TC
T.J. Coopmans
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Despite their widespread use in quantum computing and physics, the relative strengths and weaknesses of Matrix Product States (MPS), Decision Diagrams (DDs), and Restricted Boltzmann Machines (RBMs) remains poorly understood. We analytically compare the succinctness of these quantum state representations and analyze the complexity of key operations on them. To overcome shortcomings of the tractability measure, we introduce ‘rapidity’ conditions that identify when non-canonical representations efficiently simulate each other. Our results reveal that: 1. Most DD variants are redundant with respect to MPS in a strong sense; MPS is more rapid. 2. Only one DD variant, called LIMDD, and RBM have succinctness incomparable to MPS. 3. LIMDD and RBM seem to achieve this by sacrificing tractability of counting queries, as shown by a metatheorem on the conditional hardness of these queries.
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Despite their widespread use in quantum computing and physics, the relative strengths and weaknesses of Matrix Product States (MPS), Decision Diagrams (DDs), and Restricted Boltzmann Machines (RBMs) remains poorly understood. We analytically compare the succinctness of these quantum state representations and analyze the complexity of key operations on them. To overcome shortcomings of the tractability measure, we introduce ‘rapidity’ conditions that identify when non-canonical representations efficiently simulate each other. Our results reveal that: 1. Most DD variants are redundant with respect to MPS in a strong sense; MPS is more rapid. 2. Only one DD variant, called LIMDD, and RBM have succinctness incomparable to MPS. 3. LIMDD and RBM seem to achieve this by sacrificing tractability of counting queries, as shown by a metatheorem on the conditional hardness of these queries.
Journal article
(2025)
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Jan Li, Tim Coopmans, Patrick Emonts, Kenneth Goodenough, Jordi Tura, Evert van Nieuwenburg
Quantum repeaters play a crucial role in the effective distribution of entanglement over long distances. The nearest-future type of quantum repeater requires two operations: entanglement generation across neighbouring repeaters and entanglement swapping to promote short-range entanglement to long-range. For many hardware setups, these actions are probabilistic, leading to longer distribution times and incurred errors. Significant efforts have been vested in finding the optimal entanglement-distribution policy, i.e. the protocol specifying when a network node needs to generate or swap entanglement, such that the expected time to distribute long-distance entanglement is minimal. This problem is even more intricate in more realistic scenarios, especially when classical communication delays are taken into account. In this work, we formulate our problem as a Markov decision problem and use reinforcement learning (RL) to optimise over centralised strategies, where one designated node instructs other nodes which actions to perform. Contrary to most RL models, ours can be readily interpreted. Additionally, we introduce and evaluate a fixed local policy, the ‘predictive swap-asap’ policy, where nodes only coordinate with nearest neighbours. Compared to the straightforward generalisation of the common swap-asap policy to the scenario with classical communication effects, the ‘wait-for-broadcast swap-asap’ policy, both of the aforementioned entanglement-delivery policies are faster at high success probabilities. Our work showcases the merit of considering policies acting with incomplete information in the realistic case when classical communication effects are significant.
...
Quantum repeaters play a crucial role in the effective distribution of entanglement over long distances. The nearest-future type of quantum repeater requires two operations: entanglement generation across neighbouring repeaters and entanglement swapping to promote short-range entanglement to long-range. For many hardware setups, these actions are probabilistic, leading to longer distribution times and incurred errors. Significant efforts have been vested in finding the optimal entanglement-distribution policy, i.e. the protocol specifying when a network node needs to generate or swap entanglement, such that the expected time to distribute long-distance entanglement is minimal. This problem is even more intricate in more realistic scenarios, especially when classical communication delays are taken into account. In this work, we formulate our problem as a Markov decision problem and use reinforcement learning (RL) to optimise over centralised strategies, where one designated node instructs other nodes which actions to perform. Contrary to most RL models, ours can be readily interpreted. Additionally, we introduce and evaluate a fixed local policy, the ‘predictive swap-asap’ policy, where nodes only coordinate with nearest neighbours. Compared to the straightforward generalisation of the common swap-asap policy to the scenario with classical communication effects, the ‘wait-for-broadcast swap-asap’ policy, both of the aforementioned entanglement-delivery policies are faster at high success probabilities. Our work showcases the merit of considering policies acting with incomplete information in the realistic case when classical communication effects are significant.