Resource Allocation in Quantum Networks
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Abstract
The primary responsibility of first and second-generation quantum networks will be to facilitate the delivery of bipartite quantum entanglements, also known as EPR pairs, to distinct pairs of end-users. Future optical networks empowered with quantum communication capabilities are one of the pillars of quantum technologies. However, existing classical optical network infrastructure cannot be immediately used for quantum network applications due to photon loss. The expenses and intrinsic noise inherent in quantum hardware underscore the need for an efficient deployment strategy that optimizes the placement of quantum repeaters and memories.
In the first part of this thesis, we first analyze two previously proposed protocols for entanglement distribution for quantum repeater chains but in a realistic scenario where repeaters are non-uniformly placed from each other and we have memory decoherence and classical communication overhead. Then, we present a comprehensive framework for network planning, aiming to efficiently distribute quantum repeaters across existing infrastructure, to maximize quantum network utility within an entanglement distribution network. Then, we show how learning-based approaches such as genetic and gradient-based algorithms can be used for resource allocation in quantum networks while ensuring the necessary quality of service for end users. Furthermore, we introduce the Quantum Storage Network (QSN) architecture as a solution to address time-varying demands in quantum networks. In a QSN, we envision that the network infrastructure provides entanglement storage service at some of the network nodes for future usage. During peak demands when the demand for entanglement generation is high, requests can be served from the stored EPR pairs at the storage nodes. Our results demonstrate that QSNs fare well by a factor of 40% with respect to meeting surges and changing demands compared to traditional non-overlay proposals.
For the remaining portion of this thesis, we plan to explore the idea of a quantum data center where multiple quantum processors are connected to execute a quantum algorithm in a distributed and fault-tolerant way. Specifically, we explore resource allocation for fault-tolerant distributed quantum computing architecture where we perform two-qubit gates over two logical qubits each encoded in a separate error-correcting code such as surface code.