Quantum Sampling Algorithms for Near-Term Devices

• URL: http://arxiv.org/abs/2005.14059v3
• Date: Tue, 7 Sep 2021 11:42:52 GMT
• Title: Quantum Sampling Algorithms for Near-Term Devices
• Authors: Dominik S. Wild, Dries Sels, Hannes Pichler, Cristian Zanoci, Mikhail D. Lukin
• Abstract summary: We introduce a family of quantum algorithms that provide unbiased samples by encoding the entire Gibbs distribution. We show that this approach leads to a speedup over a classical Markov chain algorithm. It opens the door to exploring potentially useful sampling algorithms on near-term quantum devices.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Efficient sampling from a classical Gibbs distribution is an important computational problem with applications ranging from statistical physics over Monte Carlo and optimization algorithms to machine learning. We introduce a family of quantum algorithms that provide unbiased samples by preparing a state encoding the entire Gibbs distribution. We show that this approach leads to a speedup over a classical Markov chain algorithm for several examples including the Ising model and sampling from weighted independent sets of two different graphs. Our approach connects computational complexity with phase transitions, providing a physical interpretation of quantum speedup. Moreover, it opens the door to exploring potentially useful sampling algorithms on near-term quantum devices as the algorithm for sampling from independent sets on certain graphs can be naturally implemented using Rydberg atom arrays.

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