Related Experiment Video
Updated: Aug 2, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
Published on: September 8, 2023
Lower Bounds on Quantum Annealing Times
Luis Pedro García-Pintos1,2,3, Lucas T Brady4,5, Jacob Bringewatt1,2
1Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, Maryland 20742, USA.
Quantum annealing can prepare ground states faster than previously thought. This study derives lower bounds for quantum annealing time, proving optimal scaling for fast schedules and highlighting quantum coherence as a key resource.
Area of Science:
- Quantum Computing
- Quantum Annealing
- Theoretical Physics
Background:
- The adiabatic theorem offers guidelines for preparing ground states in quantum systems.
- Faster quantum annealing protocols exist but lack rigorous theoretical bounds outside the adiabatic regime.
Purpose of the Study:
- To derive rigorous lower bounds on the time required for successful quantum annealing.
- To demonstrate the optimality of known fast annealing schedules.
- To identify the role of quantum coherence in rapid annealing.
Main Methods:
- Derivation of analytical lower bounds for quantum annealing time.
- Analysis of specific models including the Roland and Cerf unstructured search, Hamming spike, and p-spin models.
- Asymptotic analysis of annealing schedules.
Main Results:
- Established rigorous lower bounds on quantum annealing time.
- Demonstrated that the derived bounds are asymptotically saturated by known fast annealing schedules, proving their optimal scaling.
- Showcased that rapid annealing necessitates coherent superpositions of energy eigenstates.
Conclusions:
- The derived bounds provide a theoretical foundation for understanding the limits of quantum annealing speed.
- Fast quantum annealing schedules for specific problems exhibit optimal time scaling.
- Quantum coherence is identified as an essential computational resource for achieving rapid quantum annealing.
Related Concept Videos
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Maxam-Gilbert Sequencing
Challenges of the Maxam-Gilbert Method
The...
Ampere-Maxwell's Law: Problem-Solving
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
Atomic Nuclei: Types of Nuclear Relaxation
In spin–lattice or longitudinal relaxation, the excited spins exchange energy with the surrounding lattice as they return to the lower energy level. Among several mechanisms that contribute to spin–lattice relaxation, magnetic dipolar interactions are significant. Here, the excited nucleus transfers...
Biot-Savart Law: Problem-Solving
Consider a mobile phone battery bank as a source of steady current, which flows through the wire connected between the two. What is the magnitude of the magnetic field created by this current at a field point P?
To estimate the magnitude of the total magnetic field, we first consider a small current element of length dl, at a distance r from the field point. Now the following...
First Law Of Thermodynamics: Problem-Solving
The following strategies can be used to solve any problem involving the first law of thermodynamics.

