency (HRE) quantum memory for near-term quantum devices. An HRE quantum memory unit integrates local unitary operations on its hardware level for the optimization of the readout procedure and utilizes the advanced techniques of quantum machine learning. We define the integrated unitary operations of an HRE quantum memory, prove the learning procedure, and evaluate the achievable output signal-to-noise ratio values. We prove that the local unitaries of an HRE quantum memory achieve the optimization of the readout procedure in an unsupervised manner without the use of any labeled data or training sequences. We show that the readout procedure of an HRE quantum memory is realized in a completely blind manner without any information about the input quantum system or about the unknown quantum operation of the quantum register. We evaluate the retrieval efficiency of an HRE quantum memory and the output SNR (signalto-noise ratio). The results are particularly convenient for gate-model quantum computers and the near-term quantum devices of the quantum Internet.1. We define a novel quantum memory called high-retrieval-efficiency (HRE) quantum memory.2. An HRE quantum memory unit integrates local unitary operations on its hardware level for the optimization of the readout procedure and utilizes the advanced techniques of quantum machine learning.3. We define the integrated unitary operations of an HRE quantum memory, prove the learning procedure, and evaluate the achievable output signal-to-noise ratio values. We prove that local unitaries of an HRE quantum memory achieve the optimization of the readout procedure in an unsupervised manner without the use of any labeled data or training sequences.4. We evaluate the retrieval efficiency of an HRE quantum memory and the output SNR.5. The proposed results are convenient for gate-model quantum computers and near-term quantum devices.This paper is organized as follows. Section 2 defines the system model and the problem statement. Section 3 evaluates the integrated local unitary operations of an HRE quantum memory. Section 4 proposes the retrieval efficiency in terms of the achievable output SNR values. Finally, Section 5 concludes the results. Supplemental material is included in the Appendix.

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Quantum Internet: Networking Challenges in Distributed Quantum Computing

Cited by 284 publications

References 16 publications

Training Optimization for Gate-Model Quantum Neural Networks

Training Optimization for Gate-Model Quantum Neural Networks

Delocalized information in quantum networks

Optimizing High-Efficiency Quantum Memory with Quantum Machine Learning for Near-Term Quantum Devices

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