56 research outputs found
The Classical Complexity of Boson Sampling
We study the classical complexity of the exact Boson Sampling problem where
the objective is to produce provably correct random samples from a particular
quantum mechanical distribution. The computational framework was proposed by
Aaronson and Arkhipov in 2011 as an attainable demonstration of `quantum
supremacy', that is a practical quantum computing experiment able to produce
output at a speed beyond the reach of classical (that is non-quantum) computer
hardware. Since its introduction Boson Sampling has been the subject of intense
international research in the world of quantum computing. On the face of it,
the problem is challenging for classical computation. Aaronson and Arkhipov
show that exact Boson Sampling is not efficiently solvable by a classical
computer unless and the polynomial hierarchy collapses to
the third level.
The fastest known exact classical algorithm for the standard Boson Sampling
problem takes time to produce samples for a
system with input size and output modes, making it infeasible for
anything but the smallest values of and . We give an algorithm that is
much faster, running in time and
additional space. The algorithm is simple to implement and has low constant
factor overheads. As a consequence our classical algorithm is able to solve the
exact Boson Sampling problem for system sizes far beyond current photonic
quantum computing experimentation, thereby significantly reducing the
likelihood of achieving near-term quantum supremacy in the context of Boson
Sampling.Comment: 15 pages. To appear in SODA '1
The streaming -mismatch problem
We consider the streaming complexity of a fundamental task in approximate
pattern matching: the -mismatch problem. It asks to compute Hamming
distances between a pattern of length and all length- substrings of a
text for which the Hamming distance does not exceed a given threshold . In
our problem formulation, we report not only the Hamming distance but also, on
demand, the full \emph{mismatch information}, that is the list of mismatched
pairs of symbols and their indices. The twin challenges of streaming pattern
matching derive from the need both to achieve small working space and also to
guarantee that every arriving input symbol is processed quickly.
We present a streaming algorithm for the -mismatch problem which uses
bits of space and spends \ourcomplexity time on
each symbol of the input stream, which consists of the pattern followed by the
text. The running time almost matches the classic offline solution and the
space usage is within a logarithmic factor of optimal.
Our new algorithm therefore effectively resolves and also extends an open
problem first posed in FOCS'09. En route to this solution, we also give a
deterministic -bit encoding of all
the alignments with Hamming distance at most of a length- pattern within
a text of length . This secondary result provides an optimal solution to
a natural communication complexity problem which may be of independent
interest.Comment: 27 page
Cell-probe Lower Bounds for Dynamic Problems via a New Communication Model
In this paper, we develop a new communication model to prove a data structure
lower bound for the dynamic interval union problem. The problem is to maintain
a multiset of intervals over with integer coordinates,
supporting the following operations:
- insert(a, b): add an interval to , provided that
and are integers in ;
- delete(a, b): delete a (previously inserted) interval from
;
- query(): return the total length of the union of all intervals in
.
It is related to the two-dimensional case of Klee's measure problem. We prove
that there is a distribution over sequences of operations with
insertions and deletions, and queries, for which any data
structure with any constant error probability requires time
in expectation. Interestingly, we use the sparse set disjointness protocol of
H\aa{}stad and Wigderson [ToC'07] to speed up a reduction from a new kind of
nondeterministic communication games, for which we prove lower bounds.
For applications, we prove lower bounds for several dynamic graph problems by
reducing them from dynamic interval union
The k-mismatch problem revisited
We revisit the complexity of one of the most basic problems in pattern
matching. In the k-mismatch problem we must compute the Hamming distance
between a pattern of length m and every m-length substring of a text of length
n, as long as that Hamming distance is at most k. Where the Hamming distance is
greater than k at some alignment of the pattern and text, we simply output
"No".
We study this problem in both the standard offline setting and also as a
streaming problem. In the streaming k-mismatch problem the text arrives one
symbol at a time and we must give an output before processing any future
symbols. Our main results are as follows:
1) Our first result is a deterministic time offline algorithm for k-mismatch on a text of length n. This is a
factor of k improvement over the fastest previous result of this form from SODA
2000 by Amihood Amir et al.
2) We then give a randomised and online algorithm which runs in the same time
complexity but requires only space in total.
3) Next we give a randomised -approximation algorithm for the
streaming k-mismatch problem which uses
space and runs in worst-case time per
arriving symbol.
4) Finally we combine our new results to derive a randomised
space algorithm for the streaming k-mismatch problem
which runs in worst-case time per
arriving symbol. This improves the best previous space complexity for streaming
k-mismatch from FOCS 2009 by Benny Porat and Ely Porat by a factor of k. We
also improve the time complexity of this previous result by an even greater
factor to match the fastest known offline algorithm (up to logarithmic
factors)
The Dynamic k-Mismatch Problem
The text-to-pattern Hamming distances problem asks to compute the Hamming
distances between a given pattern of length and all length- substrings
of a given text of length . We focus on the -mismatch version of the
problem, where a distance needs to be returned only if it does not exceed a
threshold . We assume (in general, one can partition the text into
overlapping blocks). In this work, we show data structures for the dynamic
version of this problem supporting two operations: An update performs a
single-letter substitution in the pattern or the text, and a query, given an
index , returns the Hamming distance between the pattern and the text
substring starting at position , or reports that it exceeds .
First, we show a data structure with update and
query time. Then we show that update and query
time is also possible. These two provide an optimal trade-off for the dynamic
-mismatch problem with : we prove that, conditioned on the
strong 3SUM conjecture, one cannot simultaneously achieve
time for all operations.
For , we give another lower bound, conditioned on the Online
Matrix-Vector conjecture, that excludes algorithms taking
time per operation. This is tight for constant-sized alphabets: Clifford et al.
(STACS 2018) achieved time per operation in that case,
but with time per operation for large alphabets. We
improve and extend this result with an algorithm that, given ,
achieves update time and query
time . In particular, for , an appropriate choice
of yields time per operation, which is
when no threshold is provided
Scheduling Algorithms for Procrastinators
This paper presents scheduling algorithms for procrastinators, where the
speed that a procrastinator executes a job increases as the due date
approaches. We give optimal off-line scheduling policies for linearly
increasing speed functions. We then explain the computational/numerical issues
involved in implementing this policy. We next explore the online setting,
showing that there exist adversaries that force any online scheduling policy to
miss due dates. This impossibility result motivates the problem of minimizing
the maximum interval stretch of any job; the interval stretch of a job is the
job's flow time divided by the job's due date minus release time. We show that
several common scheduling strategies, including the "hit-the-highest-nail"
strategy beloved by procrastinators, have arbitrarily large maximum interval
stretch. Then we give the "thrashing" scheduling policy and show that it is a
\Theta(1) approximation algorithm for the maximum interval stretch.Comment: 12 pages, 3 figure
- …