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Energy Inference of Black-Box Quantum Computers Using Quantum Speed Limit

Nobumasa Ishida, Yoshihiko Hasegawa

TL;DR

The results demonstrate that fundamental energetic properties of black-box quantum computers can be quantitatively accessed through operational time measurements, reflecting the conjugate relationship between time and energy imposed by the uncertainty principle.

Abstract

Cloud-based quantum computers do not provide users with access to hardware-level information such as the underlying Hamiltonians, which obstructs the characterization of their physical properties. We propose a method to infer the energy scales of gate Hamiltonians in such black-box quantum processors using only user-accessible data, by exploiting quantum speed limits. Specifically, we reinterpret the Margolus-Levitin and Mandelstam-Tamm bounds as estimators of the energy expectation value and variance, respectively, and relate them to the shortest time for the processor to orthogonalize a quantum state. This shortest gate time, expected to lie on the nanosecond scale, is inferred from job execution times measured in seconds by employing gate-time amplification. We apply the method to IBM's superconducting quantum processor and estimate the energy scales associated with single-, two-, and three-qubit gates. The order of estimated energy is consistent with typical drive energies in superconducting qubit systems, suggesting that current gate operations approach the quantum speed limit. Our results demonstrate that fundamental energetic properties of black-box quantum computers can be quantitatively accessed through operational time measurements, reflecting the conjugate relationship between time and energy imposed by the uncertainty principle.

Energy Inference of Black-Box Quantum Computers Using Quantum Speed Limit

TL;DR

The results demonstrate that fundamental energetic properties of black-box quantum computers can be quantitatively accessed through operational time measurements, reflecting the conjugate relationship between time and energy imposed by the uncertainty principle.

Abstract

Cloud-based quantum computers do not provide users with access to hardware-level information such as the underlying Hamiltonians, which obstructs the characterization of their physical properties. We propose a method to infer the energy scales of gate Hamiltonians in such black-box quantum processors using only user-accessible data, by exploiting quantum speed limits. Specifically, we reinterpret the Margolus-Levitin and Mandelstam-Tamm bounds as estimators of the energy expectation value and variance, respectively, and relate them to the shortest time for the processor to orthogonalize a quantum state. This shortest gate time, expected to lie on the nanosecond scale, is inferred from job execution times measured in seconds by employing gate-time amplification. We apply the method to IBM's superconducting quantum processor and estimate the energy scales associated with single-, two-, and three-qubit gates. The order of estimated energy is consistent with typical drive energies in superconducting qubit systems, suggesting that current gate operations approach the quantum speed limit. Our results demonstrate that fundamental energetic properties of black-box quantum computers can be quantitatively accessed through operational time measurements, reflecting the conjugate relationship between time and energy imposed by the uncertainty principle.

Paper Structure

This paper contains 3 sections, 15 equations, 2 figures, 1 table.

Table of Contents

  1. Appendix
  2. Analysis of the effective Hamiltonian
  3. Effect of gate errors on the estimation

Figures (2)

  • Figure 1: Schematic of the black-box quantum computer. The user can submit quantum circuits and receive measurement outcomes along with the execution time of the whole job. The internal structure of the quantum computer, such as the Hamiltonian generating the dynamics, is hidden from the user.
  • Figure 2: Example of gate-time amplification for the $X$ gate on ibm_torino. The vertical axis shows the job execution time $T_{\rm exec}$ and the horizontal axis shows the number of repeated $X$ gates $N_{\rm gate}$ contained in each circuit. Each data point corresponds to a job consisting of a single circuit with $N_{\rm shots}=10^3$.