Kumar, Vivek
(2024)
Optimal Entanglement Distillation Policies for Bipartite Quantum Switches.
Master's Thesis, University of Pittsburgh.
(Unpublished)
This is the latest version of this item.
Abstract
In an entanglement distribution network, the function of a quantum switch is to generate elementary entanglement with its clients followed by entanglement swapping to distribute end-to-end entanglement of sufficiently high fidelity between clients. The threshold on entanglement fidelity is any quality-of-service requirement specified by the clients as dictated by the application they run on the network.
We consider a discrete-time model for a quantum switch that attempts generation of fresh elementary entanglement with two clients in each time step in the form of maximally entangled qubit pairs, or Bell pairs, which succeed probabilistically; the successfully generated Bell pairs are stored in noisy quantum memories until they can be swapped. We focus on establishing the value of entanglement distillation of the stored Bell pairs prior to entanglement swapping in presence of their inevitable aging, i.e., decoherence: For a simple instance of a switch with two clients, exponential decay of entanglement fidelity, and a well-known probabilistic but heralded two-to-one distillation protocol, given a threshold end-to-end entanglement fidelity, we've employed both the Markov Decision Processes framework and a Reinforcement Learning approach to find optimal policies. This dual approach allows us to address the discrete state space assumptions that constrained the Markov Decision Process Model.
By integrating Reinforcement Learning, we aim to enhance our model's flexibility. With these combined methodologies, our goal is to pinpoint the optimal action policy—whether it's waiting, distilling, or swapping—that can effectively maximize throughput. We compare the switch's performance under the optimal distillation-enabled policy with that excluding distillation.
Simulations of the two policies demonstrate the improvements that are possible in principle via optimal use of distillation with respect to average throughput, average fidelity, and jitter of end-to-end entanglement, as functions of fidelity threshold. Our model thus helps capture the role of entanglement distillation in mitigating the effects of decoherence in a quantum switch in an entanglement distribution network, adding to the growing literature on quantum switches. We also compare the switch’s performance found using simulations with theoretical bounds found out by employing queuing theory concepts on the same model.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
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| ETD Committee: |
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| Date: |
22 January 2024 |
| Date Type: |
Publication |
| Defense Date: |
28 November 2023 |
| Approval Date: |
22 January 2024 |
| Submission Date: |
9 December 2023 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
84 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Computing and Information > Information Science |
| Degree: |
MSIS - Master of Science in Information Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
—quantum switch, entanglement distribution
networks, entanglement distillation, entanglement swapping,
Markov decision process, Reinforcement Learning, Deep Reinforcement Learning |
| Date Deposited: |
22 Jan 2024 16:54 |
| Last Modified: |
22 Jan 2024 16:54 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/45642 |
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Optimal Entanglement Distillation Policies for Bipartite Quantum Switches. (deposited 22 Jan 2024 16:54)
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