An Integrated Programming and Development Environment for Adiabatic Quantum Optimization

Abstract

Adiabatic quantum computing is a promising route to the computational power afforded by quantum information processing. The recent availability of adiabatic hardware has raised challenging questions about how to evaluate adiabatic quantum optimization programs. Processor behavior depends on multiple steps to synthesize an adiabatic quantum program, which are each highly tunable. We present an integrated programming and development environment for adiabatic quantum optimization called JADE that provides control over all the steps taken during program synthesis. JADE captures the workflow needed to rigorously specify the adiabatic quantum optimization algorithm while allowing a variety of problem types, programming techniques, and processor configurations. We have also integrated JADE with a quantum simulation engine that enables program profiling using numerical calculation. The computational engine supports plug-ins for simulation methodologies tailored to various metrics and computing resources. We present the design, integration, and deployment of JADE and discuss its potential use for benchmarking adiabatic quantum optimization programs by the quantum computer science community.

28 pages, 17 figures, feedback welcomed, even if it's criticism; v2 manuscript updated based on reviewer feedback; v3 manuscript updated based on reviewer feedback, title modified

References in corpus (19)

• Defining and detecting quantum speedup
• Architectural considerations in the design of a superconducting quantum annealing processor
• Minor-embedding in adiabatic quantum computation: II. Minor-universal graph design
• Simple proof of equivalence between adiabatic quantum computation and the circuit model
• Realizable Hamiltonians for Universal Adiabatic Quantum Computers
• Glassy Chimeras could be blind to quantum speedup: Designing better benchmarks for quantum annealing machines
• Towards Fault Tolerant Adiabatic Quantum Computation
• Size dependence of the minimum excitation gap in the Quantum Adiabatic Algorithm
• Non-perturbative k-body to two-body commuting conversion Hamiltonians and embedding problem instances into Ising spins
• How "Quantum" is the D-Wave Machine?
• Training a Binary Classifier with the Quantum Adiabatic Algorithm
• Error suppression and error correction in adiabatic quantum computation I: techniques and challenges
• Image recognition with an adiabatic quantum computer I. Mapping to quadratic unconstrained binary optimization
• Ground State Spin Logic
• A Near-Term Quantum Computing Approach for Hard Computational Problems in Space Exploration
• Quantum pattern recognition with liquid-state nuclear magnetic resonance
• Error suppression and error correction in adiabatic quantum computation II: non-equilibrium dynamics
• On Quadratization of Pseudo-Boolean Functions
• Adiabatic Quantum Programming: Minor Embedding With Hard Faults

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