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Electronic Colloquium on Computational Complexity

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REPORTS > KEYWORD > QUANTUM ALGORITHMS:
Reports tagged with quantum algorithms:
TR10-075 | 22nd April 2010
Ben Reichardt

Least span program witness size equals the general adversary lower bound on quantum query complexity

Span programs form a linear-algebraic model of computation, with span program "size" used in proving classical lower bounds. Quantum query complexity is a coherent generalization, for quantum algorithms, of classical decision-tree complexity. It is bounded below by a semi-definite program (SDP) known as the general adversary bound. We connect these ... more >>>


TR15-098 | 15th June 2015
Andris Ambainis, Kaspars Balodis, Aleksandrs Belovs, Troy Lee, Miklos Santha, Juris Smotrovs

Separations in Query Complexity Based on Pointer Functions

Revisions: 2

In 1986, Saks and Wigderson conjectured that the largest separation between deterministic and zero-error randomized
query complexity for a total boolean function is given by the function $f$ on $n=2^k$ bits defined by a complete binary tree
of NAND gates of depth $k$, which achieves $R_0(f) = O(D(f)^{0.7537\ldots})$. ... more >>>


TR19-061 | 16th April 2019
Scott Aaronson, Daniel Grier, Luke Schaeffer

A Quantum Query Complexity Trichotomy for Regular Languages

We present a trichotomy theorem for the quantum query complexity of regular languages. Every regular language has quantum query complexity $\Theta(1)$, $\tilde{\Theta}(\sqrt n)$, or $\Theta(n)$. The extreme uniformity of regular languages prevents them from taking any other asymptotic complexity. This is in contrast to even the context-free languages, which we ... more >>>


TR20-185 | 1st December 2020
Srinivasan Arunachalam, Alex Grilo, Tom Gur, Igor Oliveira, Aarthi Sundaram

Quantum learning algorithms imply circuit lower bounds

Revisions: 1

We establish the first general connection between the design of quantum algorithms and circuit lower bounds. Specifically, let $\mathrm{C}$ be a class of polynomial-size concepts, and suppose that $\mathrm{C}$ can be PAC-learned with membership queries under the uniform distribution with error $1/2 - \gamma$ by a time $T$ quantum algorithm. ... more >>>


TR21-174 | 29th November 2021
Tom Gur, Min-Hsiu Hsieh, Sathyawageeswar Subramanian

Sublinear quantum algorithms for estimating von Neumann entropy

Entropy is a fundamental property of both classical and quantum systems, spanning myriad theoretical and practical applications in physics and computer science. We study the problem of obtaining estimates to within a multiplicative factor $\gamma>1$ of the Shannon entropy of probability distributions and the von Neumann entropy of mixed quantum ... more >>>


TR22-177 | 7th December 2022
Vahid Reza Asadi, Alexander Golovnev, Tom Gur, Igor Shinkar, Sathyawageeswar Subramanian

Quantum Worst-Case to Average-Case Reductions for All Linear Problems

We study the problem of designing worst-case to average-case reductions for quantum algorithms. For all linear problems, we provide an explicit and efficient transformation of quantum algorithms that are only correct on a small (even sub-constant) fraction of their inputs into ones that are correct on all inputs. This stands ... more >>>




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