Hampshire College

About: Hampshire College is a education organization based out in Amherst Center, Massachusetts, United States. It is known for research contribution in the topics: Genetic programming & Population. The organization has 461 authors who have published 998 publications receiving 40827 citations.

Genetic programming for quantum computers

Abstract: Genetic programming can be used to automatically discover algorithms for quantum computers that are more efficient than any classical computer algorithms for the same problems. In this paper we exhibit the first evolved betterthan-classical quantum algorithm, for Deutsch’s “early promise” problem. We also demonstrate a technique for evolving scalable quantum gate arrays and discuss other issues in the application of genetic programming to quantum computation and vice versa. 1. Quantum Computing Quantum computers are computational devices that use atomic-scale objects, for example 2-state particles, to store and manipulate information (Steane, 1997; for an elementary on-line tutorial see Braunstein, 1995; for an introduction for the general reader see Milburn, 1997). The physics of these devices allows them to do things that common digital (henceforth “classical”) computers cannot. Although quantum computers and classical computers appear to be bound by the same limits of Turing computability, physicists argue that quantum computers can solve certain problems using less resources (time and space) than classical computers are thought to require (Jozsa, 1997). For example, Shor’s quantum algorithm finds the prime factors of an n-digit number in time O(n), while the best known classical factoring algorithms require at least time O(2 1 3 2 3 n n / / log( ) ) (Shor, 1994; Beckman et al. 1996). And Grover’s quantum database search algorithm can find an item in an unsorted list of n items in O( n ) steps, while classical algorithms clearly require O(n) (Grover, 1997). The full power of quantum computation is a subject of active investigation. The smallest unit of quantum information is the qubit, which is analogous to the classical bit. Whereas a classical system of n bits is at any time in one of 2 states, a quantum system of n qubits can be in any linear superposition of Genetic Programming for Quantum Computers Lee Spector Howard Barnum Herbert J. Bernstein lspector@hampshire.edu hbarnum@hampshire.edu hbernstein@hampshire.edu School of Cognitive Science School of Natural Science Hampshire College and Institute for Science and Interdisciplinary Studies (ISIS) Amherst, MA 01002 Hampshire College Amherst, MA 01002 these 2 states simultaneously. Although we cannot read the entire state (because measurement interferes with the system), it appears that this quantum parallelism can nonetheless be harnessed to perform real computational work. In physical terms, a qubit can be thought of as a twocomponent wave, where each component represents a classical value, 0 or 1. The height of a component wave gives the probability that the qubit will be found in a particular classical state, and the phase controls how the wave will interfere with other waves. As usual, a wave with height and phase can be represented by a single complex number. Unlike a classical bit, a qubit can be in both states at the same time, and these states may be in phase, out of phase, or somewhere in between, leading to constructive or destructive interference. To date, only very small quantum computers have been built, but the planning and construction of larger devices is in progress. Current experimental quantum computing hardware is based on the use of ion traps, cavity QED, and NMR techniques; many difficult problems must be solved before these techniques can be scaled up, but a discussion of these issues is beyond the scope of this paper (see Preskill, 1997). Quantum computers are different from classical computers in several ways, and it is not obvious how, in general, software can be developed to take advantage of their non-classical power. A variety of basic questions about this power are still open, for example whether or not there exist polynomial time quantum algorithms for classically NP complete problems. In order to assess the wisdom of expending resources on the physical realization of quantum computers, it is important that we develop a more solid understanding of their real computational powers. One way to further this understanding is to develop more quantum algorithms, either by hand or by use of automatic programming techniques (as below). And there are additional reasons, aside from guidance for research and development efforts, for wanting to determine the real powers of quantum computation. For example, Penrose has argued that quantum effects play an important role in human brains (Penrose, 1994), although his claims have been disputed. Because complexity-theoretic arguments play a major role in cognitive science, a rethinking of the computational complexity limits of brain processes could have a significant impact on the study of human cognition. Spector, L., H. Barnum, and H.J. Bernstein. 1998. Genetic Programming for Quantum Computers. In Genetic Programming 1998: Proceedings of the Third Annual Conference, edited by J.R. Koza, W. Banzhaf, K. Chellapilla, K. Deb, M. Dorigo, D.B. Fogel, M.H. Garzon, D.E. Goldberg, H. Iba, and R.L. Riolo. pp. 365-374. San Francisco, CA: Morgan Kaufmann.

Fractional crystallization in granites of the Sierra Nevada: How important is it?

Abstract: Although compositional variation in zoned calc-alkalic plutons is often ascribed to crystal fractionation, diagnostic large-scale field evidence of crystal accumulation in these slowly cooled bodies is generally missing. In many plutons, however, small-scale crystal cumulates have been preserved as layered schlieren and in microcosm may allow an assessment of the importance of crystal fractionation in their host pluton9s development. Small, widely separated patches of schlieren in the Tuolumne Intrusive Series, Yosemite National Park, California, formed as cumulates. Their darkest layers show high concentrations of magnetite, sphene, biotite, horn-blende, and zircon, and have unusually fractionated major and trace element compositions (FeO >33%; Al 2 O 3 in chondrites ∼750; Zr ∼2000 ppm). The layers define smooth trends on major and trace element Al 2 O 3 variation diagrams that diverge strongly from patterns for the main-sequence rocks of the Tuolumne Series and granitoids throughout the Sierra Nevada. Removal of such cumulates from any main-sequence magma would produce Al-rich evolved rocks, not the Al- poor felsic rocks of the pluton. The findings suggest that fractional crystallization did not produce the dominant chemical patterns seen in the Tuolumne and similar Sierra Nevada granites.

Biocultural perspectives on maternal mortality and obstetrical death from the past to the present.

Abstract: Global efforts to improve maternal health are the fifth focus goal of the Millennium Development Goals adopted by the international community in 2000. While maternal mortality is an epidemic, and the death of a woman in childbirth is tragic, certain assumptions that frame the risk of death for reproductive aged women continue to hinge on the anthropological theory of the "obstetric dilemma." According to this theory, a cost of hominin selection to bipedalism is the reduction of the pelvic girdle; in tension with increasing encephalization, this reduction results in cephalopelvic disproportion, creating an assumed fragile relationship between a woman, her reproductive body, and the neonates she gives birth to. This theory, conceived in the 19th century, gained traction in the paleoanthropological literature in the mid-20th century. Supported by biomedical discourses, it was cited as the definitive reason for difficulties in human birth. Bioarchaeological research supported this narrative by utilizing demographic parameters that depict the death of young women from reproductive complications. But the roles of biomedical and cultural practices that place women at higher risk for morbidity and early mortality are often not considered. This review argues that reinforcing the obstetrical dilemma by framing reproductive complications as the direct result of evolutionary forces conceals the larger health disparities and risks that women face globally. The obstetrical dilemma theory shifts the focus away from other physiological and cultural components that have evolved in concert with bipedalism to ensure the safe delivery of mother and child. It also sets the stage for a framework of biological determinism and structural violence in which the reproductive aged female is a product of her pathologized reproductive body. But what puts reproductive aged women at risk for higher rates of morbidity and mortality goes far beyond the reproductive body. Moving beyond reproduction as the root causes of health inequalities reveals gendered-based oppression and inequality in health analyses. In this new model, maternal mortality can be seen as a sensitive indicator of inequality and social development, and can be explored for what it is telling us about women's health and lives. This article reviews the research in pelvic architecture and cephalopelvic relationships from the subfields of evolutionary biology, paleoanthropology, bioarchaeology, medical anthropology, and medicine, juxtaposing it with historical, ethnographic, and global maternal health analyses to offer a biocultural examination of maternal mortality and reproductive risk management. It reveals the structural violence against reproductive aged women inherent in the biomedical management of birth. By reframing birth as normal, not pathological, global health initiatives can consider new policies that focus on larger issues of disparity (e.g., poverty, lack of education, and poor nutrition) and support better health outcomes across the spectrum of life for women globally.

Improving generalization of evolved programs through automatic simplification

Abstract: Programs evolved by genetic programming unfortunately often do not generalize to unseen data. Reliable synthesis of programs that generalize to unseen data is therefore an important open problem. We present evidence that smaller programs evolved using the PushGP system tend to generalize better over a range of program synthesis problems. Like in many genetic programming systems, programs evolved by PushGP usually have pieces that can be removed without changing the behavior of the program. We describe methods for automatically simplifying evolved programs to make them smaller and potentially improve their generalization. We present five simplification methods and analyze their strengths and weaknesses on a suite of general program synthesis benchmark problems. All of our methods use a straightforward hill-climbing procedure to remove pieces of a program while ensuring that the resulting program gives the same errors on the training data as did the original program. We show that automatic simplification, previously used both for post-run analysis and as a genetic operator, can significantly improve the generalization rates of evolved programs.