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This is an announcement of Danylo Lykov's MS Presentation<br class="">
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Candidate: Danylo Lykov<br class="">
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Date: Thursday, February 08, 2024<br class="">
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Time: 2 pm CST<br class="">
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Remote Location: <a href="https://uchicago.zoom.us/j/96585809367?pwd=ZmwvVGtFVjgzdTB6Z0VwVGNHWnQ5UT09" class="">https://uchicago.zoom.us/j/96585809367?pwd=ZmwvVGtFVjgzdTB6Z0VwVGNHWnQ5UT09</a> Meeting ID: 965 8580 9367 Passcode: 235711<br class="">
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Location: JCL 298<br class="">
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Title: Large-scale Tensor Network quantum algorithm simulator<br class="">
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Abstract: As quantum computing field is starting to reach the realm of advantage over classical algorithms, the tools used for simulating quantum circuits encounter increasingly complex challenges in designing and evaluating quantum algorithms. In this context,
tensor networks, which have become a standard method for simulations in various areas of physics, from many-body quantum physics to quantum gravity, offer a natural approach. 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 as an example.<br class="">
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I begin by describing various methods for constructing a tensor network that represents a quantum circuit and the differences between these tensor networks. I demonstrate that minor changes in circuit gate decomposition can lead to significant reductions in
computational cost. Moreover, further optimizations are possible when calculating the expectation value of a local observable.<br class="">
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Next, I discuss how to optimally contract a tensor network, given a set of classical hardware resources. I discuss different parallelization approaches that can enhance simulation performance. I also show how using GPUs may necessitate dynamic allocation of
tensor contraction tasks to mitigate the impact of the GPU overhead.<br class="">
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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. When the quality of these simulations falls behind that of quantum hardware, we may be on the brink of truly entering the era of practical quantum computing.<br class="">
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Advisors: Fred Chong, Yuri Alexeev<br class="">
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Committee Members: Fred Chong, Yuri Alexeev, and Robert Rand<br class="">
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