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20142026
most citedExperimental demonstration of information to energy conversion in a quantum system at the Landauer Limit

133 citations · 188 across the 16 of their papers we have counts for

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Showing 2024Show all

6 papers · 1 filter

quant-ph2024★ 1 cited

Tensor-network decoders for process tensor descriptions of non-Markovian noise

Fumiyoshi Kobayashi, Hidetaka Manabe, Gregory A. L. White +3

Quantum error correction (QEC) is essential for fault-tolerant quantum computation. Often in QEC errors are assumed to be independent and identically distributed and can be discret…

quant-ph2024

Practical learning of multi-time statistics in open quantum systems

Gregory A. L. White, Lloyd C. L. Hollenberg, Charles D. Hill +1

Randomised measurements can efficiently characterise many-body quantum states by learning the expectation values of observables with low Pauli weights. In this paper, we generalise…

quant-ph2024★ 9 cited

Quantum master equation from the eigenstate thermalization hypothesis

Peter O'Donovan, Philipp Strasberg, Kavan Modi +2

We use the eigenstate thermalization hypothesis to derive a quantum master equation for a system weakly coupled to a chaotic finite-sized bath prepared in a pure state. We show tha…

quant-ph2024★ 7 cited

Detrimental non-Markovian errors for surface code memory

John F Kam, Spiro Gicev, Kavan Modi +2

The realization of fault-tolerant quantum computers hinges on effective quantum error correction protocols, whose performance significantly relies on the nature of the underlying n…

quant-ph2024

Quantum HodgeRank: Topology-Based Rank Aggregation on Quantum Computers

Caesnan M. G. Leditto, Angus Southwell, Behnam Tonekaboni +2

HodgeRank generalizes ranking algorithms, e.g. Google PageRank, to rank alternatives based on real-world (often incomplete) data using graphs and discrete exterior calculus. It ana…

quant-ph2024★ 8 cited

Adversarial Robustness Guarantees for Quantum Classifiers

Neil Dowling, Maxwell T. West, Angus Southwell +4

Despite their ever more widespread deployment throughout society, machine learning algorithms remain critically vulnerable to being spoofed by subtle adversarial tampering with the…