3,804 research outputs found
Formal Proofs for Nonlinear Optimization
We present a formally verified global optimization framework. Given a
semialgebraic or transcendental function and a compact semialgebraic domain
, we use the nonlinear maxplus template approximation algorithm to provide a
certified lower bound of over . This method allows to bound in a modular
way some of the constituents of by suprema of quadratic forms with a well
chosen curvature. Thus, we reduce the initial goal to a hierarchy of
semialgebraic optimization problems, solved by sums of squares relaxations. Our
implementation tool interleaves semialgebraic approximations with sums of
squares witnesses to form certificates. It is interfaced with Coq and thus
benefits from the trusted arithmetic available inside the proof assistant. This
feature is used to produce, from the certificates, both valid underestimators
and lower bounds for each approximated constituent. The application range for
such a tool is widespread; for instance Hales' proof of Kepler's conjecture
yields thousands of multivariate transcendental inequalities. We illustrate the
performance of our formal framework on some of these inequalities as well as on
examples from the global optimization literature.Comment: 24 pages, 2 figures, 3 table
COLAB : a hybrid knowledge representation and compilation laboratory
Knowledge bases for real-world domains such as mechanical engineering require expressive and efficient representation and processing tools. We pursue a declarative-compilative approach to knowledge engineering. While Horn logic (as implemented in PROLOG) is well-suited for representing relational clauses, other kinds of declarative knowledge call for hybrid extensions: functional dependencies and higher-order knowledge should be modeled directly. Forward (bottom-up) reasoning should be integrated with backward (top-down) reasoning. Constraint propagation should be used wherever possible instead of search-intensive resolution. Taxonomic knowledge should be classified into an intuitive subsumption hierarchy. Our LISP-based tools provide direct translators of these declarative representations into abstract machines such as an extended Warren Abstract Machine (WAM) and specialized inference engines that are interfaced to each other. More importantly, we provide source-to-source transformers between various knowledge types, both for user convenience and machine efficiency. These formalisms with their translators and transformers have been developed as part of COLAB, a compilation laboratory for studying what we call, respectively, "vertical\u27; and "horizontal\u27; compilation of knowledge, as well as for exploring the synergetic collaboration of the knowledge representation formalisms. A case study in the realm of mechanical engineering has been an important driving force behind the development of COLAB. It will be used as the source of examples throughout the paper when discussing the enhanced formalisms, the hybrid representation architecture, and the compilers
Quantum-assisted quantum compiling
Compiling quantum algorithms for near-term quantum computers (accounting for
connectivity and native gate alphabets) is a major challenge that has received
significant attention both by industry and academia. Avoiding the exponential
overhead of classical simulation of quantum dynamics will allow compilation of
larger algorithms, and a strategy for this is to evaluate an algorithm's cost
on a quantum computer. To this end, we propose a variational hybrid
quantum-classical algorithm called quantum-assisted quantum compiling (QAQC).
In QAQC, we use the overlap between a target unitary and a trainable
unitary as the cost function to be evaluated on the quantum computer. More
precisely, to ensure that QAQC scales well with problem size, our cost involves
not only the global overlap but also the local
overlaps with respect to individual qubits. We introduce novel short-depth
quantum circuits to quantify the terms in our cost function, and we prove that
our cost cannot be efficiently approximated with a classical algorithm under
reasonable complexity assumptions. We present both gradient-free and
gradient-based approaches to minimizing this cost. As a demonstration of QAQC,
we compile various one-qubit gates on IBM's and Rigetti's quantum computers
into their respective native gate alphabets. Furthermore, we successfully
simulate QAQC up to a problem size of 9 qubits, and these simulations highlight
both the scalability of our cost function as well as the noise resilience of
QAQC. Future applications of QAQC include algorithm depth compression,
black-box compiling, noise mitigation, and benchmarking.Comment: 19 + 10 pages, 14 figures. Added larger scale implementations and
proof that cost function is DQC1-har
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