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Electronic Colloquium on Computational Complexity

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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

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 >>>

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