@conference {577,
	title = {Quantum Networks: Reset-and-Reuse can be a Game-changer for Entanglement via Distillation},
	booktitle = {2022 IEEE International Conference on Quantum Computing and Engineering (QCE)},
	year = {2022},
	abstract = {<p>The future quantum network repeater is envisioned to primarily serve the role of creating entanglement between nodes and distilling those entanglements to an optimal level of performance. During our investigation, we implemented a multi-pass protocol for entanglement distillation and tested it on the IBM-Q environment, demonstrating successively improved results after multiple passes. We implemented two versions of multi-pass distillation, BBPSSW and DEJMPS, with a focus on optimizing the use of qubits, via the reset-and-reuse capability of the IBM implementation. The novel feature of reset-and-reuse can be a game-changer and can minimize the number of qubits required for large-scale applications. We also found that, though it is currently not possible to implement a criterion for continued distillation passes as a run-time feedback loop, the process can be studied through post-circuit data analysis. Our results also show that fidelity alone may guide us to discard some approaches that show success based on other metrics, such as entanglement success and success of transmitting a bit of data. The fidelity was experimentally found to be excessively low, for this complex process of multi-pass distillation.</p>
},
	doi = {10.1109/QCE53715.2022.00143},
	author = {Germain, Julie and Dantu, Ram and Thompson, Mark and Dockendorf, Mark}
}
@conference {508,
	title = {Qubit Reset and Refresh: A Gamechanger for Random Number Generation},
	booktitle = {Twelveth ACM Conference on Data and Application Security and Privacy (CODASPY )},
	year = {2022},
	abstract = {<p>Generation of random binary numbers for cryptographic use is often addressed using pseudorandom number generating functions in compilers and specialized cryptographic packages. Using the IBM{\textquoteright}s Qiskit reset functionality, we were able to implement a straight-forward in-line Python function that returns a list of quantum-generated random numbers, by creating and executing a circuit on IBM quantum systems. We successfully created a list of 1000 1024-bit binary random numbers as well as a list of 40,000 25-bit binary random numbers for randomness testing, using the NIST Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications. The quantum-generated random data we tested showed very strong randomness, according to the NIST suite. Previously, IBM{\textquoteright}s quantum implementation required a single qubit for each bit of data generated in a circuit, making generation of large random numbers impractical. IBM{\textquoteright}s addition of the reset instruction eliminates this restriction and allows for the creation of functions that can generate a larger quantity of data-bit output, using only a small number of qubits.</p>
},
	keywords = {Qiskit, QRNG, Quantum Computing, Random Number Generator},
	doi = {10.1145/3508398.3519364},
	url = {https://dl.acm.org/doi/abs/10.1145/3508398.3519364},
	author = {Germain, Julie and Dantu, Ram and Thompson, Mark}
}
