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This is an announcement of Danylo Lykov's Candidacy Exam.<br>
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Candidate: Danylo Lykov<br>
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Date: Tuesday, May 28, 2024<br>
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Time: 2 pm CT<br>
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Location: JCL 298<br>
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Title: Large-scale Tensor Network contractions for quantum algorithms<br>
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Abstract: "Tensor networks allow to model probabilistic processes over many variables. Quantum computing is built on using complex numbers in place of conventional probability, which is an easy change to add to tensor networks. Despite the availability of efficient
tools for physics simulations, simulating quantum circuits presents unique challenges, which I address in this work, specifically using the Quantum Approximate Optimization Algorithm (QAOA) as an example.<br>
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In this work I start by delving into two basic steps of using TNs for quantum simulation: creating a TN from quantum circuit and efficiently computing inference from (contracting) a TN. Both steps have interesting optimizations that allow for orders of magnitude
cost reduction. This also includes discussion on how to contract TNs on GPUs.<br>
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Tensor networks give a particular advantage for some special cases of quantum simulations, which involve calculating expectation values for shallow circuits. This allowed me to use TN simulations to study performance of QAOA and compare it against classical
counterparts.<br>
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Finally, I examine various methods to reduce the memory footprint of simulations at the expense of simulation quality. These methods can simulate larger circuits but depend not only on the circuit connectivity, but on the gate parameters. As quantum circuit
complexity increases, continued simulation efforts will inevitably require some compromise in quality."<br>
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Advisors: Fred Chong, Yuri Alexeev<br>
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Committee Members: Fred Chong, Yuri Alexeev, and Robert Rand</div>
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