Edward Farhi

Last updated
Edward Farhi
Born
Edward Henry Farhi

(1952-06-26) June 26, 1952 (age 72) [1]
New York, U.S. [1]
Nationality American
Alma mater Bronx Science
Brandeis University
Harvard University
Scientific career
Fields Physics
Institutions SLAC
CERN
MIT
Doctoral advisor Howard Georgi

Edward Henry Farhi (born June 26, 1952 [1] ) is a physicist working on quantum computation as a principal scientist at Google. In 2018 he retired from his position as the Cecil and Ida Green Professor of Physics at the Massachusetts Institute of Technology. He was the director of the Center for Theoretical Physics at MIT from 2004 until 2016. He made contributions to particle physics, general relativity and astroparticle physics before turning to his current interest, quantum computation.

Contents

Education

Edward (Eddie) Farhi attended the Bronx High School of Science and obtained his B.A. and M.A. in physics at Brandeis University before getting his Ph.D. in 1978 from Harvard University under the supervision of Howard Georgi. He was then on the staff at the Stanford Linear Accelerator Center and at CERN in Geneva, Switzerland before coming to MIT, where he joined the faculty in 1982. At MIT, he taught undergraduate courses in quantum mechanics and special relativity as well as freshman physics. At the graduate level he taught quantum mechanics, quantum field theory, particle physics and general relativity. In July 2004, he was appointed the Director of MIT's Center for Theoretical Physics.[ citation needed ]

Research

Farhi was trained as a theoretical particle physicist but has also worked on astrophysics, general relativity, and the foundations of quantum mechanics. His present interest is the theory of quantum computation.

As a graduate student, Farhi invented the jet variable "Thrust" which is used today at the Large Hadron Collider to describe how particles in high energy accelerator collisions come out in collimated streams. [2] He then worked with Leonard Susskind on grand unified theories with electro-weak dynamical symmetry breaking. At CERN, he and Larry Abbott proposed an (almost viable) model in which quarks, leptons, and massive gauge bosons are composite. [3] At MIT, with Robert Jaffe, he worked out many of the properties of a possibly stable super dense form of matter called ``Strange Matter" [4] and with Charles Alcock and Angela Olinto he studied the properties of ``Strange Stars", [5] compact objects made of strange matter. His interest then shifted to general relativity and he and Alan Guth studied the classical and quantum prospects of creating a new inflationary universe in a laboratory. [6] He and Guth, along with Sean Carroll, showed how building a time machine would require resources beyond what could ever be possible to obtain. [7]

Since the late '90s, Farhi has been studying how to use quantum mechanics to gain algorithmic speedup in solving problems that are difficult for conventional computers. He and Sam Gutmann pioneered the continuous time Hamiltonian based approach to quantum computation [8] which is an alternative to the conventional gate model. He and Gutmann then proposed the idea of designing algorithms based on quantum walks, which was used to demonstrate the power of quantum computation over classical. [9] They, along with Jeffrey Goldstone and Michael Sipser, introduced the idea of quantum computation by adiabatic evolution [10] which generated much interest in the quantum computing community. For example, the D-Wave machine is designed to run the quantum adiabatic algorithm. In 2007, Farhi, Goldstone and Gutmann showed, using quantum walks, that a quantum computer can determine who wins a game faster than a classical computer. [11] In 2010, he along with Peter Shor and others at MIT introduced a scheme for Quantum Money [12] which so far has resisted attack. In 2014 Farhi, Goldstone and Gutmann introduced the Quantum Approximate Optimization Algorithm (QAOA), a novel quantum algorithm for finding approximate solutions to combinatorial search problems. [13] Farhi and Harrow showed that the lowest depth version of the QAOA exhibits Quantum Supremacy which means that in worst case its output can not be simulated efficiently by a classical device. The QAOA is viewed as one of the best candidates to run on noisy Intermediate-scale quantum (NISQ) devices which are coming online in the near future.

Farhi continues to work on quantum computing but keeps a close eye on particle physics and recent developments in cosmology.

Related Research Articles

<span class="mw-page-title-main">Many-worlds interpretation</span> Interpretation of quantum mechanics

The many-worlds interpretation (MWI) is a philosophical position about how the mathematics used in quantum mechanics relates to physical reality. It asserts that the universal wavefunction is objectively real, and that there is no wave function collapse. This implies that all possible outcomes of quantum measurements are physically realized in some "world" or universe. In contrast to some other interpretations of quantum mechanics, the evolution of reality as a whole in MWI is rigidly deterministic and local. Many-worlds is also called the relative state formulation or the Everett interpretation, after physicist Hugh Everett, who first proposed it in 1957. Bryce DeWitt popularized the formulation and named it many-worlds in the 1970s.

<span class="mw-page-title-main">Quantum computing</span> Technology that uses quantum mechanics

A quantum computer is a computer that exploits quantum mechanical phenomena. On small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior using specialized hardware. Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the art is largely experimental and impractical, with several obstacles to useful applications.

In quantum computing, a quantum algorithm is an algorithm that runs on a realistic model of quantum computation, the most commonly used model being the quantum circuit model of computation. A classical algorithm is a finite sequence of instructions, or a step-by-step procedure for solving a problem, where each step or instruction can be performed on a classical computer. Similarly, a quantum algorithm is a step-by-step procedure, where each of the steps can be performed on a quantum computer. Although all classical algorithms can also be performed on a quantum computer, the term quantum algorithm is generally reserved for algorithms that seem inherently quantum, or use some essential feature of quantum computation such as quantum superposition or quantum entanglement.

<span class="mw-page-title-main">Seth Lloyd</span> American mechanical engineer and physicist

Seth Lloyd is a professor of mechanical engineering and physics at the Massachusetts Institute of Technology.

Jeffrey Goldstone is a British theoretical physicist and an emeritus physics faculty member at the MIT Center for Theoretical Physics.

<span class="mw-page-title-main">Michael Sipser</span> American theoretical computer scientist (born 1954)

Michael Fredric Sipser is an American theoretical computer scientist who has made early contributions to computational complexity theory. He is a professor of applied mathematics and was the dean of science at the Massachusetts Institute of Technology.

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Quantum annealing (QA) is an optimization process for finding the global minimum of a given objective function over a given set of candidate solutions, by a process using quantum fluctuations. Quantum annealing is used mainly for problems where the search space is discrete with many local minima; such as finding the ground state of a spin glass or the traveling salesman problem. The term "quantum annealing" was first proposed in 1988 by B. Apolloni, N. Cesa Bianchi and D. De Falco as a quantum-inspired classical algorithm. It was formulated in its present form by T. Kadowaki and H. Nishimori in 1998 though an imaginary-time variant without quantum coherence had been discussed by A. B. Finnila, M. A. Gomez, C. Sebenik and J. D. Doll in 1994.

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Quantum walks are quantum analogs of classical random walks. In contrast to the classical random walk, where the walker occupies definite states and the randomness arises due to stochastic transitions between states, in quantum walks randomness arises through (1) quantum superposition of states, (2) non-random, reversible unitary evolution and (3) collapse of the wave function due to state measurements.

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Quantum optimization algorithms are quantum algorithms that are used to solve optimization problems. Mathematical optimization deals with finding the best solution to a problem from a set of possible solutions. Mostly, the optimization problem is formulated as a minimization problem, where one tries to minimize an error which depends on the solution: the optimal solution has the minimal error. Different optimization techniques are applied in various fields such as mechanics, economics and engineering, and as the complexity and amount of data involved rise, more efficient ways of solving optimization problems are needed. Quantum computing may allow problems which are not practically feasible on classical computers to be solved, or suggest a considerable speed up with respect to the best known classical algorithm.

Continuous-variable (CV) quantum information is the area of quantum information science that makes use of physical observables, like the strength of an electromagnetic field, whose numerical values belong to continuous intervals. One primary application is quantum computing. In a sense, continuous-variable quantum computation is "analog", while quantum computation using qubits is "digital." In more technical terms, the former makes use of Hilbert spaces that are infinite-dimensional, while the Hilbert spaces for systems comprising collections of qubits are finite-dimensional. One motivation for studying continuous-variable quantum computation is to understand what resources are necessary to make quantum computers more powerful than classical ones.

Quil is a quantum instruction set architecture that first introduced a shared quantum/classical memory model. It was introduced by Robert Smith, Michael Curtis, and William Zeng in A Practical Quantum Instruction Set Architecture. Many quantum algorithms require a shared memory architecture. Quil is being developed for the superconducting quantum processors developed by Rigetti Computing through the Forest quantum programming API. A Python library called pyQuil was introduced to develop Quil programs with higher level constructs. A Quil backend is also supported by other quantum programming environments.

This glossary of quantum computing is a list of definitions of terms and concepts used in quantum computing, its sub-disciplines, and related fields.

Quantum computational chemistry is an emerging field that exploits quantum computing to simulate chemical systems. Despite quantum mechanics' foundational role in understanding chemical behaviors, traditional computational approaches face significant challenges, largely due to the complexity and computational intensity of quantum mechanical equations. This complexity arises from the exponential growth of a quantum system's wave function with each added particle, making exact simulations on classical computers inefficient.

References

  1. 1 2 3 American Men and Women of Science, Thomson Gale 2004
  2. Farhi, Edward (1977). "Quantum Chromodynamics Test for Jets". Physical Review Letters. 39 (25): 1587–1588. Bibcode:1977PhRvL..39.1587F. doi:10.1103/PhysRevLett.39.1587.
  3. Abbott, L.F.; Farhi, Edward (1982). "Are the weak interactions strong". Physics Letters B. 117 (1–2): 29–33. doi: 10.1016/0370-2693(82)90867-X .
  4. Farhi, Edward; Jaffe, R. L. (1984). "Strange matter". Physical Review D. 30 (11): 2379–2390. Bibcode:1984PhRvD..30.2379F. doi:10.1103/PhysRevD.30.2379.
  5. Alcock, Charles; Farhi, Edward; Olinto, Angela (1986). "Strange stars". The Astrophysical Journal. 310: 261. Bibcode:1986ApJ...310..261A. doi:10.1086/164679.
  6. Farhi, Edward; Guth, Alan H. (1987). "An obstacle to creating a universe in the laboratory". Physics Letters B. 183 (2): 149. Bibcode:1987PhLB..183..149F. doi:10.1016/0370-2693(87)90429-1.
  7. Carroll, Sean M.; Farhi, Edward; Guth, Alan H. (1992). "Gott Time Machines Cannot Exist in an Open (2+1)-Dimensional Universe with Timelike Total Momentum". arXiv: hep-th/9207037 .
  8. Farhi, Edward; Gutmann, Sam (1996). "An Analog Analogue of a Digital Quantum Computation". arXiv: quant-ph/9612026 .
  9. Farhi, Edward; Gutmann, Sam (1998). "Quantum computation and decision trees". Physical Review A. 58 (2): 915–928. arXiv: quant-ph/9706062 . Bibcode:1998PhRvA..58..915F. doi:10.1103/PhysRevA.58.915. S2CID   1439479.
  10. Farhi, Edward; Goldstone, Jeffrey; Gutmann, Sam; Sipser, Michael (1999). "Quantum Computation by Adiabatic Evolution". arXiv: quant-ph/0001106 .
  11. Farhi, Edward; Goldstone, Jeffrey; Gutmann, Sam (2007). "A Quantum Algorithm for the Hamiltonian NAND Tree". arXiv: quant-ph/0702144 .
  12. Farhi, Edward; Gosset, David; Hassidim, Avinatan; Lutomirski, Andrew; Shor, Peter (2010). "Quantum money from knots". arXiv: 1004.5127 [quant-ph].
  13. Farhi, Edward; Goldstone, Jeffrey; Gutmann, Sam (2014). "A Quantum Approximate Optimization Algorithm". arXiv: 1411.4028 [quant-ph].