513 research outputs found
Depth-Independent Lower bounds on the Communication Complexity of Read-Once Boolean Formulas
We show lower bounds of and on the
randomized and quantum communication complexity, respectively, of all
-variable read-once Boolean formulas. Our results complement the recent
lower bound of by Leonardos and Saks and
by Jayram, Kopparty and Raghavendra for
randomized communication complexity of read-once Boolean formulas with depth
. We obtain our result by "embedding" either the Disjointness problem or its
complement in any given read-once Boolean formula.Comment: 5 page
Data Structures in Classical and Quantum Computing
This survey summarizes several results about quantum computing related to
(mostly static) data structures. First, we describe classical data structures
for the set membership and the predecessor search problems: Perfect Hash tables
for set membership by Fredman, Koml\'{o}s and Szemer\'{e}di and a data
structure by Beame and Fich for predecessor search. We also prove results about
their space complexity (how many bits are required) and time complexity (how
many bits have to be read to answer a query). After that, we turn our attention
to classical data structures with quantum access. In the quantum access model,
data is stored in classical bits, but they can be accessed in a quantum way: We
may read several bits in superposition for unit cost. We give proofs for lower
bounds in this setting that show that the classical data structures from the
first section are, in some sense, asymptotically optimal - even in the quantum
model. In fact, these proofs are simpler and give stronger results than
previous proofs for the classical model of computation. The lower bound for set
membership was proved by Radhakrishnan, Sen and Venkatesh and the result for
the predecessor problem by Sen and Venkatesh. Finally, we examine fully quantum
data structures. Instead of encoding the data in classical bits, we now encode
it in qubits. We allow any unitary operation or measurement in order to answer
queries. We describe one data structure by de Wolf for the set membership
problem and also a general framework using fully quantum data structures in
quantum walks by Jeffery, Kothari and Magniez
Fourier Growth of Parity Decision Trees
We prove that for every parity decision tree of depth d on n variables, the sum of absolute values of Fourier coefficients at level ? is at most d^{?/2} ? O(? ? log(n))^?. Our result is nearly tight for small values of ? and extends a previous Fourier bound for standard decision trees by Sherstov, Storozhenko, and Wu (STOC, 2021).
As an application of our Fourier bounds, using the results of Bansal and Sinha (STOC, 2021), we show that the k-fold Forrelation problem has (randomized) parity decision tree complexity ??(n^{1-1/k}), while having quantum query complexity ? k/2?.
Our proof follows a random-walk approach, analyzing the contribution of a random path in the decision tree to the level-? Fourier expression. To carry the argument, we apply a careful cleanup procedure to the parity decision tree, ensuring that the value of the random walk is bounded with high probability. We observe that step sizes for the level-? walks can be computed by the intermediate values of level ? ?-1 walks, which calls for an inductive argument. Our approach differs from previous proofs of Tal (FOCS, 2020) and Sherstov, Storozhenko, and Wu (STOC, 2021) that relied on decompositions of the tree. In particular, for the special case of standard decision trees we view our proof as slightly simpler and more intuitive.
In addition, we prove a similar bound for noisy decision trees of cost at most d - a model that was recently introduced by Ben-David and Blais (FOCS, 2020)
A composition theorem for parity kill number
In this work, we study the parity complexity measures
and .
is the \emph{parity kill number} of , the
fewest number of parities on the input variables one has to fix in order to
"kill" , i.e. to make it constant. is the depth
of the shortest \emph{parity decision tree} which computes . These
complexity measures have in recent years become increasingly important in the
fields of communication complexity \cite{ZS09, MO09, ZS10, TWXZ13} and
pseudorandomness \cite{BK12, Sha11, CT13}.
Our main result is a composition theorem for .
The -th power of , denoted , is the function which results
from composing with itself times. We prove that if is not a parity
function, then In other words, the parity kill number of
is essentially supermultiplicative in the \emph{normal} kill number of
(also known as the minimum certificate complexity).
As an application of our composition theorem, we show lower bounds on the
parity complexity measures of and . Here is the sort function due to Ambainis \cite{Amb06},
and is Kushilevitz's hemi-icosahedron function \cite{NW95}. In
doing so, we disprove a conjecture of Montanaro and Osborne \cite{MO09} which
had applications to communication complexity and computational learning theory.
In addition, we give new lower bounds for conjectures of \cite{MO09,ZS10} and
\cite{TWXZ13}
Separations in Query Complexity Based on Pointer Functions
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 on bits defined by a complete
binary tree of NAND gates of depth , which achieves . We show this is false by giving an example of a total
boolean function on bits whose deterministic query complexity is
while its zero-error randomized query complexity is . We further show that the quantum query complexity of the same
function is , giving the first example of a total function
with a super-quadratic gap between its quantum and deterministic query
complexities.
We also construct a total boolean function on variables that has
zero-error randomized query complexity and bounded-error
randomized query complexity . This is the first
super-linear separation between these two complexity measures. The exact
quantum query complexity of the same function is .
These two functions show that the relations and are optimal, up to poly-logarithmic factors. Further
variations of these functions give additional separations between other query
complexity measures: a cubic separation between and , a -power
separation between and , and a 4th power separation between
approximate degree and bounded-error randomized query complexity.
All of these examples are variants of a function recently introduced by
\goos, Pitassi, and Watson which they used to separate the unambiguous
1-certificate complexity from deterministic query complexity and to resolve the
famous Clique versus Independent Set problem in communication complexity.Comment: 25 pages, 6 figures. Version 3 improves separation between Q_E and
R_0 and updates reference
Top-Down Induction of Decision Trees: Rigorous Guarantees and Inherent Limitations
Consider the following heuristic for building a decision tree for a function
. Place the most influential variable of
at the root, and recurse on the subfunctions and on the
left and right subtrees respectively; terminate once the tree is an
-approximation of . We analyze the quality of this heuristic,
obtaining near-matching upper and lower bounds:
Upper bound: For every with decision tree size and every
, this heuristic builds a decision tree of size
at most .
Lower bound: For every and , there is an with decision tree size such that
this heuristic builds a decision tree of size .
We also obtain upper and lower bounds for monotone functions:
and
respectively. The lower bound disproves conjectures of Fiat and Pechyony (2004)
and Lee (2009).
Our upper bounds yield new algorithms for properly learning decision trees
under the uniform distribution. We show that these algorithms---which are
motivated by widely employed and empirically successful top-down decision tree
learning heuristics such as ID3, C4.5, and CART---achieve provable guarantees
that compare favorably with those of the current fastest algorithm (Ehrenfeucht
and Haussler, 1989). Our lower bounds shed new light on the limitations of
these heuristics.
Finally, we revisit the classic work of Ehrenfeucht and Haussler. We extend
it to give the first uniform-distribution proper learning algorithm that
achieves polynomial sample and memory complexity, while matching its
state-of-the-art quasipolynomial runtime
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