Quantum Computer Systems for Scientific Discovery

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Quantum Computer Systems for Scientific Discovery

Yuri Alexeev, Dave Bacon, Kenneth R. Brown, Robert Calderbank, Lincoln D. Carr, Frederic T. Chong, Brian DeMarco, Dirk Englund, Edward Farhi, Bill Fefferman, Alexey V. Gorshkov, Andrew Houck, Jungsang Kim, Shelby Kimmel, Michael Lange, Seth Lloyd, Mikhail D. Lukin, Dmitri Maslov, Peter Maunz, Christopher Monroe, John Preskill, Martin Roetteler, Martin J. Savage, and Jeff Thompson

PRX Quantum 2, 017001 – Published 24 February 2021

Abstract

The great promise of quantum computers comes with the dual challenges of building them and finding their useful applications. We argue that these two challenges should be considered together, by codesigning full-stack quantum computer systems along with their applications in order to hasten their development and potential for scientific discovery. In this context, we identify scientific and community needs, opportunities, a sampling of a few use case studies, and significant challenges for the development of quantum computers for science over the next 2–10 years. This document is written by a community of university, national laboratory, and industrial researchers in the field of Quantum Information Science and Technology, and is based on a summary from a U.S. National Science Foundation workshop on Quantum Computing held on October 21–22, 2019 in Alexandria, VA.

Received 4 February 2020
Accepted 21 October 2020

DOI:https://doi.org/10.1103/PRXQuantum.2.017001

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Published by the American Physical Society

Physics Subject Headings (PhySH)

Adiabatic quantum optimization Machine learning Measurement-based quantum computing Quantum algorithms Quantum benchmarking Quantum computation Quantum control Quantum gates Quantum simulation Surface code quantum computing Topological quantum computing

Quantum Information, Science & TechnologyCondensed Matter, Materials & Applied PhysicsAtomic, Molecular & Optical

Authors & Affiliations

Yuri Alexeev ¹, Dave Bacon², Kenneth R. Brown^3,4,5, Robert Calderbank^3,6, Lincoln D. Carr ⁷, Frederic T. Chong⁸, Brian DeMarco⁹, Dirk Englund¹⁰, Edward Farhi^11,12, Bill Fefferman ⁸, Alexey V. Gorshkov ^13,14, Andrew Houck¹⁵, Jungsang Kim^3,5,16, Shelby Kimmel ¹⁷, Michael Lange¹⁸, Seth Lloyd¹⁹, Mikhail D. Lukin²⁰, Dmitri Maslov ²¹, Peter Maunz²², Christopher Monroe^13,16,*, John Preskill²³, Martin Roetteler²⁴, Martin J. Savage²⁵, and Jeff Thompson¹⁵

¹Argonne National Laboratory, Lemont, Illinois, USA
²Google, Inc., Seattle, Washington, USA
³Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA
⁴Department of Chemistry, Duke University, Durham, North Carolina, USA
⁵Department of Physics, Duke University, Durham, North Carolina, USA
⁶Department of Computer Science and Department of Mathematics, Duke University, Durham, North Carolina, USA
⁷Department of Physics, Colorado School of Mines, Golden, Colorado, USA
⁸Department of Computer Science, University of Chicago, Chicago, Illinois, USA
⁹Department of Physics and IQUIST, University of Illinois, Urbana-Champaign, Illinois, USA
¹⁰Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
¹¹Google, Inc., Venice, California, USA
¹²Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
¹³Joint Quantum Institute, Joint Center for Quantum Information and Computer Science, and Department of Physics, University of Maryland, College Park, Maryland, USA
¹⁴National Institute of Standards and Technology, Gaithersburg, Maryland, USA
¹⁵Department of Electrical Engineering, Princeton University, Princeton, New Jersey, USA
¹⁶IonQ, Inc., College Park, Maryland, USA
¹⁷Department of Computer Science, Middlebury College, Middlebury, Vermont, USA
¹⁸L3Harris Technologies, Melborune, Florida, USA
¹⁹Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
²⁰Department of Physics, Harvard University, Cambridge, Massachusetts, USA
²¹IBM T.J. Watson Research Center, Yorktown Heights, New York, USA
²²Sandia National Laboratories, Albuquerque, New Mexico, USA
²³Institute for Quantum Information and Matter and Walter Burke Institute for Theoretical Physics, California Institute of Technology, Pasadena, California, USA
²⁴Microsoft Quantum, Redmond, Washington, USA
²⁵Institute for Nuclear Theory and Department of Physics, University of Washington, Seattle, Washington, USA

^*[email protected]

Popular Summary

Quantum computers represent a radical approach to information processing, allowing computational tasks that are difficult or impossible with conventional computers. At the core of a quantum computer is a collection of quantum bits (qubits) that can be in quantum superpositions of 0 and 1 when sufficiently isolated from the environment. Through quantum logic gate operations, qubits can also become “entangled,” a uniquely quantum attribute whereby multiple qubits exhibit strong correlations even though they are individually random when measured. This inherent “wiring” allows quantum computers to compute and sample over enormous spaces of information not available in any possible conventional computer.

It is unclear however how quantum computers will be used in the future, as there are only a few known algorithms offering a quantum advantage. Moreover, it remains a great challenge to build quantum computers and scale the qubit number and operation fidelity to that required for useful applications. We argue that these two challenges are related: by building larger and more capable quantum computers we discover new applications, and by refining these applications we effectively guide the development of quantum computer hardware. This intertwining of application discovery and device building is called codesign.

Early quantum computer applications will likely come from science itself, such as programmable simulations of quantum phenomena to the understanding of how information evolves and propagates in entangled quantum systems. We therefore advocate for a scientific approach to codesigning future quantum computer systems to scientific discovery through a tight coupling of mathematicians and computer scientists with physicists, chemists, and engineers of all types.

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Vol. 2, Iss. 1 — February - April 2021

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Figure 1
The rotation and $cnot$ gates are an example of a universal quantum gate family when available on all qubits, with explicit evolution (top) and quantum circuit block schematics (bottom). (a) The single-qubit rotation gate $R (θ, ϕ)$ , with two continuous parameters $θ$ and $ϕ$ , evolves input qubit state $| x ⟩$ to output state $| \tilde{x} ⟩$ . (b) The $cnot$ (or reversible xor) gate on two qubits evolves two (control and target) input qubit states $| x_{C} ⟩$ and $| x_{T} ⟩$ to output states $| {\tilde{x}}_{C} = x_{C} ⟩$ and $| {\tilde{x}}_{T} = x_{C} \oplus x_{T} ⟩$ , where “ $\oplus$ ” is addition modulo 2, or, equivalently, the xor operation.
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Figure 2
Advances in all levels of the quantum computer stack, from algorithms and quantum software down to control engineering and qubit technology, will be required to bring quantum computers to fruition. We expect scientific opportunities at every level and at the interfaces between levels. At the highest levels, quantum computer algorithms are expected to advance many fields of science and technology. At the middle levels (software stack), the compilation and translation of quantum gates will allow for algorithmic compression to accelerate performance, while error-correction techniques will mitigate quantum computing errors. At the lowest levels, new ways to control interactions between qubit technologies may lead to better performance. Future capable qubit technologies will require tight integration with the other layers of the stack to realize their potential.
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Figure 3
Schematic of the measurement and feedback optimization in the variational quantum eigensolver algorithm (from Ref. [35]).
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Figure 4
In the variational quantum eigensolver algorithm commonly applied to chemistry simulations, small changes in the quantum state induced by controlled rotation involve $cnot$ gates between qubits (left). However, with the Ising or $xx$ gate native to ion trap quantum computers [94], these small controlled rotations can be expressed as a small-angle Ising gate accompanied by single-qubit rotation operations as written. This expression greatly reduces noise [78]. The rotation operations are $R_{x} (θ) = R (θ, ϕ = 0)$ , $R_{y} (θ) = R (θ, ϕ = π / 2)$ , and $R_{z} (θ) = R_{x} (π / 2) R_{y} (θ) R_{x} (- π / 2)$ .
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Figure 5
The MIS problem on a random unit disk graph, which is natively represented in an array of atoms with a Rydberg blockade interaction. The MIS of this instance is labeled in red. With variable weights on each vertex, the problem can be generalized to the maximum-weight independent set problem. The legend shows the encoding of qubits in the ground and excited Rydberg states of a neutral atom. The detuning $Δ_{i}$ of a driving field with resonant Rabi frequency $Ω$ controls the Rydberg blockade radius, which is denoted by the green dashed circles surrounding each atom and is assumed uniform $(Δ_{i} = Δ)$ .
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