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 011005

Subject Categories

Computer Engineering

Computer Science

Computer Systems

DOI

10.1103/PhysRevX.12.011005

More information

Latest update

1/25/2022