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Cumulative Memory Lower Bounds for Randomized and Quantum Computation

Authors Paul Beame , Niels Kornerup

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Author Details

Paul Beame
  • Computer Science & Engineering, University of Washington, Seattle, WA, USA
Niels Kornerup
  • Computer Science, University of Texas, Austin, TX, USA

Funding

  • Beame, Paul: Research supported by NSF grant CCF-2006359.

Acknowledgements

Many thanks to David Soloveichik for his guidance and contributions to our initial results.

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Paul Beame and Niels Kornerup. Cumulative Memory Lower Bounds for Randomized and Quantum Computation. In 50th International Colloquium on Automata, Languages, and Programming (ICALP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 261, pp. 17:1-17:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.ICALP.2023.17

Abstract

Cumulative memory - the sum of space used per step over the duration of a computation - is a fine-grained measure of time-space complexity that was introduced to analyze cryptographic applications like password hashing. It is a more accurate cost measure for algorithms that have infrequent spikes in memory usage and are run in environments such as cloud computing that allow dynamic allocation and de-allocation of resources during execution, or when many multiple instances of an algorithm are interleaved in parallel. We prove the first lower bounds on cumulative memory complexity for both sequential classical computation and quantum circuits. Moreover, we develop general paradigms for bounding cumulative memory complexity inspired by the standard paradigms for proving time-space tradeoff lower bounds that can only lower bound the maximum space used during an execution. The resulting lower bounds on cumulative memory that we obtain are just as strong as the best time-space tradeoff lower bounds, which are very often known to be tight. Although previous results for pebbling and random oracle models have yielded time-space tradeoff lower bounds larger than the cumulative memory complexity, our results show that in general computational models such separations cannot follow from known lower bound techniques and are not true for many functions. Among many possible applications of our general methods, we show that any classical sorting algorithm with success probability at least 1/poly(n) requires cumulative memory ̃ Ω(n²), any classical matrix multiplication algorithm requires cumulative memory Ω(n⁶/T), any quantum sorting circuit requires cumulative memory Ω(n³/T), and any quantum circuit that finds k disjoint collisions in a random function requires cumulative memory Ω(k³n/T²).

Subject Classification

ACM Subject Classification
  • Theory of computation → Oracles and decision trees
  • Theory of computation → Quantum query complexity
  • Theory of computation → Quantum complexity theory
Keywords
  • Cumulative memory complexity
  • time-space tradeoffs
  • branching programs
  • quantum lower bounds

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