11,070 research outputs found
Circuit Depth Reductions
The best known size lower bounds against unrestricted circuits have remained
around for several decades. Moreover, the only known technique for proving
lower bounds in this model, gate elimination, is inherently limited to proving
lower bounds of less than . In this work, we propose a non-gate-elimination
approach for obtaining circuit lower bounds, via certain depth-three lower
bounds. We prove that every (unbounded-depth) circuit of size can be
expressed as an OR of -CNFs. For DeMorgan formulas, the best
known size lower bounds have been stuck at around for decades.
Under a plausible hypothesis about probabilistic polynomials, we show that
-size DeMorgan formulas have
-size depth-3 circuits which are approximate
sums of -degree polynomials over .
While these structural results do not immediately lead to new lower bounds,
they do suggest new avenues of attack on these longstanding lower bound
problems.
Our results complement the classical depth- reduction results of Valiant,
which show that logarithmic-depth circuits of linear size can be computed by an
OR of -CNFs, and slightly stronger results for
series-parallel circuits. It is known that no purely graph-theoretic reduction
could yield interesting depth-3 circuits from circuits of super-logarithmic
depth. We overcome this limitation (for small-size circuits) by taking into
account both the graph-theoretic and functional properties of circuits and
formulas.
We show that improvements of the following pseudorandom constructions imply
new circuit lower bounds: dispersers for varieties, correlation with constant
degree polynomials, matrix rigidity, and hardness for depth- circuits with
constant bottom fan-in
Algorithms and Lower Bounds in Circuit Complexity
Computational complexity theory aims to understand what problems can be efficiently solved by computation. This thesis studies computational complexity in the model of Boolean circuits. Boolean circuits provide a basic mathematical model for computation and play a central role in complexity theory, with important applications in separations of complexity classes, algorithm design, and pseudorandom constructions. In this thesis, we investigate various types of circuit models such as threshold circuits, Boolean formulas, and their extensions, focusing on obtaining complexity-theoretic lower bounds and algorithmic upper bounds for these circuits. (1) Algorithms and lower bounds for generalized threshold circuits: We extend the study of linear threshold circuits, circuits with gates computing linear threshold functions, to the more powerful model of polynomial threshold circuits where the gates can compute polynomial threshold functions. We obtain hardness and meta-algorithmic results for this circuit model, including strong average-case lower bounds, satisfiability algorithms, and derandomization algorithms for constant-depth polynomial threshold circuits with super-linear wire complexity. (2) Algorithms and lower bounds for enhanced formulas: We investigate the model of Boolean formulas whose leaf gates can compute complex functions. In particular, we study De Morgan formulas whose leaf gates are functions with "low communication complexity". Such gates can capture a broad class of functions including symmetric functions and polynomial threshold functions. We obtain new and improved results in terms of lower bounds and meta-algorithms (satisfiability, derandomization, and learning) for such enhanced formulas. (3) Circuit lower bounds for MCSP: We study circuit lower bounds for the Minimum Circuit Size Problem (MCSP), the fundamental problem of deciding whether a given function (in the form of a truth table) can be computed by small circuits. We get new and improved lower bounds for MCSP that nearly match the best-known lower bounds against several well-studied circuit models such as Boolean formulas and constant-depth circuits
NP-hardness of circuit minimization for multi-output functions
Can we design efficient algorithms for finding fast algorithms? This question is captured by various circuit minimization problems, and algorithms for the corresponding tasks have significant practical applications. Following the work of Cook and Levin in the early 1970s, a central question is whether minimizing the circuit size of an explicitly given function is NP-complete. While this is known to hold in restricted models such as DNFs, making progress with respect to more expressive classes of circuits has been elusive.
In this work, we establish the first NP-hardness result for circuit minimization of total functions in the setting of general (unrestricted) Boolean circuits. More precisely, we show that computing the minimum circuit size of a given multi-output Boolean function f : {0,1}^n ? {0,1}^m is NP-hard under many-one polynomial-time randomized reductions. Our argument builds on a simpler NP-hardness proof for the circuit minimization problem for (single-output) Boolean functions under an extended set of generators.
Complementing these results, we investigate the computational hardness of minimizing communication. We establish that several variants of this problem are NP-hard under deterministic reductions. In particular, unless ? = ??, no polynomial-time computable function can approximate the deterministic two-party communication complexity of a partial Boolean function up to a polynomial. This has consequences for the class of structural results that one might hope to show about the communication complexity of partial functions
Anticoncentration theorems for schemes showing a quantum speedup
One of the main milestones in quantum information science is to realise
quantum devices that exhibit an exponential computational advantage over
classical ones without being universal quantum computers, a state of affairs
dubbed quantum speedup, or sometimes "quantum computational supremacy". The
known schemes heavily rely on mathematical assumptions that are plausible but
unproven, prominently results on anticoncentration of random prescriptions. In
this work, we aim at closing the gap by proving two anticoncentration theorems
and accompanying hardness results, one for circuit-based schemes, the other for
quantum quench-type schemes for quantum simulations. Compared to the few other
known such results, these results give rise to a number of comparably simple,
physically meaningful and resource-economical schemes showing a quantum speedup
in one and two spatial dimensions. At the heart of the analysis are tools of
unitary designs and random circuits that allow us to conclude that universal
random circuits anticoncentrate as well as an embedding of known circuit-based
schemes in a 2D translation-invariant architecture.Comment: 12+2 pages, added applications sectio
- …