Enhancing quantum annealing performance for the molecular similarity problem

  title={Enhancing quantum annealing performance for the molecular similarity problem},
  author={Maritza Hernandez and Maliheh Aramon},
  journal={Quantum Information Processing},
Quantum annealing is a promising technique which leverages quantum mechanics to solve hard optimization problems. Considerable progress has been made in the development of a physical quantum annealer, motivating the study of methods to enhance the efficiency of such a solver. In this work, we present a quantum annealing approach to measure similarity among molecular structures. Implementing real-world problems on a quantum annealer is challenging due to hardware limitations such as sparse… 

Breaking limitation of quantum annealer in solving optimization problems under constraints

The present study proposes an alternative approach to solve a large-scale optimization problem on the chimera graph via a well-known method in statistical mechanics called the Hubbard-Stratonovich transformation or its variants and can be used to deal with a fully connected Ising model without embedding on the Chimera graph.

Applying the Hubbard-Stratonovich Transformation to Solve Scheduling Problems Under Inequality Constraints With Quantum Annealing

The implementation of the Hubbard-Stratonovich transformation carried out in this paper on a scheduling use case suggests that this approach could be investigated further and applied to a variety of real-life integer programming problems under multiple constraints to lower the cost of mapping to QUBO, a key step towards the near-term practical application of quantum computing.

Assessment of image generation by quantum annealer

A discriminator given by a neural network trained on an a priori dataset shows a higher performance of quantum annealer compared with the classical approach for Boltzmann machine learning in training of the generative model, however the generation of the data suffers from the remanent quantum fluctuation in the quantumAnnealer.

Viewing vanilla quantum annealing through spin glasses

Quantum annealing promises to solve complex combinatorial optimization problems faster than current transistor-based computer technologies. Although to date only one commercially-available quantum

Quantum annealing for industry applications: introduction and review

A literature review of the theoretical motivations for QA as a heuristic quantum optimization algorithm, the software and hardware that is required to use such quantum processors, and the state-of-the-art applications and proofs- of-concepts that have been demonstrated using them is provided.

A quantum annealing approach to ionic diffusion in solids

This work has developed a framework for using quantum annealing computation to evaluate a key quantity in ionic diffusion in solids, the correlation factor, and mapped the problem into a quantum spin system described by the Ising Hamiltonian.

Physics-Inspired Optimization for Quadratic Unconstrained Problems Using a Digital Annealer

The results show that the Digital Annealer currently exhibits a time-to-solution speedup of roughly two orders of magnitude for fully connected spin-glass problems with bimodal or Gaussian couplings, over the single-core implementations of simulated annealing and parallel tempering Monte Carlo used in this study.

Optimal control of traffic signals using quantum annealing

This paper reports a QUBO formatting of the problem of optimal control of time-dependent traffic signals on an artificial grid-structured road network so as to ease the flow of traffic, and the use of D-Wave Systems’ quantum annealer to solve it.

Obtaining Ground States of the XXZ Model Using the Quantum Annealing with Inductively Coupled Superconducting Flux Qubits

Preparing ground states of Hamiltonians is important in the condensed matter physics and the quantum chemistry. The interaction Hamiltonians typically contain not only diagonal but also off-diagonal

On Modeling Local Search with Special-Purpose Combinatorial Optimization Hardware

This paper tackles the main challenges of problem size and precision limitation that the Ising hardware model typically suffers from and can be generally used in any local search and refinement solvers that are broadly employed in combinatorial scientific computing algorithms.



Quantum annealing correction with minor embedding

This work demonstrates that quantum annealing correction can and should be used to improve the robustness of quantumAnnealing not only for natively embeddable problems, but also when minor embedding is used to extend the connectivity of physical devices.

Probing for quantum speedup in spin-glass problems with planted solutions

This work introduces a method to construct a set of frustrated Ising-model optimization problems with tunable hardness, and studies the performance of a D-Wave Two device with up to 503 qubits on these problems and compares it to a suite of classical algorithms.

Experimental quantum annealing: case study involving the graph isomorphism problem

An experimental case study of quantum annealing and some of the factors involved in real-world solvers, using a 504-qubit D-Wave Two machine and the graph isomorphism problem and the enhanced solver demonstrated clear advantages over the baseline approach.

CORRIGENDUM: Optimization using quantum mechanics: quantum annealing through adiabatic evolution

We review here some recent work in the field of quantum annealing, alias adiabatic quantum computation. The idea of quantum annealing is to perform optimization by a quantum adiabatic evolution which

Mapping Constrained Optimization Problems to Quantum Annealing with Application to Fault Diagnosis

A new embedding algorithm for mapping a CSP onto a hardware Ising model with a fixed sparse set of interactions, and two new decomposition algorithms for solving problems too large to map directly into hardware are proposed.

Seeking Quantum Speedup Through Spin Glasses: The Good, the Bad, and the Ugly

This work proposes to complement head-to-head scaling studies that compare quantum annealing machines to state-of-the-art classical codes with an approach that compares the performance of different algorithms and/or computing architectures on different classes of computationally hard tunable spin-glass instances.

Error-corrected quantum annealing with hundreds of qubits.

A substantial improvement over the performance of the processors in the absence of error correction is demonstrated, paving the way towards large-scale noise-protected adiabatic quantum optimization devices, although a threshold theorem such as has been established in the circuit model of quantum computing remains elusive.

Minor-embedding in adiabatic quantum computation: I. The parameter setting problem

  • V. Choi
  • Computer Science, Physics
    Quantum Inf. Process.
  • 2008
The embedded Ising Hamiltonian for solving the maximum independent set (MIS) problem via adiabatic quantum computation (AQC) using an Ising spin-1/2 system is demonstrated.

Quantum annealing with manufactured spins

This programmable artificial spin network bridges the gap between the theoretical study of ideal isolated spin networks and the experimental investigation of bulk magnetic samples, and may provide a practical physical means to implement a quantum algorithm, possibly allowing more-effective approaches to solving certain classes of hard combinatorial optimization problems.

Defining and detecting quantum speedup

Here, it is shown how to define and measure quantum speedup and how to avoid pitfalls that might mask or fake such a speedup, and the subtle nature of the quantum speed up question is illustrated.