The past decade has seen four generations of quantum annealing processors, with qubit counts increasing from 512 on the D-Wave Two (released in 2013), to over 5000 on Advantage processors available in 2023. During this time, expanding access for researchers has sparked enormous growth in publications and in the body of knowledge surrounding capabilities, applications, and best practices in use of these novel computing systems.
This Research Topic will invite submissions on all aspects of empirical experience with annealing-based quantum computers. The intention is to present a broad survey of the current state of knowledge about quantum annealing hardware, performance, software infrastructures, and applications.
Topics of interest include, but are not limited to, the following areas.
Best practice in performance tuning:
+Preprocessing: problem reduction, compact representation of constraints, minor embedding, setting chain strength,
+Annealing computations: parameter setting and tuning strategies for realizing best performance
+Postprocessing: chain repair strategies
Hybrid computation (combining classical and quantum computations):
+ New approaches to hybrid computation
+ Benchmarking hybrid solvers
+ Hybrid solvers for constrained problems
+ Hybrid solvers for exact computations
Estimating resource usage:
+ New benchmarking metrics and infrastructures
+ Developing empirical performance models and profiles
+ Characterizing quantum-friendly input properties
+ Benchmarking quantum versus classical systems
+ Benchmarking quantum annealing versus gate model quantum systems
New application areas, including proof of concept demonstrations:
+Optimization and approximate optimization
+Quantum materials simulation
+Quantum simulations of physical systems in chemistry, biology, earth science, and more.
+Sampling applications
+Machine learning
+Hybrid interfaces for constrained problems
+Application specific hybrid interfaces
+Hybrid methods for exact computations
Software tools and infrastructures:
+ Application specific embedding strategies
+ Representing constraints as penalty functions
+ Utilities for automatic parameter setting
While submissions of original research papers are warmly encouraged, Research Topics are open to variety of article categories, such as literature surveys and descriptions of open source codebases. All submissions will be peer-reviewed.
Topic editor Catherine McGeoch is employed by D-Wave Systems. All other Topic Editors declare no competing interests with regard to the Research Topic subject.
The past decade has seen four generations of quantum annealing processors, with qubit counts increasing from 512 on the D-Wave Two (released in 2013), to over 5000 on Advantage processors available in 2023. During this time, expanding access for researchers has sparked enormous growth in publications and in the body of knowledge surrounding capabilities, applications, and best practices in use of these novel computing systems.
This Research Topic will invite submissions on all aspects of empirical experience with annealing-based quantum computers. The intention is to present a broad survey of the current state of knowledge about quantum annealing hardware, performance, software infrastructures, and applications.
Topics of interest include, but are not limited to, the following areas.
Best practice in performance tuning:
+Preprocessing: problem reduction, compact representation of constraints, minor embedding, setting chain strength,
+Annealing computations: parameter setting and tuning strategies for realizing best performance
+Postprocessing: chain repair strategies
Hybrid computation (combining classical and quantum computations):
+ New approaches to hybrid computation
+ Benchmarking hybrid solvers
+ Hybrid solvers for constrained problems
+ Hybrid solvers for exact computations
Estimating resource usage:
+ New benchmarking metrics and infrastructures
+ Developing empirical performance models and profiles
+ Characterizing quantum-friendly input properties
+ Benchmarking quantum versus classical systems
+ Benchmarking quantum annealing versus gate model quantum systems
New application areas, including proof of concept demonstrations:
+Optimization and approximate optimization
+Quantum materials simulation
+Quantum simulations of physical systems in chemistry, biology, earth science, and more.
+Sampling applications
+Machine learning
+Hybrid interfaces for constrained problems
+Application specific hybrid interfaces
+Hybrid methods for exact computations
Software tools and infrastructures:
+ Application specific embedding strategies
+ Representing constraints as penalty functions
+ Utilities for automatic parameter setting
While submissions of original research papers are warmly encouraged, Research Topics are open to variety of article categories, such as literature surveys and descriptions of open source codebases. All submissions will be peer-reviewed.
Topic editor Catherine McGeoch is employed by D-Wave Systems. All other Topic Editors declare no competing interests with regard to the Research Topic subject.
