1,710 research outputs found
Quantitative Robustness Analysis of Quantum Programs (Extended Version)
Quantum computation is a topic of significant recent interest, with practical
advances coming from both research and industry. A major challenge in quantum
programming is dealing with errors (quantum noise) during execution. Because
quantum resources (e.g., qubits) are scarce, classical error correction
techniques applied at the level of the architecture are currently
cost-prohibitive. But while this reality means that quantum programs are almost
certain to have errors, there as yet exists no principled means to reason about
erroneous behavior. This paper attempts to fill this gap by developing a
semantics for erroneous quantum while-programs, as well as a logic for
reasoning about them. This logic permits proving a property we have identified,
called -robustness, which characterizes possible "distance" between
an ideal program and an erroneous one. We have proved the logic sound, and
showed its utility on several case studies, notably: (1) analyzing the
robustness of noisy versions of the quantum Bernoulli factory (QBF) and quantum
walk (QW); (2) demonstrating the (in)effectiveness of different error
correction schemes on single-qubit errors; and (3) analyzing the robustness of
a fault-tolerant version of QBF.Comment: 34 pages, LaTeX; v2: fixed typo
Symbolic and analytic techniques for resource analysis of Java bytecode
Recent work in resource analysis has translated the idea of amortised resource analysis to imperative languages using a program logic that allows mixing of assertions about heap shapes, in the tradition of separation logic, and assertions about consumable resources. Separately, polyhedral methods have been used to calculate bounds on numbers of iterations in loop-based programs. We are attempting to combine these ideas to deal with Java programs involving both data structures and loops, focusing on the bytecode level rather than on source code
Credimus
We believe that economic design and computational complexity---while already
important to each other---should become even more important to each other with
each passing year. But for that to happen, experts in on the one hand such
areas as social choice, economics, and political science and on the other hand
computational complexity will have to better understand each other's
worldviews.
This article, written by two complexity theorists who also work in
computational social choice theory, focuses on one direction of that process by
presenting a brief overview of how most computational complexity theorists view
the world. Although our immediate motivation is to make the lens through which
complexity theorists see the world be better understood by those in the social
sciences, we also feel that even within computer science it is very important
for nontheoreticians to understand how theoreticians think, just as it is
equally important within computer science for theoreticians to understand how
nontheoreticians think
Computer-aided verification in mechanism design
In mechanism design, the gold standard solution concepts are dominant
strategy incentive compatibility and Bayesian incentive compatibility. These
solution concepts relieve the (possibly unsophisticated) bidders from the need
to engage in complicated strategizing. While incentive properties are simple to
state, their proofs are specific to the mechanism and can be quite complex.
This raises two concerns. From a practical perspective, checking a complex
proof can be a tedious process, often requiring experts knowledgeable in
mechanism design. Furthermore, from a modeling perspective, if unsophisticated
agents are unconvinced of incentive properties, they may strategize in
unpredictable ways.
To address both concerns, we explore techniques from computer-aided
verification to construct formal proofs of incentive properties. Because formal
proofs can be automatically checked, agents do not need to manually check the
properties, or even understand the proof. To demonstrate, we present the
verification of a sophisticated mechanism: the generic reduction from Bayesian
incentive compatible mechanism design to algorithm design given by Hartline,
Kleinberg, and Malekian. This mechanism presents new challenges for formal
verification, including essential use of randomness from both the execution of
the mechanism and from the prior type distributions. As an immediate
consequence, our work also formalizes Bayesian incentive compatibility for the
entire family of mechanisms derived via this reduction. Finally, as an
intermediate step in our formalization, we provide the first formal
verification of incentive compatibility for the celebrated
Vickrey-Clarke-Groves mechanism
Polynomial Invariants for Affine Programs
We exhibit an algorithm to compute the strongest polynomial (or algebraic)
invariants that hold at each location of a given affine program (i.e., a
program having only non-deterministic (as opposed to conditional) branching and
all of whose assignments are given by affine expressions). Our main tool is an
algebraic result of independent interest: given a finite set of rational square
matrices of the same dimension, we show how to compute the Zariski closure of
the semigroup that they generate
Generating Non-Linear Interpolants by Semidefinite Programming
Interpolation-based techniques have been widely and successfully applied in
the verification of hardware and software, e.g., in bounded-model check- ing,
CEGAR, SMT, etc., whose hardest part is how to synthesize interpolants. Various
work for discovering interpolants for propositional logic, quantifier-free
fragments of first-order theories and their combinations have been proposed.
However, little work focuses on discovering polynomial interpolants in the
literature. In this paper, we provide an approach for constructing non-linear
interpolants based on semidefinite programming, and show how to apply such
results to the verification of programs by examples.Comment: 22 pages, 4 figure
Perspectives for proof unwinding by programming languages techniques
In this chapter, we propose some future directions of work, potentially
beneficial to Mathematics and its foundations, based on the recent import of
methodology from the theory of programming languages into proof theory. This
scientific essay, written for the audience of proof theorists as well as the
working mathematician, is not a survey of the field, but rather a personal view
of the author who hopes that it may inspire future and fellow researchers
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