Demonstration of Density Matrix Exponentiation Using a Superconducting Quantum Processor
Journal article, 2022
Quantum computers hold the potential to outperform classical supercomputers at certain tasks. To implement algorithms on a quantum computer, programmers use conventional computers and hardware to create a set of classical control signals that implement a desired quantum algorithm. However, feeding the quantum information forward requires an inefficient conversion: extraction of quantum information, conversion to classical control signals, and reinjection of those signals into the system to implement quantum operations. Here, we demonstrate a more natively quantum strategy to programming quantum computers. Our approach uses the density matrix exponentiation (DME) protocol, a general technique for using a quantum state to enact a quantum operation. It can be thought of as a subroutine with which programmers can turn multiple copies of a quantum state into instructions for next steps in a quantum algorithm.We implement DME using two qubits in a superconducting quantum processor. Our implementation relies on a high-fidelity two-qubit gate and a novel technique called quantum measurement emulation to approximately reset a known quantum state. These developments enable us to demonstrate the DME protocol for the first time on a small-scale quantum processor and benchmark its performance.While DME was originally proposed in the context of a specific quantum machine-learning algorithm, it may also represent a fundamentally different approach to quantum programming. It allows the possibility of encoding quantum algorithms directly into quantum states and executing those algorithms on other quantum states, enabling a new class of efficient quantum algorithms.
DME protocol
Quantum computers
quantum measurement emulation
Author
M. Kjaergaard
Massachusetts Institute of Technology (MIT)
M. E. Schwartz
MIT Lincoln Laboratory
A. Greene
Massachusetts Institute of Technology (MIT)
G. Samach
Massachusetts Institute of Technology (MIT)
MIT Lincoln Laboratory
Andreas Bengtsson
Chalmers, Microtechnology and Nanoscience (MC2), Quantum Technology
Massachusetts Institute of Technology (MIT)
M. O'Keeffe
MIT Lincoln Laboratory
C. M. McNally
Massachusetts Institute of Technology (MIT)
Jochen Braumüller
Massachusetts Institute of Technology (MIT)
David K. Kim
MIT Lincoln Laboratory
Philip Krantz
Massachusetts Institute of Technology (MIT)
M. Marvian
Massachusetts Institute of Technology (MIT)
Alexander Melville
MIT Lincoln Laboratory
Bethany M. Niedzielski
MIT Lincoln Laboratory
Y. Sung
Massachusetts Institute of Technology (MIT)
Roni Winik
Massachusetts Institute of Technology (MIT)
Jonilyn L. Yoder
MIT Lincoln Laboratory
D. Rosenberg
MIT Lincoln Laboratory
K. Obenland
MIT Lincoln Laboratory
S. Lloyd
Massachusetts Institute of Technology (MIT)
T. P. Orlando
Massachusetts Institute of Technology (MIT)
I. Marvian
Duke University
S. Gustavsson
Massachusetts Institute of Technology (MIT)
W. D. Oliver
Massachusetts Institute of Technology (MIT)
MIT Lincoln Laboratory
Physical Review X
21603308 (eISSN)
Vol. 12 1 011005Subject Categories
Computer Engineering
Computer Science
Computer Systems
DOI
10.1103/PhysRevX.12.011005