301,791 research outputs found
Some results on circuit depth
An important problem in theoretical computer science is to develop methods for estimating the complexity of finite functions. For many familiar functions there remain important gaps between the best known lower and upper bound we investigate the inherent complexity of Boolean functional taking circuits as our model of computation and depth (or delay)to be the measure of complexity. The relevance of circuits as a model of computation for Boolean functions stems from the fact that Turing machine computations may be efficiently simulated by circuits. Important relations among various measures of circuit complexity are btained as well as bounds on the maximum depth of any function and of any monotone function. We then give a detailed account of the complexity of NAND circuits for several important functions and pursue an analysis of the important set of symmetric functions. A number of gap theorems for symmetric functions are exhibited and these are contrasted with uniform hierarchies for several large sets of functions.
Finally, we describe several short formulae for threshold
functions
Depth-Optimized Reversible Circuit Synthesis
In this paper, simultaneous reduction of circuit depth and synthesis cost of
reversible circuits in quantum technologies with limited interaction is
addressed. We developed a cycle-based synthesis algorithm which uses negative
controls and limited distance between gate lines. To improve circuit depth, a
new parallel structure is introduced in which before synthesis a set of
disjoint cycles are extracted from the input specification and distributed into
some subsets. The cycles of each subset are synthesized independently on
different sets of ancillae. Accordingly, each disjoint set can be synthesized
by different synthesis methods. Our analysis shows that the best worst-case
synthesis cost of reversible circuits in the linear nearest neighbor
architecture is improved by the proposed approach. Our experimental results
reveal the effectiveness of the proposed approach to reduce cost and circuit
depth for several benchmarks.Comment: 13 pages, 6 figures, 5 tables; Quantum Information Processing (QINP)
journal, 201
Information Theory and Noisy Computation
We report on two types of results. The first is a study of the rate of decay of information carried by a signal which is being propagated over a noisy channel. The second is a series of lower bounds on the depth, size, and component reliability of noisy logic circuits which are required to compute some function reliably. The arguments used for the circuit results are information-theoretic, and in particular, the signal decay result is essential to the depth lower bound. Our first result can be viewed as a quantified version of the data processing lemma, for the case of Boolean random variables
Quantified Derandomization of Linear Threshold Circuits
One of the prominent current challenges in complexity theory is the attempt
to prove lower bounds for , the class of constant-depth, polynomial-size
circuits with majority gates. Relying on the results of Williams (2013), an
appealing approach to prove such lower bounds is to construct a non-trivial
derandomization algorithm for . In this work we take a first step towards
the latter goal, by proving the first positive results regarding the
derandomization of circuits of depth .
Our first main result is a quantified derandomization algorithm for
circuits with a super-linear number of wires. Specifically, we construct an
algorithm that gets as input a circuit over input bits with
depth and wires, runs in almost-polynomial-time, and
distinguishes between the case that rejects at most inputs
and the case that accepts at most inputs. In fact, our
algorithm works even when the circuit is a linear threshold circuit, rather
than just a circuit (i.e., is a circuit with linear threshold gates,
which are stronger than majority gates).
Our second main result is that even a modest improvement of our quantified
derandomization algorithm would yield a non-trivial algorithm for standard
derandomization of all of , and would consequently imply that
. Specifically, if there exists a quantified
derandomization algorithm that gets as input a circuit with depth
and wires (rather than wires), runs in time at
most , and distinguishes between the case that rejects at
most inputs and the case that accepts at most
inputs, then there exists an algorithm with running time
for standard derandomization of .Comment: Changes in this revision: An additional result (a PRG for quantified
derandomization of depth-2 LTF circuits); rewrite of some of the exposition;
minor correction
Lifting for Constant-Depth Circuits and Applications to MCSP
Lifting arguments show that the complexity of a function in one model is essentially that of a related function (often the composition of the original function with a small function called a gadget) in a more powerful model. Lifting has been used to prove strong lower bounds in communication complexity, proof complexity, circuit complexity and many other areas.
We present a lifting construction for constant depth unbounded fan-in circuits. Given a function f, we construct a function g, so that the depth d+1 circuit complexity of g, with a certain restriction on bottom fan-in, is controlled by the depth d circuit complexity of f, with the same restriction. The function g is defined as f composed with a parity function. With some quantitative losses, average-case and general depth-d circuit complexity can be reduced to circuit complexity with this bottom fan-in restriction. As a consequence, an algorithm to approximate the depth d (for any d > 3) circuit complexity of given (truth tables of) Boolean functions yields an algorithm for approximating the depth 3 circuit complexity of functions, i.e., there are quasi-polynomial time mapping reductions between various gap-versions of AC?-MCSP. Our lifting results rely on a blockwise switching lemma that may be of independent interest.
We also show some barriers on improving the efficiency of our reductions: such improvements would yield either surprisingly efficient algorithms for MCSP or stronger than known AC? circuit lower bounds
Increasing the representation accuracy of quantum simulations of chemistry without extra quantum resources
Proposals for near-term experiments in quantum chemistry on quantum computers
leverage the ability to target a subset of degrees of freedom containing the
essential quantum behavior, sometimes called the active space. This
approximation allows one to treat more difficult problems using fewer qubits
and lower gate depths than would otherwise be possible. However, while this
approximation captures many important qualitative features, it may leave the
results wanting in terms of absolute accuracy (basis error) of the
representation. In traditional approaches, increasing this accuracy requires
increasing the number of qubits and an appropriate increase in circuit depth as
well. Here we introduce a technique requiring no additional qubits or circuit
depth that is able to remove much of this approximation in favor of additional
measurements. The technique is constructed and analyzed theoretically, and some
numerical proof of concept calculations are shown. As an example, we show how
to achieve the accuracy of a 20 qubit representation using only 4 qubits and a
modest number of additional measurements for a simple hydrogen molecule. We
close with an outlook on the impact this technique may have on both near-term
and fault-tolerant quantum simulations
The Encoding and Decoding Complexities of Entanglement-Assisted Quantum Stabilizer Codes
Quantum error-correcting codes are used to protect quantum information from
decoherence. A raw state is mapped, by an encoding circuit, to a codeword so
that the most likely quantum errors from a noisy quantum channel can be removed
after a decoding process.
A good encoding circuit should have some desired features, such as low depth,
few gates, and so on. In this paper, we show how to practically implement an
encoding circuit of gate complexity for an
quantum stabilizer code with the help of pairs of maximally-entangled
states. For the special case of an stabilizer code with , the
encoding complexity is , which is previously known to be
. For this suggests that the benefits from shared
entanglement come at an additional cost of encoding complexity.
Finally we discuss decoding of entanglement-assisted quantum stabilizer codes
and extend previously known computational hardness results on decoding quantum
stabilizer codes.Comment: accepted by the 2019 IEEE International Symposium on Information
Theory (ISIT2019
AND and/or OR: Uniform Polynomial-Size Circuits
We investigate the complexity of uniform OR circuits and AND circuits of
polynomial-size and depth. As their name suggests, OR circuits have OR gates as
their computation gates, as well as the usual input, output and constant (0/1)
gates. As is the norm for Boolean circuits, our circuits have multiple sink
gates, which implies that an OR circuit computes an OR function on some subset
of its input variables. Determining that subset amounts to solving a number of
reachability questions on a polynomial-size directed graph (which input gates
are connected to the output gate?), taken from a very sparse set of graphs.
However, it is not obvious whether or not this (restricted) reachability
problem can be solved, by say, uniform AC^0 circuits (constant depth,
polynomial-size, AND, OR, NOT gates). This is one reason why characterizing the
power of these simple-looking circuits in terms of uniform classes turns out to
be intriguing. Another is that the model itself seems particularly natural and
worthy of study.
Our goal is the systematic characterization of uniform polynomial-size OR
circuits, and AND circuits, in terms of known uniform machine-based complexity
classes. In particular, we consider the languages reducible to such uniform
families of OR circuits, and AND circuits, under a variety of reduction types.
We give upper and lower bounds on the computational power of these language
classes. We find that these complexity classes are closely related to tallyNL,
the set of unary languages within NL, and to sets reducible to tallyNL.
Specifically, for a variety of types of reductions (many-one, conjunctive truth
table, disjunctive truth table, truth table, Turing) we give characterizations
of languages reducible to OR circuit classes in terms of languages reducible to
tallyNL classes. Then, some of these OR classes are shown to coincide, and some
are proven to be distinct. We give analogous results for AND circuits. Finally,
for many of our OR circuit classes, and analogous AND circuit classes, we prove
whether or not the two classes coincide, although we leave one such inclusion
open.Comment: In Proceedings MCU 2013, arXiv:1309.104
Does qubit connectivity impact quantum circuit complexity?
Some physical implementation schemes of quantum computing can apply two-qubit
gates only on certain pairs of qubits. These connectivity constraints are
commonly viewed as a significant disadvantage. For example, compiling an
unrestricted -qubit quantum circuit to one with poor qubit connectivity,
such as a 1D chain, usually results in a blowup of depth by and size
by . It is appealing to conjecture that this overhead is unavoidable -- a
random circuit on qubits has two-qubit gates in each layer and
a constant fraction of them act on qubits separated by distance .
While it is known that almost all -qubit unitary operations need quantum
circuits of depth and size to realize with
all-to-all qubit connectivity, in this paper, we show that all -qubit
unitary operations can be implemented by quantum circuits of depth
and size even under {1D chain} qubit connectivity constraint.
We extend this result and investigate qubit connectivity in three directions.
First, we consider more general connectivity graphs and show that the circuit
size can always be made as long as the graph is connected. For circuit
depth, we study -dimensional grids, complete -ary trees and expander
graphs, and show results similar to the 1D chain. Second, we consider the case
when ancillary qubits are available. We show that, with ancilla, the circuit
depth can be made polynomial, and the space-depth trade-off is not impaired by
connectivity constraints unless we have exponentially many ancillary qubits.
Third, we obtain nearly optimal results on special families of unitaries,
including diagonal unitaries, 2-by-2 block diagonal unitaries, and Quantum
State Preparation (QSP) unitaries, the last being a fundamental task used in
many quantum algorithms for machine learning and linear algebra
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