Synthesizing Quantum-Circuit Optimizers

Near-term quantum computers are expected to work in an environment where each operation is noisy, with no error correction. Therefore, quantum-circuit optimizers are applied to minimize the number of noisy operations. Today, physicists are constantly experimenting with novel devices and architectures. For every new physical substrate and for every modification of a quantum computer, we need to modify or rewrite major pieces of the optimizer to run successful experiments. In this paper, we present QUESO, an efficient approach for automatically synthesizing a quantum-circuit optimizer for a given quantum device. For instance, in 1.2 minutes, QUESO can synthesize an optimizer with high-probability correctness guarantees for IBM computers that significantly outperforms leading compilers, such as IBM's Qiskit and TKET, on the majority (85%) of the circuits in a diverse benchmark suite. A number of theoretical and algorithmic insights underlie QUESO: (1) An algebraic approach for representing rewrite rules and their semantics. This facilitates reasoning about complex symbolic rewrite rules that are beyond the scope of existing techniques. (2) A fast approach for probabilistically verifying equivalence of quantum circuits by reducing the problem to a special form of polynomial identity testing. (3) A novel probabilistic data structure, called a polynomial identity filter (PIF), for efficiently synthesizing rewrite rules. (4) A beam-search-based algorithm that efficiently applies the synthesized symbolic rewrite rules to optimize quantum circuits.Comment: Full version of PLDI 2023 pape

Scaling Automatic Modular Verification

Automated software verification techniques, while widely successful, often suffer from scalability issues due to state-space explosion. In both automated and manual verification, modular approaches help scale verification by breaking verification problems into several easier-to-solve subproblems. These subproblems often involve discovering suitable invariants that can be used to help derive a proof that the entire system meets the desired specification. In this dissertation, I describe work toward developing modular automatic techniques for software verification in which such invariants are discovered automatically. These techniques notably involve exploiting the structure and syntax of both system components and/or their specifications in order to find useful invariants for scaling verification. I have developed techniques for several related kinds of verification problems: the verification of k-safety properties, the verification of safety properties for general single-threaded interprocedural programs, and the verification of information-flow security—a specific kind of 2-safety property. For each of these verification problems, I have implemented the developed techniques in a verification tool and compared the tool to existing state-of-the-art tools for solving the verification problem. Experimental results demonstrate that the developed techniques help scale verification over existing state-of-the-art and allow more verification problems to be solved automatically

Automating Modular Verification of Secure Information Flow

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Quantification of urbanization in relation to chronic diseases in developing countries: a systematic review

During and beyond the twentieth century, urbanization has represented a major demographic shift particularly in the developed world. The rapid urbanization experienced in the developing world brings increased mortality from lifestyle diseases such as cancer and cardiovascular disease. We set out to understand how urbanization has been measured in studies which examined chronic disease as an outcome. Following a pilot search of PUBMED, a full search strategy was developed to identify papers reporting the effect of urbanization in relation to chronic disease in the developing world. Full searches were conducted in MEDLINE, EMBASE, CINAHL, and GLOBAL HEALTH. Of the 868 titles identified in the initial search, nine studies met the final inclusion criteria. Five of these studies used demographic measures (such as population density) at an area level to measure urbanization. Four studies used more complicated summary measures of individual and area level data (such as distance from a city, occupation, home and land ownership) to define urbanization. The papers reviewed were limited by using simple area level summary measures (e.g., urban rural dichotomy) or having to rely on preexisting data at the individual level. Further work is needed to develop a measure of urbanization that treats urbanization as a process and which is sensitive enough to track changes in "urbanicity" and subsequent emergence of chronic disease risk factors and mortality