Technology Information

2017

How Will Early Quantum Computing Benefit Computational Methods?

A paper about near-term application problems for quantum annealing on D-Wave, from the 2017 SIAM Annual Meeting minisymposium entitled “Identifying Computational Methods for Early Benefit from Quantum Computing". Paper authors are from Los Alamos National Laboratory, 1QBit, Volkswagen and D-Wave. DOWNLOAD.

D-Wave 2000Q Technology Overview
D-Wave Overview

A brief introduction to D-Wave and quantum computing. Read the D-Wave Overview

2016

IDC: Quantum Computing in the Real World
Steve Conway; Earl C. Joseph, Ph.D.; and Robert Sorensen

IDC recently published a Technology Spotlight on quantum computing. Download the report here. 

D-Wave White Papers

2017

Reverse Quantum Annealing for Local Refinement of Solutions

The success of classical heuristic search algorithms often depends on the balance between global search for good regions of the solution space (exploration) and local search that refones known good solutions (exploitation). While local refinement of known solutions is not available to the canonical forward quantum annealing algorithm, D-Wave has developed a reverse annealing feature that makes this possible by annealing backward from a specified state, then forward to a new state. This enables the use of quantum annealing for the refinement of classical states via local search, making it possible to use quantum annealing as a component in more sophisticated hybrid algorithms. Local quantum search has been analyzed theoretically to explore applications such as protein folding, and has natural application in molecular dynamics, quantum simulation, and quantum chemistry, but has not been available for experiments until now. In a preliminary example, we show that reverse annealing can be used to generate new global optima up to 150 times faster than forward quantum annealing.

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Virtual Graphs for High-Performance Embedded Topologies

Many optimization and machine learning algorithms are commonly described as graph problems. For example, graphical models are often used to analyze the  flow of traffic between cities or the transmission of information between neurons in an artificial neural network.

D-Wave quantum processing units (QPUs) solve graphifical models—specifically, Ising minimization problems on a physical working graph made up of qubits and couplers. The new virtual graphs feature of the D-Wave 2000Q system provides users with improved embedding performance wrapped in a simplified interface. We describe the key enabling processor technologies, and provide a simple example with performance results enabled by this new feature in the D-Wave 2000Q system. DOWNLOAD.

Quantum Performance Evaluation: A Short Reading List

Since the first release of D-Wave annealing-based quantum computers in 2010, scores of research papers have been published describing their physical properties, capabilities, and performance. The research domain is complex and rich, which means that the work is ongoing and will continue for many years.

This white paper gives a snapshot of recent work on quantum system performance evaluation, which considers both solution quality and computation time.

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Optimization with Clause Problems
C. C. McGeoch, J. King, M. Mohammadi Nevisi, S. Yarkoni, and J. Hilton

We introduce a new input class called clause problems, that can be used to study local constraint structures, which occur in inputs translated from general NP-hard problems to the D-Wave native topology. We describe a small family of clause problems that are contrived to create significant challenges for two classical competition solvers, simulated annealing (SA) and the Hamze–de Frietas–Selby solver (HFS). We identify key properties of these inputs that lead to poor performance by the classical solvers, and consider whether these properties might naturally arise in problems from real-world applications.

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Computational Power Consumption and Speedup

Power consumption for computation is a serious and growing issue for the world. We rely more and more on computing in everything we do as we try to satisfy our ever-increasing thirst for mobile computing, automation, machine intelligence, cloud computing, and increasingly powerful supercomputers. Highly specialized coprocessors such as D-Wave’s quantum processing units (QPUs) show promise in significantly increasing the power effciency of computing. In a recent study, D-Wave’s 2000-qubit system was shown to be up to 100 times more energy effcient than highly specialized algorithms on state-of-the-art classical computing servers when considering pure computation time, suggesting immediate relevance to large-scale energy efficient computing. 

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Quantum Annealing amid Local Ruggedness and Global Frustration
J. King, S. Yarkoni, J. Raymond, I. Oz dan, A. D. King, M. Mohammadi Nevisi, J. P. Hilton, and C. C. McGeoch

We introduce a problem class with two attributes crucial to the evaluation of quantum annealing processors: local ruggedness (i.e., tall, thin energy barriers in the energy landscape) so that quantum tunneling can be harnessed as a useful resource, and global frustration so that the problems are combinatorially challenging and representative of real-world inputs. We evaluate the new 2000-qubit D-Wave quantum processing unit (QPU) on these inputs, comparing it to software solvers that include both GPU-based solvers and a CPU-based solver which is highly tailored to the D-Wave topology. The D-Wave QPU solidly outperforms the software solvers: when we consider pure annealing time, the D-Wave QPU is three to four orders of magnitude faster than software solvers in both optimization and sampling evaluations. 

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Boosting integer factoring performance via quantum annealing offsets
Evgeny Andriyash, Zhengbing Bian, Fabian Chudak, Marshall Drew-Brook, Andrew D. King, William G. Macready, Aidan Roy

D-Wave quantum computing systems now allow a user to advance or delay the annealing path of individual qubits through the anneal offsets feature. Here we demonstrate the potential of this feature by using it in an integer factoring circuit. Offsets allow the user to homogenize dynamics of various computational elements in the circuit. This gives a remarkable improvement over baseline performance, in some cases making the computation more than 1000 times faster. 

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Limits on Parallel Speedup for Classical Ising Model Solvers

Why can’t we put together a million cores and make it run a million times faster? Parallel computing systems offer enormous potential for significant runtime speedups over computation by a single CPU core. However, many computational tasks cannot be effciently parallelized. We explore some practical limits to achieving parallel speedups, with reference to some classical optimization solvers that are competitors to D-Wave quantum computers.

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Partitioning Optimization Problems for Hybrid Classical/Quantum Execution
Michael Booth, Steven P. Reinhardt, and Aidan Roy

In this white paper we introduce qbsolv, a tool that solves large quadratic unconstrained binary optimization (QUBO) problems by partitioning into subproblems targeted for execution on a D-Wave system. Using a classical subproblem solver rather than quantum annealing, qbsolv delivers state-of-the-art numerical results and executes almost twice as fast as the best previously known implementation. We have released qbsolv as open-source software to foster greater use and experimentation in such partitioning solvers and to establish the QUBO form as a target for higher-level optimization interfaces. The software can be acccessed on GitHub at github.com/dwavesystems/qbsolv

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2013

Programming with D-Wave: Map Coloring Problem

Quantum computing, as implemented in the D-Wave system, is described by a simple but largely unfamiliar programming model. Using a simple map coloring problem this white paper describes the entire set of transformations needed to find solutions by executing a single quantum machine instruction (QMI) within this programming model. This “direct embedding” is one of several ways to program the D-Wave quantum computer. 

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D-Wave Publications

Selected Papers

Can quantum Monte Carlo simulate quantum annealing?

Evgeny Andriyash and Mohammad H. Amin

"Recent theoretical and experimental studies have suggested that quantum Monte Carlo (QMC) simulation can behave similarly to quantum annealing (QA)...Here, we compare incoherent tunneling and QMC escape using perturbation theory, which has much wider validity than WKB approximation. We show that the two do not scale the same way when there are multiple homotopy-inequivalent paths for tunneling. We demonstrate through examples that frustration can generate an exponential number of tunneling paths, which under certain conditions can lead to an exponential advantage for incoherent tunneling over classical QMC escape. We provide analytical and numerical evidence for such an advantage and show that it holds beyond perturbation theory."

(27 Mar 2017) Link to PDF.

Benchmarking Quantum Hardware for Training of Fully Visible Boltzmann Machines
Dmytro Korenkevych, Yanbo Xue, Zhengbing Bian, Fabian Chudak, William G. Macready, Jason Rolfe, Evgeny Andriyash

“Quantum annealing (QA) is a hardware-based heuristic optimization and sampling method applicable to discrete undirected graphical models. While similar to simulated annealing, QA relies on quantum, rather than thermal, effects to explore complex search spaces. For many classes of problems, QA is known to offer computational advantages over simulated annealing. Here we report on the ability of recent QA hardware to accelerate training of fully visible Boltzmann machines.”

(14 Nov 2016) https://arxiv.org/abs/1611.04528

Discrete Variational Autoencoders
Jason Tyler Rolfe

“Probabilistic models with discrete latent variables naturally capture datasets composed of discrete classes. However, they are difficult to train efficiently, since back propagation through discrete variables is generally not possible. We introduce a novel class of probabilistic models, comprising an undirected discrete component and a directed hierarchical continuous component, that can be trained efficiently using the variational autoencoder framework. The discrete component captures the distribution over the disconnected smooth manifolds induced by the continuous component. As a result, this class of models efficiently learns both the class of objects in an image, and their specific realization in pixels, from unsupervised data; and outperforms state-of-the-art methods on the permutation-invariant MNIST, OMNIGLOT, and Caltech-101 Silhouettes datasets.”

(7 Sep 2016) http://arxiv.org/abs/1609.02200

Degeneracy, degree, and heavy tails in quantum annealing

Andrew D. King, Emile Hoskinson, Trevor Lanting, Evgeny Andriyash, Mohammad H. Amin

"Both simulated quantum annealing and physical quantum annealing have shown the emergence of "heavy tails" in their performance as optimizers: The total time needed to solve a set of random input instances is dominated by a small number of very hard instances…On similar inputs designed to suppress local degeneracy, performance of a quantum annealing processor on hard instances improves by orders of magnitude at the 512-qubit scale, while classical performance remains relatively unchanged."

 (23 Dec 2015) Link to PDF.

Constructing SAT Filters with a Quantum Annealer

Adam Douglass, Andrew D. King, Jack Raymond

"Presented here is a case study of SAT filter construction with a focus on constraint satisfaction problems based on MAX-CUT clauses (Not-all-equal 3-SAT, 2-in-4-SAT, etc.) and frustrated cycles in the Ising model... Solutions are sampled using a D-Wave quantum annealer, and results are measured against classical approaches."

( 27 Oct 2015) http://link.springer.com/chapter/10.1007%2F978-3-319-24318-4_9

Benchmarking a quantum annealing processor with the time-to-target metric
James King, Sheir Yarkoni, Mayssam M. Nevisi, Jeremy P. Hilton, and Catherine C. McGeoch

In this paper we introduce the Time to Target (TTT) metric and compare the performance of the D-Wave 2X on a host of native hardware problems against highly optimized and tuned solvers. 

(20 Aug, 2015)  Link to PDF

Entanglement in a quantum annealing processor

T. Lanting et. al

In this paper we present experimental evidence that, during a critical portion of QA, qubits in the D-Wave processor become entangled and entanglement persists even as these systems reach equilibrium with a thermal environment. Our results provide an encouraging sign that quantum annealing is a viable technology for large-scale quantum computing.

Physical Review X (29 May 2014) https://journals.aps.org/prx/abstract/10.1103/PhysRevX.4.021041

Thermally assisted quantum annealing of a 16-qubit problem

N G Dickson et al. 

"Efforts to develop useful quantum computers have been blocked primarily by environmental noiseHere we examine the environment’s effect on quantum annealing using 16 qubits of a superconducting quantum processor. For a problem instance with an isolated small-gap anticrossing between the lowest two energy levels, we experimentally demonstrate that, even with annealing times eight orders of magnitude longer than the predicted single-qubit decoherence time, the probabilities of performing a successful computation are similar to those expected for a fully coherent system. Moreover, for the problem studied, we show that quantum annealing can take advantage of a thermal environment to achieve a speedup factor of up to 1,000 over a closed system."

Nature Communications, 1903 (May 21 2013) http://www.nature.com/ncomms/journal/v4/n5/full/ncomms2920.html

Quantum annealing with manufactured spins

M. W. Johnson et al. 

Many interesting but practically intractable problems can be reduced to that of finding the ground state of a system of interacting spins; however, finding such a ground state remains computationally difficult..Here we use quantum annealing to find the ground state of an artificial Ising spin system comprising an array of eight superconducting flux quantum bits with programmable spin–spin couplings. We observe a clear signature of quantum annealing, distinguishable from classical thermal annealing through the temperature dependence of the time at which the system dynamics freezes.

Nature 473, 194-198 (12 May 2011) http://www.nature.com/nature/journal/v473/n7346/full/nature10012.html

2017

From Near to Eternity: Spin-glass planting, tiling puzzles, and constraint satisfaction problems

Firas Hamze (D-Wave); Darryl C. Jacob, Andrew J. Ochoa (Texas A&M University); Wenlong Wang (KTH Royal Institute of Technology,Texas A&M University); and Helmut G. Katzgraber (Texas A&M University, 1QBit Technologies)

We present a methodology for generating Ising Hamiltonians of tunable complexity and with a priori known ground states based on a decomposition of the model graph into edge-disjoint subgraphs. The idea is illustrated with a spin-glass model defined on a cubic lattice, where subproblems, whose couplers are restricted to the two values {-1,+1}, are specified on unit cubes and are parametrized by their local degeneracy. The construction is shown to be equivalent to a type of three-dimensional constraint satisfaction problem known as the tiling puzzle. By varying the proportions of subproblem types, the Hamiltonian can span a dramatic range of typical computational complexity, from fairly easy to many orders of magnitude more difficult than prototypical bimodal and Gaussian spin glasses in three space dimensions. We corroborate this behavior via experiments with different algorithms and discuss generalizations and extensions to different types of graphs.

(Nov 15, 2017) https://arxiv.org/abs/1711.04083

Experimental demonstration of perturbative anticrossing mitigation using nonuniform driver Hamiltonians

Trevor Lanting, Andrew D. King, Bram Evert, and Emile Hoskinson

"Perturbative anticrossings have long been identified as a potential computational bottleneck for quantum annealing. This bottleneck can appear, for example, when a uniform transverse driver Hamiltonian is applied to each qubit. Previous theoretical research sought to alleviate such anticrossings by adjusting the transverse driver Hamiltonians on individual qubits according to a perturbative approximation. Here we apply this principle to a physical implementation of quantum annealing in a D-Wave 2000Q system. We use samples from the quantum annealing hardware and per-qubit anneal offsets to produce nonuniform driver Hamiltonians. On small instances with severe perturbative anticrossings, our algorithm yields an increase in minimum eigengaps, ground-state success probabilities, and escape rates from metastable valleys. We also demonstrate that the same approach can mitigate biased sampling of degenerate ground states."

Physical Review A (16 Oct 2017) https://journals.aps.org/pra/abstract/10.1103/PhysRevA.96.042322

Experimental demonstration of perturbative anticrossing mitigation using non-uniform driver Hamiltonians

Trevor Lanting, Andrew D. King, Bram Evert, Emile Hoskinson

"Perturbative anticrossings have long been identified as a potential computational bottleneck for quantum annealing. This bottleneck can appear, for example, when a uniform transverse driver Hamiltonian is applied to each qubit. Previous theoretical research sought to alleviate such anticrossings by adjusting the transverse driver Hamiltonians on individual qubits according to a perturbative approximation. Here we apply this principle to a physical implementation of quantum annealing in a D-Wave 2000Q system. We use samples from the quantum annealing hardware and per-qubit anneal offsets to produce nonuniform driver Hamiltonians. On small instances with severe perturbative anticrossings, our algorithm yields an increase in minimum eigengaps, ground state success probabilities, and escape rates from metastable valleys. We also demonstrate that the same approach can mitigate biased sampling of degenerate ground states."

(10 Aug 2017) https://arxiv.org/abs/1708.03049

Can quantum Monte Carlo simulate quantum annealing?

Evgeny Andriyash and Mohammad H. Amin

"Recent theoretical and experimental studies have suggested that quantum Monte Carlo (QMC) simulation can behave similarly to quantum annealing (QA)...Here, we compare incoherent tunneling and QMC escape using perturbation theory, which has much wider validity than WKB approximation. We show that the two do not scale the same way when there are multiple homotopy-inequivalent paths for tunneling. We demonstrate through examples that frustration can generate an exponential number of tunneling paths, which under certain conditions can lead to an exponential advantage for incoherent tunneling over classical QMC escape. We provide analytical and numerical evidence for such an advantage and show that it holds beyond perturbation theory."

(27 Mar 2017) Link to PDF.

Quantum eigenstate tomography with qubit tunneling spectroscopy

Anatoly Yu. Smirnov and Mohammad H. Amin

"Measurement of the energy eigenvalues (spectrum) of a multi-qubit system has recently become possible by qubit tunneling spectroscopy (QTS). In the standard QTS experiments, an incoherent probe qubit is strongly coupled to one of the qubits of the system in such a way that its incoherent tunneling rate provides information about the energy eigenvalues of the original (source) system. In this paper, we generalize QTS by coupling the probe qubit to many source qubits. We show that by properly choosing the couplings, one can perform projective measurements of the source system energy eigenstates in an arbitrary basis, thus performing quantum eigenstate tomography. As a practical example of a limited tomography, we apply our scheme to probe the eigenstates of a kink in a frustrated transverse Ising chain."

(25 Feb 2017) Link to PDF.

2016

Benchmarking Quantum Hardware for Training of Fully Visible Boltzmann Machines
Dmytro Korenkevych, Yanbo Xue, Zhengbing Bian, Fabian Chudak, William G. Macready, Jason Rolfe, Evgeny Andriyash

“Quantum annealing (QA) is a hardware-based heuristic optimization and sampling method applicable to discrete undirected graphical models. While similar to simulated annealing, QA relies on quantum, rather than thermal, effects to explore complex search spaces. For many classes of problems, QA is known to offer computational advantages over simulated annealing. Here we report on the ability of recent QA hardware to accelerate training of fully visible Boltzmann machines.”

(14 Nov 2016) https://arxiv.org/abs/1611.04528

Discrete Variational Autoencoders
Jason Tyler Rolfe

“Probabilistic models with discrete latent variables naturally capture datasets composed of discrete classes. However, they are difficult to train efficiently, since back propagation through discrete variables is generally not possible. We introduce a novel class of probabilistic models, comprising an undirected discrete component and a directed hierarchical continuous component, that can be trained efficiently using the variational autoencoder framework. The discrete component captures the distribution over the disconnected smooth manifolds induced by the continuous component. As a result, this class of models efficiently learns both the class of objects in an image, and their specific realization in pixels, from unsupervised data; and outperforms state-of-the-art methods on the permutation-invariant MNIST, OMNIGLOT, and Caltech-101 Silhouettes datasets.”

(7 Sep 2016) http://arxiv.org/abs/1609.02200

Global warming: Temperature estimation in annealers

Jack Raymond, Sheir Yarkoni, Evgeny Andriyash

"Sampling from a Boltzmann distribution is NP-hard and so requires heuristic approaches. Quantum annealing is one promising candidate. The failure of annealing dynamics to equilibrate on practical time scales is a well understood limitation, but does not always prevent a heuristically useful distribution from being generated. In this paper we evaluate several methods for determining a useful operational temperature range for annealers.

(2 Jun 2016) Download PDF.

Mapping constrained optimization problems to quantum annealing with application to fault diagnosis

Zhengbing Bian, Fabian Chudak, Robert Israel, Brad Lackey, William G. Macready, Aidan Roy

Current quantum annealing (QA) hardware suffers from practical limitations such as finite temperature, sparse connectivity, small qubit numbers, and control error. We propose new algorithms for mapping boolean constraint satisfaction problems (CSPs) onto QA hardware mitigating these limitations. In particular we develop a new embedding algorithm for mapping a CSP onto a hardware Ising model with a fixed sparse set of interactions, and propose two new decomposition algorithms for solving problems too large to map directly into hardware.

(10 Mar 2016) http://arxiv.org/abs/1603.03111

Quantum Boltzmann Machine

Mohammad H. Amin, Evgeny Andriyash, Jason Rolfe, Bohdan Kulchytskyy, Roger Melko

Inspired by the success of Boltzmann Machines based on classical Boltzmann distribution, we propose a new machine learning approach based on quantum Boltzmann distribution of a transverse-field Ising Hamiltonian. Due to the non-commutative nature of quantum mechanics, the training process of the Quantum Boltzmann Machine (QBM) can become nontrivial. We circumvent the problem by introducing bounds on the quantum probabilities. This allows us to train the QBM efficiently by sampling. We show examples of QBM training with and without the bound, using exact diagonalization, and compare the results with classical Boltzmann training. We also discuss the possibility of using quantum annealing processors like D-Wave for QBM training and application.

(8 Jan 2016) http://arxiv.org/abs/1601.02036

 

2015

Degeneracy, degree, and heavy tails in quantum annealing

Andrew D. King, Emile Hoskinson, Trevor Lanting, Evgeny Andriyash, Mohammad H. Amin

"Both simulated quantum annealing and physical quantum annealing have shown the emergence of "heavy tails" in their performance as optimizers: The total time needed to solve a set of random input instances is dominated by a small number of very hard instances…On similar inputs designed to suppress local degeneracy, performance of a quantum annealing processor on hard instances improves by orders of magnitude at the 512-qubit scale, while classical performance remains relatively unchanged."

 (23 Dec 2015) Link to PDF.

Fast clique minor generation in Chimera qubit connectivity graphs
Constructing SAT Filters with a Quantum Annealer

Adam Douglass, Andrew D. King, Jack Raymond

"Presented here is a case study of SAT filter construction with a focus on constraint satisfaction problems based on MAX-CUT clauses (Not-all-equal 3-SAT, 2-in-4-SAT, etc.) and frustrated cycles in the Ising model... Solutions are sampled using a D-Wave quantum annealer, and results are measured against classical approaches."

( 27 Oct 2015) http://link.springer.com/chapter/10.1007%2F978-3-319-24318-4_9

Performance of a quantum annealer on range-limited constraint satisfaction problems

A.D.King, T.Lanting, and R.Harris

(3 Sep 2015) http://arxiv.org/pdf/1502.02098.pdf

Benchmarking a quantum annealing processor with the time-to-target metric
James King, Sheir Yarkoni, Mayssam M. Nevisi, Jeremy P. Hilton, and Catherine C. McGeoch

In this paper we introduce the Time to Target (TTT) metric and compare the performance of the D-Wave 2X on a host of native hardware problems against highly optimized and tuned solvers. 

(20 Aug, 2015)  Link to PDF

Searching for quantum speedup in quasistatic quantum annealers

Mohammad H. Amin

(13 Mar 2015) http://arxiv.org/pdf/1503.04216.pdf

Computational Role of Collective Tunneling in a Quantum Annealer

Sergio Boixo et al.

(19 Feb 2015) http://arxiv.org/pdf/1411.4036.pdf 

2014

Discrete optimization using quantum annealing on sparse Ising models
Zhengbing Bian, Fabian Chudak, Robert Israel, Brad Lackey, William G. Macready and Aidan Roy
A practical heuristic for finding graph minors

Jun Cai, Bill Macready, Aidan Roy

(12 Jun 2014) http://arxiv.org/pdf/1406.2741.pdf

Entanglement in a quantum annealing processor

T. Lanting et. al

In this paper we present experimental evidence that, during a critical portion of QA, qubits in the D-Wave processor become entangled and entanglement persists even as these systems reach equilibrium with a thermal environment. Our results provide an encouraging sign that quantum annealing is a viable technology for large-scale quantum computing.

Physical Review X (29 May 2014) https://journals.aps.org/prx/abstract/10.1103/PhysRevX.4.021041

Architectural considerations in the design of a superconducting quantum annealing processor

P. Bunyk et al.
Pre-print (21 Jan 2014) http://arxiv.org/pdf/1401.5504v1

2013

Evidence for temperature dependent spin-diffusion as a mechanism of intrinsic flux noise in SQUIDs

T. Lanting et al.

(23 Dec 2013) http://arxiv.org/pdf/1306.1512.pdf 

Thermally assisted quantum annealing of a 16-qubit problem

N G Dickson et al. 

"Efforts to develop useful quantum computers have been blocked primarily by environmental noiseHere we examine the environment’s effect on quantum annealing using 16 qubits of a superconducting quantum processor. For a problem instance with an isolated small-gap anticrossing between the lowest two energy levels, we experimentally demonstrate that, even with annealing times eight orders of magnitude longer than the predicted single-qubit decoherence time, the probabilities of performing a successful computation are similar to those expected for a fully coherent system. Moreover, for the problem studied, we show that quantum annealing can take advantage of a thermal environment to achieve a speedup factor of up to 1,000 over a closed system."

Nature Communications, 1903 (May 21 2013) http://www.nature.com/ncomms/journal/v4/n5/full/ncomms2920.html

Adiabatic quantum optimization with qudits

M.H. Amin et al. 
Quant. Inf. Proc. 12, 1819-1829 (April 2013) doi:10.1007/s11128-012-0480-x/QuantInfProc12/

Tunneling spectroscopy using a probe qubit

A. J. Berkley et al. 
Phys. Rev. B 87, 020502(R) (2013) doi:10.1103/PhysRevB.87.020502

2012

Algorithmic approach to adiabatic quantum optimization

N. G. Dickson et al. 
Phys. Rev. A 85, 032303 (2012) doi:10.1103/PhysRevA.85.032303arXiv:1108.33031

Approximate diagonalization method for large-scale Hamiltonians

M. H. Amin et al.
Phys. Rev. A 86, 052314 (2012) doi:10.1103/PhysRevA.86.052314

2011

Probing high-frequency noise with macroscopic resonant tunneling

T. Lanting et al. 
Physical Review B PhysRevB.83.180502 arXiv:1103.1931

Quantum annealing with manufactured spins

M. W. Johnson et al. 

Many interesting but practically intractable problems can be reduced to that of finding the ground state of a system of interacting spins; however, finding such a ground state remains computationally difficult..Here we use quantum annealing to find the ground state of an artificial Ising spin system comprising an array of eight superconducting flux quantum bits with programmable spin–spin couplings. We observe a clear signature of quantum annealing, distinguishable from classical thermal annealing through the temperature dependence of the time at which the system dynamics freezes.

Nature 473, 194-198 (12 May 2011) http://www.nature.com/nature/journal/v473/n7346/full/nature10012.html

Importance of Explicit Vectorization for CPU and GPU Software Performance

N. Dickson et al. 
Journal of Computational Physics arXiv:1004.0024

The Ising model: teaching an old problem new tricks

Z. Bian et al. 
Link to PDF

Investigating the Performance of an Adiabatic Quantum Optimization Processor

K. Karimi et al. 
Quantum Information Processing arXiv:1006.4147

Does adiabatic quantum optimization fail for NP-complete problems?

N. G. Dickson et al.
Phys. Rev. Lett. 106, Issue 5, 050502 arXiv:1010.0669

2010

A scalable control system for a superconducting adiabatic quantum optimization processor

M. W. Johnson et al.
Supercond. Sci. Technol. 23, 065004 arXiv:0907.3757

Experimental Demonstration of a Robust and Scalable Flux Qubit

R. Harris et al.
Physical Review B 81, 134510 (2010) arXiv:0909.4321

High-Performance Physics Simulations Using Multi-Core CPUs and GPGPUs in a Volunteer Computing Context

K. Karimi et al.
International Journal of High Performance Computing Applications, doi: 10.1177/1094342010372928arXiv:1004.0023

Robust Parameter Selection for Parallel Tempering

F. Hamze et al
International Journal of Modern Physics C, Volume 21, issue 5 (2010) arXiv:1004.2840

Experimental Investigation of an Eight Qubit Unit Cell in a Superconducting Optimization Processor

R. Harris et al.
Phys. Rev. B 82, 024511 (2010) arXiv:1004.1628

Cotunneling in pairs of coupled flux qubits

T. Lanting et al.
Phys. Rev. B 82, 060512(R) (2010) arXiv:1006.0028

A scalable readout system for a superconducting adiabatic quantum optimization system

A. J. Berkley et al.
Supercond. Sci. Technol. 23, 105014 (2010) arXiv:0905.0891

2009

Non-Markovian incoherent quantum dynamics of a two-state system

M. H. S. Amin et al.
Phys. Rev. B 80, 214302 (2009) arXiv:0907.4797

Decoherence in adiabatic quantum computation
M. H. S. Amin et al.
Phys. Rev. A 79, 022107 (2009) arXiv:0708.0384
A Compound Josephson Junction Coupler for Flux Qubits With Minimal Crosstalk
R. Harris et al.
Phys. Rev. B 80, 052506 (2009) arXiv:0904.3784
Landau-Zener transitions in a superconducting flux qubit
J. Johansson et al.
Phys. Rev. B 80, 012507 (2009) arXiv:0807.0797
Geometrical dependence of the low-frequency noise in superconducting flux qubits
T. Lanting et al.
Phys. Rev. B 79, 060509 (2009) arXiv:0812.0378
Consistency of the Adiabatic Theorem
M. H. S. Amin et al.

Phys. Rev. Lett. 102, 220401 (2009) arXiv:0810.4335

Landau-Zener transitions in the presence of spin environment

A. T. S. Wan et al.
Int. J. Quant. Inf. 7, 725 (2009) arXiv:cond-mat/0703085

First Order Quantum Phase Transition in Adiabatic Quantum Computation

M. H. S. Amin et al.
Phys. Rev. A 80, 062326 (2009) arXiv:0904.1387

The Role of Single Qubit Decoherence Time in Adiabatic Quantum Computation
M. H. S. Amin et al.

Phys. Rev. A 80, 022303 (2009) arXiv:0803.1196

2008

Minor-Embedding in Adiabatic Quantum Computation: I. The Parameter Setting Problem
V. Choi et al.
Quantum Information Processing 7, pp193-209 (2008) arXiv:0804.4884
Macroscopic Resonant Tunneling in the Presence of Low Frequency Noise
M. H. S. Amin et al.
Phys. Rev. Lett. 100, 197001 (2008) arXiv:0712.0845
On the construction of model Hamiltonians for adiabatic quantum computing and its application to finding low energy conformations of lattice protein models
A. Perdomo et al.
Physical Review A 78, 012320 (2008) arXiv:0801.3625
Effect of Local Minima on Adiabatic Quantum Optimization
M. H. S. Amin et al.
Phys. Rev. Lett. 100, 130503 (2008) arXiv:0709.0528
Thermally Assisted Adiabatic Quantum Computation
M. H. S. Amin et al.
Phys. Rev. Lett. 100, 060503 (2008) arXiv:cond-mat/0609332
Probing Noise in Flux Qubits via Macroscopic Resonant Tunneling
R. Harris et al.
Phys. Rev. Lett. 101, 117003 (2008) arXiv:0712.0838
Realizable Hamiltonians for Universal Adiabatic Quantum Computers
J. D. Biamonte et al.
Phys. Rev. A 78, 012352 (2008) aXiv:0704.1287

2007

Sign- and Magnitude-Tunable Coupler for Superconducting Flux Qubits
R. Harris et al.
Phys. Rev. Lett. 98, 177001 (2007) arXiv:cond-mat/0608253
A Characterization of global entanglement
P. J. Love et al.
Quant. Info. Proc. 6, 187 (2007) arXiv:quant-ph/0602143

2006

Rabi oscillations in systems with small anharmonicity
M. H. S. Amin et al.
Low Temp. Phys. 32, 198 (2006) arXiv:cond-mat/0407080
Four-Qubit Device with Mixed Couplings
M. Grajcar et al.
Phys. Rev. Lett. 96, 047006 (2006) arXiv:cond-mat/0509557
Adiabatic quantum computation with flux qubits, first experimental results
S. H. W. van der Ploeg et al.
IEEE Trans. App. Supercond. 17, 113 (2006) arXiv:cond-mat/0702580

2005

Simulated Quantum Computation of Molecular Energies
A. Aspuru-Guzik et al.
Science 309 p. 1704, (2005) arXiv:quant-ph/0604193
Hamiltonian for coupled flux qubits
A. M. van den Brink et al.
Phys. Rev. B, 71, 064503 (2005) arXiv:cond-mat/0310425
Quantum nondemolition charge measurement of a Josephson qubit
M. H. S. Amin et al.
Phys. Rev. B 71, 140505 (2005) arXiv:cond-mat/0412286
Silent phase qubit based on d -wave Josephson junctions
M. H. S. Amin et al.
Phys. Rev. B 71, 064516 (2005) arXiv:cond-mat/0310224
Flux qubit in charge-phase regime
M.H.S. Amin et al.
Phys. Rev. B 71, 024504 (2005) arXiv:cond-mat/0311220
Mediated tunable coupling of flux qubits
A. M. van den Brink et al.
New J. Phys. 7 230 (2005) arXiv:cond-mat/0501148
Direct Josephson coupling between superconducting flux qubits
M. Grajcar et al.
Phys. Rev. B 72, (2005) 020503(R) arXiv:cond-mat/0501085

2004

Evidence for Entangled States of Two Coupled Flux Qubits
A. Izmalkov et al.
Phys. Rev. Lett. 93, 037003 (2004) arXiv:cond-mat/0312332
Low-frequency measurement of the tunneling amplitude in a flux qubit
M. Grajcar et al.
Phys. Rev. B 69, 060501 (2004) arXiv:cond-mat/0303657
Quasiparticle Decoherence in d-Wave Superconducting Qubits
M. H. S. Amin et al.
Phys. Rev. Lett. 92, 017001 (2004) arXiv:cond-mat/0304255
Observation of macroscopic Landau-Zener tunneling in a superconducting device
A. Izmalkov et al.
Euro. Phys. Lett. 65, 844, (2004) arXiv:cond-mat/0307506
Wigner distribution function formalism for superconductors and collisionless dynamics of the superconducting order parameter
M. H. S. Amin et al.
Low Temp. Phys. 30, 661 (2004) arXiv:cond-mat/0404401
Superconducting quantum storage and processing
M. H. S. Amin et al.
IEEE International Solid State Circuit Conference (ISSCC), Tech. Dig., p296(2004)

2003

Anomalous current-phase relation as basis for HTS qubit
S. A. Charlebois et al.
Proceedings of the European Conference on Applied Superconductivity (EUCAS 2003)
Nonequilibrium quasiclassical theory for Josephson structures
M. H. S. Amin, et al.
Phys. Rev. B 68, 054505 (2003) arXiv:cond-mat/0207724
Josephson-phase qubit without tunneling
M. H. S. Amin et al.
Phys. Rev. B 67, 100508 (2003) arXiv:cond-mat/0211638
Dynamical Effects of an Unconventional Current-Phase Relation in YBCO dc SQUIDs
T. Lindstrom et al.
Phys. Rev. Lett. 90, 117002 (2003) arXiv:cond-mat/0303144
Quasiclassical Calculations of spontaneous current in restricted geometries
M. H. S. Amin et al.
"Towards the Controllable Quantum States" edited by H. Takayanagi and J. Nitta, World Scientific Publishing Co. (2003), arXiv:cond-mat/0207617
Continuous Monitoring of Rabi Oscillations in a Josephson Flux Qubit
E. Il'ichev et al.
Phys. Rev. Lett. 91, 097906 (2003) arXiv:cond-mat/0303433
Theory of weak continuous measurements in a strongly driven quantum bit
A. Y. Smirnov, et al.
Phys. Rev. B 68, 134514 (2003) arXiv:cond-mat/0306004
Tunable coupling of superconducting qubits
A. Blais et al.
Phys. Rev. Lett. 90, 127901 (2003) arXiv:cond-mat/0207112

2002

Multi-Terminal Superconducting Phase Qubit
M. H. S. Amin et al.
Physica C 368, 310 (2002) arXiv:cond-mat/0109382
High Temperature PI/2-SQUID
M. H. S. Amin et al.
IEEE Tran. Appl. Supercond. 12, 1877 (2002) arXiv:cond-mat/0107370
Time reversal breaking states and spontaneous current pattern in Josephson junctions of d-wave superconductors
M. H. S. Amin et al.
"New Trends in Superconductivity", edited by J.F. Annett and S. Kruchinin, Kluwer, Academic Publishers (2002).
Quasiclassical theory of spontaneous currents at surfaces and interfaces of d-Wave superconductors
M. H. S. Amin et al.
Physica B 318, 162 (2002) arXiv:cond-mat/0105486
Low-frequency characterization of quantum tunneling in flux qubits
Y. S. Greenberg et al.
Phys. Rev. B 66, 214525 (2002) arXiv:cond-mat/0208076
d+is versus d+id time reversal symmetry breaking states in finite size systems
M. H. S. Amin et al.
Phys. Rev. B 66, 174515 (2002) arXiv:cond-mat/0205495
DC-SQUID based on the mesoscopic multi-terminal Josephson junction
M. H. S. Amin et al.
Physica C 372-376P1, 184 (2002); Special issue: Proceeding of the 5th European Conference on Applied Superconductivity, Copenhagen, Denmark, (Sep. 2001) arXiv:cond-mat/0109384

2001

Mesoscopic multi-terminal Josephson structures. I. effects of nonlocal weak coupling
M. H. S. Amin et al.
Low Temp Phys. 27, 616 (2001) arXiv:cond-mat/0109333
Degenerate Ground State in a Mesoscopic YBa2Cu3O7-x Grain Boundary Josephson Junction
E. Il'ichev et al.
Phys. Rev. Lett. 86, 5369 (2001) arXiv:cond-mat/0102404
Mechanisms of spontaneous current generation in an inhomogeneous d-wave superconductor
M. H. S. Amin et al.
Phys. Rev. B 63, 212502 (2001) arXiv:cond-mat/0011416

Third Party Publications

Selected Papers

A deceptive step towards quantum speedup detection

Salvatore Mandrà, Helmut G. Katzgraber

"There have been multiple attempts to design synthetic benchmark problems with the goal of detecting quantum speedup in current quantum annealing machines. To date, classical heuristics have consistently outperformed quantum-annealing based approaches. Here we introduce a class of problems based on frustrated cluster loops - deceptive cluster loops - for which all currently known state-of-the-art classical heuristics are outperformed by the D-Wave 2000Q quantum annealing machine. While there is a sizable constant speedup over all known classical heuristics, a noticeable improvement in the scaling remains elusive. These results represent the first steps towards a detection of potential quantum speedup, albeit without a scaling improvement and for synthetic benchmark problems." 

(4 Nov 2017) https://arxiv.org/abs/1711.01368

Solving a Higgs optimization problem with quantum annealing for machine learning

Alex Mott, Joshua Job, Jean-Roch Vlimant, Daniel Lidar: (University of Southern California), and Maria Spiropulu: (California Institute of Technology)

"The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical, annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model." 

(18 Oct 2017) http://www.nature.com/nature/journal/v550/n7676/full/nature24047.html

Traffic flow optimization using a quantum annealer

Florian Neukart, David Von Dollen, Gabriele Compostella, Christian Seidel, Sheir Yarkoni, and Bob Parney (Volkswagen Group of America, San Francisco; Volkswagen Data Lab, Munich, Germany; D-Wave Systems Inc., Burnaby, Canada)

"Quantum annealing algorithms belong to the class of meta-heuristic tools, applicable for solving binary optimization problems. Hardware implementations of quantum annealing, such as the quantum processing units (QPUs) produced by D-Wave Systems, have been subject to multiple analyses in research, with the aim of characterizing the technology’s usefulness for optimization and sampling tasks. In this paper, we present a real-world application that uses quantum technologies. Specifically, we show how to map certain parts of a real-world traffic flow optimization problem to be suitable for quantum annealing. We show that time-critical optimization tasks, such as continuous redistribution of position data for cars in dense road networks, are suitable candidates for quantum computing. Due to the limited size and connectivity of current-generation D-Wave QPUs, we use a hybrid quantum and classical approach to solve the traffic flow problem."

(9 Aug 2017) Link to PDF.

Graph Partitioning using Quantum Annealing on the D-Wave System

Hayato Ushijima-Mwesigwa, Christian F. A. Negre, and Susan M. Mniszewski 

"In this work, we explore graph partitioning (GP) using quantum annealing on the D-Wave 2X machine. Motivated by a recently proposed graph-based electronic structure theory applied to quantum molecular dynamics (QMD) simulations, graph partitioning is used for reducing the calculation of the density matrix into smaller subsystems rendering the calculation more computationally efficient...Results for graph partitioning using quantum and hybrid classical-quantum approaches are shown to equal or out-perform current “state of the art” methods. "

(4 May 2017) https://arxiv.org/abs/1705.03082v1

Nonnegative/binary matrix factorization with a D-Wave quantum annealer

Daniel O’Malley, Velimir V. Vesselinov, Boian S. Alexandrov, and Ludmil B. Alexandrov (Los Alamos National Laboratory)

"D-Wave quantum annealers represent a novel computational architecture and have attracted significant interest, but have been used for few real-world computations. Machine learning has been identified as an area where quantum annealing may be useful. Here, we show that the D-Wave 2X can be effectively used as part of an unsupervised machine learning method. This method can be used to analyze large datasets. The D-Wave only limits the number of features that can be extracted from the dataset. We apply this method to learn the features from a set of facial images."

(5 April 2017) https://arxiv.org/abs/1704.01605

Not Magic…Quantum
Los Alamos National Laboratory, 1663 Magazine

Los Alamos National Laboratory, 1663 Magazine, July 2016

"Quantum computers have long been on the horizon as conventional computing technologies approach their physical limits. While general-purpose quantum computers remain on the horizon for the time being, a special kind of quantum computer already exists and could be a game changer for simulation and computing tools in support of Los Alamos National Laboratory’s mission of stockpile stewardship without nuclear testing. It may also enable a slew of broader national security and computer science applications. But first, it will undoubtedly draw a vibrant community of top creative thinkers in many scientific fields to Los Alamos."

Link to PDF

What is the Computational Value of Finite Range Tunneling?

Vasil S. Denchev, Sergio Boixo, Sergei V. Isakov, Nan Ding, Ryan Babbush, Vadim Smelyanskiy, John Martinis, Hartmut Neven (Google scientists)

"Quantum annealing (QA) has been proposed as a quantum enhanced optimization heuristic exploiting tunneling. Here, we demonstrate how finite range tunneling can provide considerable computational advantage. For a crafted problem designed to have tall and narrow energy barriers separating local minima, the D-Wave 2X quantum annealer achieves significant runtime advantages relative to Simulated Annealing (SA). For instances with 945 variables this results in a time-to-99%-success-probability that is ∼108 times faster than SA running on a single processor core. "

(30 Dec 2015) http://arxiv.org/abs/1512.02206

Multiple Query Optimization on the D-Wave 2X Adiabatic Quantum Computer

Immanuel Trummer and Christoph Koch, (E ́cole Polytechnique Federale de Lausanne scientists) 

"In this paper, we tackle the problem of multiple query optimization (MQO)...While the problem sizes that can be treated are currently limited, we already find a class of problem instances where the quantum annealer is three orders of magnitude faster than other approaches."

(23 Oct 2015) http://arxiv.org/pdf/1510.06437v1.pdf

 

Application of quantum annealing to training of deep neural networks

Steven H. Adachi, Maxwell P. Henderson (Lockheed Martin scientists)

"We investigated an alternative approach [to Deep Learning] that estimates model expectations of Restricted Boltzmann Machines using samples from a D-Wave quantum annealing machine...In our tests we found that the quantum sampling-based training approach achieves comparable or better accuracy with significantly fewer iterations of generative training than conventional CD-based training."

(Oct 2015) Mathpubs: http://www.mathpubs.com/detail/1510.06356v1/Application-of-Quantum-Annea...

Solving the Optimal Trading Trajectory Problem Using a Quantum Annealer

Gili Rosenberg, Poya Haghnegahdar, Phil Goddard, Peter Carr, Kesheng Wu, Marcos López de Prado

“We solve a multi-period portfolio optimization problem using D-Wave Systems' quantum annealer. We derive a formulation of the problem, discuss several possible integer encoding schemes, and present numerical examples that show high success rates.”

(22 Aug 2015) http://arxiv.org/abs/1508.06182

2017

A deceptive step towards quantum speedup detection

Salvatore Mandrà, Helmut G. Katzgraber

"There have been multiple attempts to design synthetic benchmark problems with the goal of detecting quantum speedup in current quantum annealing machines. To date, classical heuristics have consistently outperformed quantum-annealing based approaches. Here we introduce a class of problems based on frustrated cluster loops - deceptive cluster loops - for which all currently known state-of-the-art classical heuristics are outperformed by the D-Wave 2000Q quantum annealing machine. While there is a sizable constant speedup over all known classical heuristics, a noticeable improvement in the scaling remains elusive. These results represent the first steps towards a detection of potential quantum speedup, albeit without a scaling improvement and for synthetic benchmark problems." 

(4 Nov 2017) https://arxiv.org/abs/1711.01368

Solving a Higgs optimization problem with quantum annealing for machine learning

Alex Mott, Joshua Job, Jean-Roch Vlimant, Daniel Lidar: (University of Southern California), and Maria Spiropulu: (California Institute of Technology)

"The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical, annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model." 

(18 Oct 2017) http://www.nature.com/nature/journal/v550/n7676/full/nature24047.html

Traffic flow optimization using a quantum annealer

Florian Neukart, David Von Dollen, Gabriele Compostella, Christian Seidel, Sheir Yarkoni, and Bob Parney (Volkswagen Group of America, San Francisco; Volkswagen Data Lab, Munich, Germany; D-Wave Systems Inc., Burnaby, Canada)

"Quantum annealing algorithms belong to the class of meta-heuristic tools, applicable for solving binary optimization problems. Hardware implementations of quantum annealing, such as the quantum processing units (QPUs) produced by D-Wave Systems, have been subject to multiple analyses in research, with the aim of characterizing the technology’s usefulness for optimization and sampling tasks. In this paper, we present a real-world application that uses quantum technologies. Specifically, we show how to map certain parts of a real-world traffic flow optimization problem to be suitable for quantum annealing. We show that time-critical optimization tasks, such as continuous redistribution of position data for cars in dense road networks, are suitable candidates for quantum computing. Due to the limited size and connectivity of current-generation D-Wave QPUs, we use a hybrid quantum and classical approach to solve the traffic flow problem."

(9 Aug 2017) Link to PDF.

Graph Partitioning using Quantum Annealing on the D-Wave System

Hayato Ushijima-Mwesigwa, Christian F. A. Negre, and Susan M. Mniszewski 

"In this work, we explore graph partitioning (GP) using quantum annealing on the D-Wave 2X machine. Motivated by a recently proposed graph-based electronic structure theory applied to quantum molecular dynamics (QMD) simulations, graph partitioning is used for reducing the calculation of the density matrix into smaller subsystems rendering the calculation more computationally efficient...Results for graph partitioning using quantum and hybrid classical-quantum approaches are shown to equal or out-perform current “state of the art” methods. "

(4 May 2017) https://arxiv.org/abs/1705.03082v1

Nonnegative/binary matrix factorization with a D-Wave quantum annealer

Daniel O’Malley, Velimir V. Vesselinov, Boian S. Alexandrov, and Ludmil B. Alexandrov (Los Alamos National Laboratory)

"D-Wave quantum annealers represent a novel computational architecture and have attracted significant interest, but have been used for few real-world computations. Machine learning has been identified as an area where quantum annealing may be useful. Here, we show that the D-Wave 2X can be effectively used as part of an unsupervised machine learning method. This method can be used to analyze large datasets. The D-Wave only limits the number of features that can be extracted from the dataset. We apply this method to learn the features from a set of facial images."

(5 April 2017) https://arxiv.org/abs/1704.01605

A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers

Oak Ridge National Laboratory: Thomas E. Potok, Catherine Schuman, Steven R. Young, Robert M. Patton; USC Information Sciences Institute: Federico Spedalieri, Jeremy Liu, Ke-Thia Yao; University of Tennessee: Garrett Rose, Gangotree Chakma

"Current Deep Learning approaches have been very successful using convolutional neural networks (CNN) trained on large graphical processing units (GPU)-based computers. Three limitations of this approach are: 1) they are based on a simple layered network topology, i.e., highly connected layers, without intra-layer connections; 2) the networks are manually configured to achieve optimal results, and 3) the implementation of neuron model is expensive in both cost and power. In this paper, we evaluate deep learning models using three different computing architectures to address these problems: quantum computing to train complex topologies, high performance computing (HPC) to automatically determine network topology, and neuromorphic computing for a low-power hardware implementation. We use the MNIST dataset for our experiment, due to input size limitations of current quantum computers. Our results show the feasibility of using the three architectures in tandem to address the above deep learning limitations. We show a quantum computer can find high quality values of intra-layer connections weights, in a tractable time as the complexity of the network increases; a high performance computer can find optimal layer-based topologies; and a neuromorphic computer can represent the complex topology and weights derived from the other architectures in low power memristive hardware."

(15 Mar 2017) https://arxiv.org/abs/1703.05364

2016

Not Magic…Quantum
Los Alamos National Laboratory, 1663 Magazine

Los Alamos National Laboratory, 1663 Magazine, July 2016

"Quantum computers have long been on the horizon as conventional computing technologies approach their physical limits. While general-purpose quantum computers remain on the horizon for the time being, a special kind of quantum computer already exists and could be a game changer for simulation and computing tools in support of Los Alamos National Laboratory’s mission of stockpile stewardship without nuclear testing. It may also enable a slew of broader national security and computer science applications. But first, it will undoubtedly draw a vibrant community of top creative thinkers in many scientific fields to Los Alamos."

Link to PDF

Spanning Tree Calculations on D-Wave 2 Machines

M.A. Novotny, L. Hobl, J.S. Hall, and K. Michielsen (Department of Physics and Astronomy, and Center for Computational Sciences, Mississippi State University and Institute for Advanced Simulation, Ju ̈lich Supercomputing Centre)

"Calculations on D-Wave machines are presented, both for the 500-qubit and the 1000-qubit machines. Results are presented for spanning trees on the available K4,4 Chimera graphs of both machines. Comparing trees of approximately the same size, the frequency of finding the ground state for the 1000-qubit machine is significantly improved over the 500- qubit older generation machine."

(Feb 2016) Journal of Physics Conference Series http://iopscience.iop.org/article/10.1088/1742-6596/681/1/012005/meta

2015

What is the Computational Value of Finite Range Tunneling?

Vasil S. Denchev, Sergio Boixo, Sergei V. Isakov, Nan Ding, Ryan Babbush, Vadim Smelyanskiy, John Martinis, Hartmut Neven (Google scientists)

"Quantum annealing (QA) has been proposed as a quantum enhanced optimization heuristic exploiting tunneling. Here, we demonstrate how finite range tunneling can provide considerable computational advantage. For a crafted problem designed to have tall and narrow energy barriers separating local minima, the D-Wave 2X quantum annealer achieves significant runtime advantages relative to Simulated Annealing (SA). For instances with 945 variables this results in a time-to-99%-success-probability that is ∼108 times faster than SA running on a single processor core. "

(30 Dec 2015) http://arxiv.org/abs/1512.02206

Multiple Query Optimization on the D-Wave 2X Adiabatic Quantum Computer

Immanuel Trummer and Christoph Koch, (E ́cole Polytechnique Federale de Lausanne scientists) 

"In this paper, we tackle the problem of multiple query optimization (MQO)...While the problem sizes that can be treated are currently limited, we already find a class of problem instances where the quantum annealer is three orders of magnitude faster than other approaches."

(23 Oct 2015) http://arxiv.org/pdf/1510.06437v1.pdf

 

Application of quantum annealing to training of deep neural networks

Steven H. Adachi, Maxwell P. Henderson (Lockheed Martin scientists)

"We investigated an alternative approach [to Deep Learning] that estimates model expectations of Restricted Boltzmann Machines using samples from a D-Wave quantum annealing machine...In our tests we found that the quantum sampling-based training approach achieves comparable or better accuracy with significantly fewer iterations of generative training than conventional CD-based training."

(Oct 2015) Mathpubs: http://www.mathpubs.com/detail/1510.06356v1/Application-of-Quantum-Annea...

Solving the Optimal Trading Trajectory Problem Using a Quantum Annealer

Gili Rosenberg, Poya Haghnegahdar, Phil Goddard, Peter Carr, Kesheng Wu, Marcos López de Prado

“We solve a multi-period portfolio optimization problem using D-Wave Systems' quantum annealer. We derive a formulation of the problem, discuss several possible integer encoding schemes, and present numerical examples that show high success rates.”

(22 Aug 2015) http://arxiv.org/abs/1508.06182

Guest Column: Adiabatic Quantum Computing Challenges
Cristian S. Calude, Elena Calude, Michael J. Dinneen

ACM SIGACT News archive, Volume 46 Issue 1, March 2015, Pages 40-61 http://dl.acm.org/citation.cfm?id=2744459&dl=ACM&coll=DL&CFID=666501900&CFTOKEN=29005022

"The paper presents a brief introduction to quantum computing with focus on the adiabatic model which is illustrated with the commercial D-Wave computer. We also include new theory and experimental work done on the D-Wave computer. Finally we discuss a hybrid method of combining classical and quantum computing and a few open problems."

 

 

Benchmarking Adiabatic Quantum Optimization for Complex Network Analysis

Ojas Parekh, Jeremy Wendt, Luke Shulenburger, Andrew Landahl, Jonathan Moussa, John Aidun (Sandia National Laboratories scientists)

"We lay the foundation for a benchmarking methodology for assessing current and future quantum computers. We pose and begin addressing fundamental questions about how to fairly compare computational devices at vastly different stages of technological maturity. We critically evaluate and offer our own contributions to current quantum benchmarking efforts, in particular those involving adiabatic quantum computation and the Adiabatic Quantum Optimizers produced by D-Wave Systems, Inc. "

(Apr 2015) Link to PDF

Computational Role of Multiqubit Tunneling in a Quantum Annealer

Sergio Boixo, Vadim N. Smelyanskiy, Alireza Shabani, Sergei V. Isakov, Mark Dykman, Vasil S. Denchev, Mohammad Amin, Anatoly Smirnov, Masoud Mohseni, Hartmut Neven (Scientists from Google, NASA Ames, and D-Wave)

Quantum tunneling, a phenomenon in which a quantum state traverses energy barriers above the energy of the state itself, has been hypothesized as an advantageous physical resource for optimization. This paper demonstrates that multiqubit tunneling plays a computational role in the D-Wave processor.

(Feb 20 2015) Nature Communications http://www.nature.com/ncomms/2016/160107/ncomms10327/full/ncomms10327.html

2014

First application of quantum annealing to IMRT beamlet intensity optimization

Daryl P Nazareth and Jason D Spaans (Roswell Park Cancer Institute)

"Optimization methods are critical to radiation therapy. A new technology, quantum annealing (QA), employs novel hardware and software techniques to address various discrete optimization problems in many fields. We report on the first application of quantum annealing to the process of beamlet intensity optimization for IMRT...This initial experiment suggests that more research into QA-based heuristics may offer significant speedup over conventional clinical optimization methods, as quantum annealing hardware scales to larger sizes."

(1 May 2015) Institute of Physics and Engineering in Medicine http://iopscience.iop.org/article/10.1088/0031-9155/60/10/4137/pdf

Reexamining classical and quantum models for the D-Wave One processor
Tameem Albash, Troels F. Rønnow, Matthias Troyer, Daniel A. Lidar
Quantum annealing correction for random Ising problems
Kristen L. Pudenz, Tameem Albash, Daniel A. Lidar
A Quantum Annealing Approach for Fault Detection and Diagnosis of Graph-Based Systems
Alejandro Perdomo-Ortiz, Joseph Fluegemann, Sriram Narasimhan, Rupak Biswas, Vadim N. Smelyanskiy
Quantum Optimization of Fully-Connected Spin Glasses
Davide Venturelli, Salvatore Mandrà, Sergey Knysh, Bryan O'Gorman, Rupak Biswas, Vadim Smelyanskiy
Distinguishing Classical and Quantum Models for the D-Wave Device

Walter Vinci, Tameem Albash, Anurag Mishra, Paul A. Warburton, Daniel A. Lidar

(17 Mar 2014) http://arxiv.org/abs/1403.4228

Glassy Chimeras could be blind to quantum speedup: Designing better benchmarks for quantum annealing machines

Helmut G. Katzgraber, Firas Hamze, Ruben S. Andrist

(12 Jan 2014) http://arxiv.org/pdf/1401.1546.pdf
 

2013

Experimental determination of Ramsey numbers

Z. Bian et al.
Phys. Rev. Lett. vol. 111, 130505 (2013) arXiv:1201.1842

Error corrected quantum annealing with hundreds of qubits

K.L. Pudenz et al.
(31 Jul 2013) arXiv:1307.8190

Hearing the shape of Ising models: on the distinguishability power of Physics

W. Vinci et al.
(3 Jul 2013) arXiv:1307.1114

Experimental signature of programmable quantum annealing

S Boxio et al. 
Nature Communications, 2067 (28 June 2013) doi:10.1038/ncomms3067

MAX 2-SAT with up to 108 qubits

S. Santra
(12 Jul 2013) arXiv:1307.3931

Quantum annealing with more than one hundred qubits

S. Boxio et al.
(16 Apr 2013) arXiv:1304.4595

How Fast Can Quantum Annealers Count?

I. Hen
(21 Jan 2013) arXiv:1301.4956

Experimental Evaluation of an Adiabatic Quantum System for Combinatorial Optimization

C. C. McGeoch et al. 
Download PDF

2012

Construction of Energy Functions for Lattice Heteropolymer Models: A Case Study in Constraint Satisfaction Programming and Adiabatic Quantum Optimization

R. Babbush et al.
(4 Nov 2012) arXiv:1211.3422

Solving the Graph Isomorphism Problem with a Quantum Annealer

I. Hen et al.
(6 Jul 2012) arXiv:1207.1712

Robust Classification with Adiabatic Quantum Optimization

V.S. Denchev et al. 
(5 May 2012) arXiv:1205.1148

Finding low-energy conformations of lattice protein models by quantum annealing

A. Perdomo-Ortiz et al.
(24 Apr 2012) arXiv:1204.5485

A Near-Term Quantum Computing Approach for Hard Computational Problems in Space Exploration

V.N. Smelyanskiy et al.
(12 Apr 2012) arXiv:1204.2821

Quantum Speedup by Quantum Annealing

D. Nagaj et al.
Phys. Rev. Lett. 109, 050501 (2012) arXiv:1202.6257

2009

Training a Large Scale Classifier with the Quantum Adiabatic Algorithm

H. Neven, et al.
(4 Dec 2009) arXiv:0912.0779

2008

Training a Binary Classifier with the Quantum Adiabatic Algorithm

H. Neven et al.
(4 Nov 2008) arXiv:0811.0416

Image recognition with an adiabatic quantum computer I. Mapping to quadratic unconstrained binary optimization

H. Neven et al.
(28 Apr 2008) arXiv:0804.4457