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C&EN Global Enterprise

Alán Aspuru-Guzik

special to C EN Bec Roldan

Published Sep 23, 2024• 0 citations
Publisher

Abstract

Alán Aspuru-Guzik’s nearly 2 decades of research can’t be narrowed down to a single scientific field. But the flurry of ideas coming from his lab fits, if precariously, under a grander theme: expansion. “I’m very interested in exploring life,” he says. “When there are opportunities that expand my horizon, I will take them.” For Aspuru-Guzik, a professor of chemistry and computer science at the University of Toronto, the possibilities of the unknown are what have driven his laboratory to the interfaces of quantum computing, machine learning, and chemistry. Aspuru-Guzik blurs the lines between everything: theoretical and experimental chemistry, art and science, and creativity and stiffness. “For Alán, disciplines are artificial,” says Joel Yuen-Zhou, who received his PhD from Aspuru- Guzik’s lab in 2012 and now serves as an associate professor of chemistry at the University of California San Diego. Aspuru-Guzik’s interests span art, literature, music, and science, which made him

DOI
10.1021/cen-10229-cover2

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Zapata Computing

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When computational chemist Alán Aspuru-Guzik told his business partner Christopher Savoie that he had an idea for a quantum computing software company, Savoie was skeptical, he admits. This was 2017, and quantum computing was (and arguably still is) in its infancy. Savoie asked Aspuru-Guzik what a state-of-the-art quantum computer could do. Not much more than a pocket calculator, Aspuru-Guzik told him. But the two had worked together successfully at Kyulux on machine-learning methods to advance organic light-emitting diode technology, so Savoie visited Aspuru-Guzik’s lab, then at Harvard University. By the time they’d finished lunch, Savoie says, he was convinced that Aspuru-Guzik and his collaborators—Yudong Cao, Peter Johnson, Jonathan P. Olson, and Jhonathan Romero Fontalvo—were on to something. By the afternoon, Aspuru-Guzik was calling quantum computing researchers to introduce Savoie as the new CEO of Zapata Computing. Quantum computing proponents have made huge promises, especially in chemistry. A traditional computer represents

C&EN Global Enterprise•Nov 16, 2020•0 citations

Machine Learning in Quantum Sciences

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Artificial intelligence is dramatically reshaping scientific research and is coming to play an essential role in scientific and technological development by enhancing and accelerating discovery across multiple fields. This book dives into the interplay between artificial intelligence and the quantum sciences; the outcome of a collaborative effort from world-leading experts. After presenting the key concepts and foundations of machine learning, a subfield of artificial intelligence, its applications in quantum chemistry and physics are presented in an accessible way, enabling readers to engage with emerging literature on machine learning in science. By examining its state-of-the-art applications, readers will discover how machine learning is being applied within their own field and appreciate its broader impact on science and technology. This book is accessible to undergraduates and more advanced readers from physics, chemistry, engineering, and computer science. Online resources include Jupyter notebooks to expand and develop upon key topics introduced in the book.

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Aspuru-Guzik A.: Quantum algorithm for molecular properties and geometry optimization. J. Chem. Phys. 131, 224102

Ivan Kassal, Alán Aspuru‐Guzik

•Jan 1, 2009•1 citation

AN INVESTIGATION INTO THE TRANSFORMATIVE POWER OF QUANTUM COMPUTING IN SCIENTIFIC RESEARCH

Dr. Arven L. Thaylen, Dr. Senira D. Valtome

We are on the verge of a new era in computation, one that operates on the strange and wonderful rules of the quantum world. This article explores the story of quantum computing, a technology that promises to solve problems far beyond the reach of today's fastest supercomputers. We'll journey from the core ideas that make it all possible—concepts like superposition and entanglement—to the incredible machines being built in labs around the world. We'll see how these machines are being programmed and witness the first hints of their power in fields like medicine, materials science, and cybersecurity. Finally, we'll take an honest look at the immense challenges that still lie ahead on the path to building a truly powerful quantum computer. This is a story of human ingenuity pushing the boundaries of science, a tale of the immense potential and practical hurdles of the coming quantum age.

European Journal of Emerging Cloud and Quantum Computing•Dec 30, 2024•0 citations

Chemistry is quantum computing’s killer app

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In an IBM lab in Yorktown Heights, N.Y., a circuit board sits at the bottom of a large tub cooled to temperatures colder than those of outer space. Shiny, solderlike zigzags of niobium cover the board, connecting 16 unusual computing elements. These elements, called quantum bits, or qubits, may form the basis of future computers that will solve problems by taking advantage of quantum mechanics. So far, quantum computers like IBM’s 16-qubit system can solve only simple problems, all of them easy enough to be handled by traditional computers. This spring, IBM set a quantum-computing record by modeling beryllium hydride—which contains a mere three atoms (Nature 2017, DOI: 10.1038/nature23879). Earlier prototypes modeled hydrogen and factored the number 15. As quantum computers become more sophisticated, they will allow chemists to solve problems that are intractable on today’s desktops and even supercomputers. In fact, physicists expect that chemistry simulations will be quantum

C&EN Global Enterprise•Oct 30, 2017•8 citations

Comment on ``Time-dependent current-density functional theory for generalized open quantum systems" by J. Yuen-Zhou, C. Rodriiguez-Rosario and A. Aspuru-Guzik

Roberto D’Agosta, Massimiliano Di Ventra

We comment on a recent paper by Yuen-Zhou et al. [ Phys. Chem. Chem. Phys. 2009, 11, 4509 ] which extends some of the results of Time-Dependent Current Density Functional Theory applied to open quantum systems. Besides pointing out some incorrect statements in the theorems, we show that in the proof of the main result of the paper an arbitrary condition is introduced. Moreover, we argue that, since this condition has not a physical origin, one can find two systems with different vector potentials that lead to the same dynamics of the physical quantities of interest, the current and particle densities.

arXiv:0911.3932•Nov 19, 2009•2 citations
Alán Aspuru-Guzik’s nearly 2 decades of research can’t be narrowed down to a single scientific field. But the flurry of ideas coming from his lab fits, if precariously, under a grander theme: expansion. “I’m very interested in exploring life,” he says. “When there are opportunities that expand my horizon, I will take them.” For Aspuru-Guzik, a professor of chemistry and computer science at the University of Toronto, the possibilities of the unknown are what have driven his laboratory to the interfaces of quantum computing, machine learning, and chemistry. Aspuru-Guzik blurs the lines between everything: theoretical and experimental chemistry, art and science, and creativity and stiffness. “For Alán, disciplines are artificial,” says Joel Yuen-Zhou, who received his PhD from Aspuru- Guzik’s lab in 2012 and now serves as an associate professor of chemistry at the University of California San Diego. Aspuru-Guzik’s interests span art, literature, music, and science, which made him
When computational chemist Alán Aspuru-Guzik told his business partner Christopher Savoie that he had an idea for a quantum computing software company, Savoie was skeptical, he admits. This was 2017, and quantum computing was (and arguably still is) in its infancy. Savoie asked Aspuru-Guzik what a state-of-the-art quantum computer could do. Not much more than a pocket calculator, Aspuru-Guzik told him. But the two had worked together successfully at Kyulux on machine-learning methods to advance organic light-emitting diode technology, so Savoie visited Aspuru-Guzik’s lab, then at Harvard University. By the time they’d finished lunch, Savoie says, he was convinced that Aspuru-Guzik and his collaborators—Yudong Cao, Peter Johnson, Jonathan P. Olson, and Jhonathan Romero Fontalvo—were on to something. By the afternoon, Aspuru-Guzik was calling quantum computing researchers to introduce Savoie as the new CEO of Zapata Computing. Quantum computing proponents have made huge promises, especially in chemistry. A traditional computer represents
Artificial intelligence is dramatically reshaping scientific research and is coming to play an essential role in scientific and technological development by enhancing and accelerating discovery across multiple fields. This book dives into the interplay between artificial intelligence and the quantum sciences; the outcome of a collaborative effort from world-leading experts. After presenting the key concepts and foundations of machine learning, a subfield of artificial intelligence, its applications in quantum chemistry and physics are presented in an accessible way, enabling readers to engage with emerging literature on machine learning in science. By examining its state-of-the-art applications, readers will discover how machine learning is being applied within their own field and appreciate its broader impact on science and technology. This book is accessible to undergraduates and more advanced readers from physics, chemistry, engineering, and computer science. Online resources include Jupyter notebooks to expand and develop upon key topics introduced in the book.
We are on the verge of a new era in computation, one that operates on the strange and wonderful rules of the quantum world. This article explores the story of quantum computing, a technology that promises to solve problems far beyond the reach of today's fastest supercomputers. We'll journey from the core ideas that make it all possible—concepts like superposition and entanglement—to the incredible machines being built in labs around the world. We'll see how these machines are being programmed and witness the first hints of their power in fields like medicine, materials science, and cybersecurity. Finally, we'll take an honest look at the immense challenges that still lie ahead on the path to building a truly powerful quantum computer. This is a story of human ingenuity pushing the boundaries of science, a tale of the immense potential and practical hurdles of the coming quantum age.
In an IBM lab in Yorktown Heights, N.Y., a circuit board sits at the bottom of a large tub cooled to temperatures colder than those of outer space. Shiny, solderlike zigzags of niobium cover the board, connecting 16 unusual computing elements. These elements, called quantum bits, or qubits, may form the basis of future computers that will solve problems by taking advantage of quantum mechanics. So far, quantum computers like IBM’s 16-qubit system can solve only simple problems, all of them easy enough to be handled by traditional computers. This spring, IBM set a quantum-computing record by modeling beryllium hydride—which contains a mere three atoms (Nature 2017, DOI: 10.1038/nature23879). Earlier prototypes modeled hydrogen and factored the number 15. As quantum computers become more sophisticated, they will allow chemists to solve problems that are intractable on today’s desktops and even supercomputers. In fact, physicists expect that chemistry simulations will be quantum
We comment on a recent paper by Yuen-Zhou et al. [ Phys. Chem. Chem. Phys. 2009, 11, 4509 ] which extends some of the results of Time-Dependent Current Density Functional Theory applied to open quantum systems. Besides pointing out some incorrect statements in the theorems, we show that in the proof of the main result of the paper an arbitrary condition is introduced. Moreover, we argue that, since this condition has not a physical origin, one can find two systems with different vector potentials that lead to the same dynamics of the physical quantities of interest, the current and particle densities.