Hundreds of Quantum Applications

The future is quantum – this is precisely why NEC and CSIRO provided a breakdown of the importance of quantum. In this talk, NEC reveals its plans to drive higher sustained performance using D-Wave’s quantum technologies and how CSIRO is making leaps for solving material microstructure modeling with D-Wave Leap™.

Noisy intermediate-scale quantum (NISQ) hardware is almost universally incompatible with full-scale optimization problems of practical importance which can have many variables and unwieldy objective functions. As a consequence, there is a growing body of literature that tests quantum algorithms on miniaturized versions of problems that arise in an operations research setting. Rather than taking this approach, we investigate a problem of substantial commercial value, multi-truck vehicle routing for supply chain logistics, at the scale used by a corporation in their operations. Such a problem is too complex to be fully embedded on any near-term quantum hardware or simulator; we avoid confronting this challenge by taking a hybrid workflow approach: we iteratively assign routes for trucks by generating a new binary optimization problem instance one truck at a time. Each instance has ∼2500 quadratic binary variables, putting it in a range that is feasible for NISQ quantum computing, especially quantum annealing hardware. We test our methods using simulated annealing and the D-Wave Hybrid solver as a place-holder in wait of quantum hardware developments. After feeding the vehicle routes suggested by these runs into a highly realistic classical supply chain simulation, we find excellent performance for the full supply chain. Our work gives a set of techniques that can be adopted in contexts beyond vehicle routing to apply NISQ devices in a hybrid fashion to large-scale problems of commercial interest.

Optimizing package loading within trucks for manufacturers and distributors can result in cost and time savings. In this talk, Senior Manager Nitish Umang discusses his joint work with D-Wave and show a demo for the 3D cuboid loading problem. This is a good candidate for quantum optimization due to the combinatorial nature of the problem.

Quantum annealing is a promising approach for obtaining good approximate solutions to difficult optimization problems. Folding a protein sequence into its minimum-energy structure represents such a problem. For testing new algorithms and technologies for this task, the minimal lattice-based HP model is well suited, as it represents a considerable challenge despite its simplicity. The HP model has favorable interactions between adjacent, not directly bound hydrophobic residues. Here, we develop a novel spin representation for lattice protein folding tailored for quantum annealing. With a distributed encoding onto the lattice, it differs from earlier attempts to fold lattice proteins on quantum annealers, which were based upon chain growth techniques. With our encoding, the Hamiltonian by design has the quadratic structure required for calculations on an Ising-type annealer, without having to introduce any auxiliary spin variables. This property greatly facilitates the study of long chains. The approach is robust to changes in the parameters required to constrain the spin system to chain-like configurations, and performs very well in terms of solution quality. The results are evaluated against existing exact results for HP chains with up to N=30 beads with 100% hit rate, thereby also outperforming classical simulated annealing. In addition, the method allows us to recover the lowest known energies for N=48 and N=64 HP chains, with similar hit rates. These results are obtained by the commonly used hybrid quantum-classical approach. For pure quantum annealing, our method successfully folds an N=14 HP chain. The calculations were performed on a D-Wave Advantage quantum annealer.

Computed tomography (CT) is an important imaging technique used in medical analysis of the internal structure of the human body. Previously, image segmentation methods were required after acquiring reconstructed CT images to obtain segmented CT images which made it susceptible to errors from both reconstruction and segmentation algorithms. However, this paper introduces a new approach using an advanced quantum optimization algorithm called quadratic unconstrained binary optimization (QUBO). This algorithm enables acquisition of segmented CT images from X-ray projection data with minimized discrepancies between experimentally obtained sinograms and quantized sinograms derived from quantized segmented CT images using the Radon transform. This study utilized D-Wave's hybrid solver system for verification on real-world X-ray data.

In this paper an application of D-Wave Systems quantum annealer for optimizing initial line-up of a soccer team is presented. Players and their playing position are selected to maximize the sum of players ratings for both a 4-3-3 attack formation and a 4-2-3-1 medium defensive formation. The problem is stated as a binary quadratic model (BQM) and it is solved in a D-Wave Leap Hybrid Solver. Results are presented and compared with already published results obtained with a classical solver.

Some business problems are better suited for quantum/hybrid technology than others. Watch this recording to find out how to identify and assess use cases and applications that would benefit from the use of quantum/hybrid resources.

Machine learning experts no longer need to be experts in optimization or hybrid solving in order to get the benefits of both. D-Wave's feature selection / machine learning tools enable access to the power of quantum hybrid to supercharge feature selection/ML workflows, and includes a plugin for scikit-learn.

Demand Side Response (DSR) is a strategy that enables consumers to actively participate in managing electricity demand. It aims to alleviate strain on the grid during high demand and promote a more balanced and efficient use of electricity resources. We implement DSR through discount scheduling, which involves offering discrete price incentives to consumers to adjust their electricity consumption patterns. Since we tailor the discounts to individual customers' consumption, the Discount Scheduling Problem (DSP) becomes a large combinatorial optimization task. Consequently, we adopt a hybrid quantum computing approach, using D-Wave's Leap Hybrid Cloud. We observe an indication that Leap performs better compared to Gurobi, a classical general-purpose optimizer, in our test setup. Furthermore, we propose a specialized decomposition algorithm for the DSP that significantly reduces the problem size, while maintaining an exceptional solution quality. We use a mix of synthetic data, generated based on real-world data, and real data to benchmark the performance of the different approaches.

Recently, the demand for the optimization of TV commercials (CMs) has been increasing. Specifically, it is important to determine the assignment of TVCMs so that each viewer watches the TVCMs with specific number of times. This problem is known as frequency control. By controlling the frequency of multiple TVCMs for each viewer, the message of TVCMs can be conveyed to the viewers in the intended way. In our project, we considered the problem to maximize the KPI which is the number of viewers who watch all kinds of TVCMs more than once. We solved the optimization problem and broadcasted the TVCMs based on the result of the optimization. We also compared the increase in the business KPI calculated using the actual viewing data for 3 different methods, CPLEX, D-Wave CQM, and an existing manual method. As a result, D-Wave CQM increased the KPI by 92% compared to an existing manual method, and it also outperformed CPLEX by 4%, which is a significant impact on our business. 

Annealing is a heuristic for solving optimization problems that have seen a recent surge in usage owing to the success of D-Wave Systems. This paper aims to find a good heuristic for solving the Electric Vehicle Charger Placement (EVCP) problem, a problem that stands to be very important given the costs of setting up an electric vehicle (EV) charger and the expected surge in electric vehicles across the world. The same problem statement can also be generalized to the optimal placement of any entity in a grid and can be explored for further uses. Finally, the authors introduce a novel heuristic combining Quantum Annealing and Genetic Algorithms to solve the problem. The proposed hybrid approach entails seeding the genetic algorithms with the results of quantum annealing. Experimental results show that this method decreases the minimum distance from Points of Interest (POI) by 42.89% compared to vanilla quantum annealing over the sample EVCP datasets.

Transportation has lifted to a new meaning via the sky. In this talk, Sumitomo and Seino Holdings demonstrate how they partnered with Aeronext to create SkyHub, a transportation service using quantum annealing to optimize drone and truck delivery to depopulated areas.

Towards the realization of efficient transport systems, DENSO develops a method of route optimization by using D-Wave hybrid solvers. The first system is optimizing on-demand taxies and efficiency of sharing taxi with customers will be presented. The second system is collaborated with Toyota Tsusho Corperation for delivery logistics and practical advantage compared with conventional system will be presented.

A strategy for the analysis of active debris removal missions targeting multiple objects from a set of objects in near-circular orbit with similar inclination is presented. Algebraic techniques successfully reduce the orbital mechanics regarding specific inter-debris transfer and disposal methods to simple computations, which can be used as the coefficients of a quadratic unconstrained binary optimisation (QUBO) problem formulation which minimises the total propellant used in the mission whilst allowing for servicing time and meeting the mission deadline. The QUBO is validated by solving artificial small problems (from 2 to 11 debris) using classical computational methods and the weaknesses in using these methods are examined prior to solution using quantum annealing hardware. The quantum processing unit (QPU) and quantum-classical hybrid solvers provided by D-Wave are then used to solve the same small problems, with attention paid to evident strengths and weaknesses of each approach. Hybrid solvers are found to be significantly more effective at solving larger problems. Finally, the hybrid method is used to solve a large problem using a real dataset. From a set of 79 debris objects resulting from the destruction of the Kosmos-1408 satellite, an active debris removal mission starting on 30 September 2023 targeting 5 debris objects for disposal within a year with 20 days servicing time per object is successfully planned. This plan calculates the total propellant cost of transfer and disposal to be 0.87km/s and would be complete well within the deadline at 241 days from the start date. This problem uses 6,478 binary variables in total and is solved using around 25s of QPU access time.

This paper proposes a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem. One of the well-known classical approaches for SISR relies on the well-established patch-wise sparse modeling of the problem. Yet, this field's current state of affairs is that deep neural networks (DNNs) have demonstrated far superior results than traditional approaches. Nevertheless, quantum computing is expected to become increasingly prominent for machine learning problems soon. As a result, in this work, we take the privilege to perform an early exploration of applying a quantum computing algorithm to this important image enhancement problem, i.e., SISR. Among the two paradigms of quantum computing, namely universal gate quantum computing and adiabatic quantum computing (AQC), the latter has been successfully applied to practical computer vision problems, in which quantum parallelism has been exploited to solve combinatorial optimization efficiently. This work demonstrates formulating quantum SISR as a sparse coding optimization problem, which is solved using quantum annealers accessed via the D-Wave Leap platform. The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.

The first commercial quantum applications in production are using D-Wave’s quantum technology. An “in-production” quantum application means that the business has gone through the quantum journey from use case exploration and identification, to validation, POC and pilot, all the way to deploying a real-world application that is delivering ROI-driving business outcomes. Read the case story to get a deep dive into one commercial quantum application in production using D-Wave.

Inadequately planned factory layouts can lead to higher operational costs. The planning process is time-consuming and multiple planning parameters must be considered simultaneously, leading to large solution spaces. Automated planning approaches can support the planning team in the early phases of layout planning by generating layout variants that can afterwards be further specified. Recent studies from other disciplines have shown the potential of Quantum Annealing (QA) to solve complex assignment problems within seconds. Consequently, this paper presents a first implementation of a QA-based layout planning approach and demonstrates its advantages regarding scalability, solution quality, and computing time.

With the increase of intermittent renewable generation resources feeding into the electrical grid, Distribution System Operators (DSOs) must find ways to incorporate these new actors and adapt the grid to ensure stability and enable flexibility. Dividing the grid into logical clusters entails several organization and technical benefits, helping overcome these challenges. However, finding the optimal grid partitioning remains a challenging task due to its complexity. Learn how E.ON, a leading German utility company, approached this hard problem with D-Wave technology.

With the increase of intermittent renewable generation resources feeding into the electrical grid, Distribution System Operators (DSOs) must find ways to incorporate these new actors and adapt the grid to ensure stability and enable flexibility. Dividing the grid into logical clusters entails several organization and technical benefits, helping overcome these challenges.However, finding the optimal grid partitioning remains a challenging task due to its complexity. At the same time, a new technology has gained traction in the last decades for its promising speed-up potential in solving non-trivial combinatorial optimization problems: quantum computing. This work explores its application in Graph Partitioning using electrical modularity. We benchmarked several quantum annealing and hybrid methods on IEEE well-known test cases. The results obtained for the IEEE 14-bus test case show that quantum annealing DWaveSampler brings equal solutions or, for the optimal number partitions, a 1% improvement. For the more significant test cases, hybrid quantum annealing shows a relative error of less than 0.02% compared to the classical benchmark and for IEEE 118-bus test case shows time performance speed-up. The increment in performance would enable real time planning and operations of electrical grids in real time. This work intends to be the first step to showcase the potentials of quantum computing towards the modernization and adaption of electrical grids to the decentralized future of energy systems.

In this paper we propose a hybrid quantum-classical algorithm for dynamic portfolio optimization with minimal holding period. Our algorithm is based on sampling the near-optimal portfolios at each trading step using a quantum processor, and efficiently post-selecting to meet the minimal holding constraint. We found the optimal investment trajectory in a dataset of 50 assets spanning a one year trading period using the D-Wave 2000Q processor. Our method is remarkably efficient, and produces results much closer to the efficient frontier than typical portfolios. Moreover, we also show how our approach can easily produce trajectories adapted to different risk profiles, as typically offered in financial products. Our results are a clear example of how the combination of quantum and classical techniques can offer novel valuable tools to deal with real-life problems, beyond simple toy models, in current NISQ quantum processors.

Urban Air Mobility (UAM) has been envisioned as a technology that utilizes automated air transportation services to carry people or cargo at lower altitudes in and around urban areas. In this talk, experts from Sumitomo, OneSky Systems, and Tohoku University formulate the problem of discovering the safe and short routes for multiple aircraft to fly as quadratic forms and present a dynamic route planning algorithm for time-dependent simultaneous flight requests.

Residual Statics Estimation (RSE) is a computationally challenging example of a geophysical optimization problem. In this work, researchers from researchers from TNO, Aramco, and TU Delft show how to map the RSE problem to a quadratic unconstrained binary optimization (QUBO) formulation, how to implement it on the quantum annealer, and present their early computational results.

In this work, Durham University seeks efficient optimal or near-optimal solutions for energy efficient routing of wireless sensor network. Their preliminary results show that this approach using quantum computing has great promise and may open the door for other significant improvements in the efficacy of network algorithms.

Groovenauts has successfully released the world’s first business service powered with quantum annealing technology in their service, MAGELLAN BLOCKS. Since then, they have been advancing the application of quantum annealing to companies in various industries, such as manufacturing, logistics, transportation, and service. As a pioneer of solving the actual corporate issues, they will share how they handle projects and what kind of achievement we make.

In this talk, Steven Abel, Professor at Durham University, discusses recent developments in the implementation of complex physical systems on quantum annealers. These include systems that allow quantum tunnelling and barrier penetration across specified potentials. The results we find are in accord with those predicted theoretically showing that a quantum annealer is a genuine quantum system that can be used as a quantum laboratory. More generally he shows how his techniques can generally be used to optimise arbitrary potentials in several dimensions. He compares classical with quantum optimisation methods and finds a qualitative improvement over classical techniques.

Quantum Annealing is a heuristic for solving optimization problems that have seen a recent surge in usage owing to the success of D-Wave Systems. This paper aims to find a good heuristic for solving the Electric Vehicle Charger Placement (EVCP) problem, a problem that stands to be very important given the costs of setting up an electric vehicle (EV) charger and the expected surge in electric vehicles across the world. The same problem statement can also be generalised to the optimal placement of any entity in a grid and can be explored for further uses. Finally, the authors introduce a novel heuristic combining Quantum Annealing and Genetic Algorithms to solve the problem. The proposed hybrid approach entails seeding the genetic algorithm with the results of a quantum annealer. Our experiments show this method decreases the minimum distance from POIs by 42.89% compared to vanilla quantum annealing over our sample EVCP datasets.

We developed and compared Constraint Programming (CP) and Quantum Annealing (QA) approaches for rolling stock optimisation considering necessary maintenance tasks. To deal with such problems in CP we investigated specialised pruning rules and implemented them in a global constraint. For the QA approach, we developed quadratic unconstrained binary optimisation (QUBO) models. For testing, we use data sets based on real data from Deutsche Bahn and run the QA approach on real quantum computers from D-Wave.

In this paper, the authors show how to implement in a simple way some complex real-life constraints on the portfolio optimization problem, so that it becomes amenable to quantum optimization algorithms. Specifically, first we explain how to obtain the best investment portfolio with a given target risk. This is important in order to produce portfolios with different risk profiles, as typically offered by financial institutions. Second, we show how to implement individual investment bands, i.e., minimum and maximum possible investments for each asset. This is also important in order to impose diversification and avoid corner solutions. Quite remarkably, we show how to build the constrained cost function as a quadratic binary optimization (QUBO) problem, this being the natural input of quantum annealers. The validity of our implementation is proven by finding the optimal portfolios, using D-Wave Hybrid and its Advantage quantum processor, on portfolios built with all the assets from S&P100 and S&P500.

Traffic jams create significant negative effects on daily life in terms of time, money, and the environment. DENSO and Toyota set out to change that by optimizing multimodal transportation systems, coordinating passenger and traffic flow between rideshare services and public buses.

In this work the researchers use quantum and classical annealing 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.The results show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics.

This problem is an example of an optimization problem known as the maximum independent set problem. The objective is to maximize the number of nodes in a set, with the constraint that no edges be contained in the set. There are a wide variety of applications for this problem, such as scheduling and error correcting codes.

This demo group satellites into constellations such that their average observation coverage is maximized. It is an example of a constraint satisfaction project.

In the spring of 2019, Groovenauts announced the beta launch of the world’s first business optimization system using D-Wave’s 2000Q quantum platform. By combining the power of quantum computing with Magellan Blocks, Groovenaut’s web-based suite of machine learning services and tools, the new system will help customers optimize shipping routes and staff schedules more quickly and accurately than ever before.

Financial stress testing is becoming an increasingly important tool in the arsenal built by regulators to preserve economic stability and prevent a domino effect when financial institutions experience periods of distress. Assad Bouayou applied the power of quantum computing to an XVA model to build a more qualitative, systematic approach to stress testing aimed at preventing future banking crises.

Jij Inc. is a venture company developing software for annealing-based optimization. They have developed an open-source project named “OpenJij” to provide an environment for developing annealing applications, evaluating performance, and benchmarking.

This is a demo on how to solve a Sudoku puzzle with D-Wave Ocean. It describes the Sudoku puzzle as a constraint satisfaction problem. By laying down these constraints, we can get the solver to optimize over them with the goal of returning a solution that satisfies all the constraints.

Fault diagnosis is the combinational problem of quickly localizing failures as soon as they are detected in systems. Circuit fault diagnosis is the problem of identifying a minimum-sized set of components that, if faulty, explains an observation of incorrect outputs given a set of inputs.

The demo illustrates the MIQUBO method by finding an optimal feature set for predicting survival of Titanic passengers. Its output is a ranking of subsets of features that have high MI with the variable of interest (survival) and low redundancy.

The solution technique is to construct a set of constraints that enforces the rules of moving through a maze. These constraints are then converted by Ocean software tools to a binary quadratic model (BQM) that can then be solved with a D-Wave quantum computer. The solution that gets returned by the quantum computer is the path needed to get through the maze.

This code demonstrates the use of the D-Wave system to solve a factoring problem. This is done by turning the problem into a three-bit multiplier circuit.

Social networks map relationships between people or organizations onto graphs, with the people/organizations as nodes and relationships as edges. Signed social networks map both friendly and hostile relationships by setting the values of the edges between nodes with either plus ("+") or minus ("-") signs. This demo calculates and shows structural imbalance for social networks of militant organization based on data from the Stanford Militants Mapping Project.

In the applied physics lab at Johns Hopkins, researchers are leveraging quantum annealing for pattern recognition in high energy physics particle detection. Quantum annealing enables more accurate pattern matching and access to a family of low-energy solutions that improve track reconstruction.

A graph partitioning problem is an interesting problem from graph theory with a wide variety of applications. In this demo our goal is to split a group of people into two equal-sized teams with a constraint of having as few friends on opposite teams as possible.

The US has more than 100,000 contaminated groundwater sites and the total cost to remediate them will exceed $100,000,000,000. In order to design cost-effective remedial measures, the properties of the subsurface must be understood and hydrologic inverse analysis helps us understand the properties of the subsurface. While there is a way to go before practical applications to hydrology can be made, this project demonstrated that the D-Wave system can be used solve hydrologic inverse problems.

Pipeline networks can be viewed as a graph built up of edges (pipeline segments) and nodes (junctions of pipeline segments). This demo is designed to determine a set of junctions within the pipeline network from which every pipeline segment can be monitored. This problem can be modeled as a vertex cover problem in which the goal is to find a subset of nodes in a graph such that every edge has at least one end point in the subset.

A very important problem in combinatorial optimization is partitioning a network into communities of densely connected nodes; where the connectivity between nodes inside a particular community is large compared to the connectivity between nodes belonging to different ones. This problem is known as community detection, and has become very important in various fields of science including chemistry, biology and social sciences...This paper addresses a systematic study of detecting two or more communities in a network using a quantum annealer.

A quantum annealer exploits quantum effects to solve a particular type of optimization problem. The advantage of this specialized hardware is that it effectively considers all possible solutions in parallel, thereby potentially outperforming classical computing systems. In this article, we show how to compile a subset of Prolog enhanced with support for constraint logic programming into a two-local Ising-model Hamiltonian suitable for execution on a quantum annealer.

Topological Data Analysis is an exciting new field of mathematics, in which data scientists look for patterns in data using mathematical concepts of shape. The authors explored using the D-Wave quantum computer on a small problem called Wasserstein distance.

A demo on using the D-Wave Ocean SDK to solve the map coloring problem. Namely, given a map, color the regions of the map such that no two regions sharing a border would share the same color.

Global mobile data traffic is increasing expotentially every year, and with it, user demand for fast data rates and increased wireless capacity. Researchers at Princeton University wanted to discover if quantum computing could reduce the amount of time it takes to process data in real time and cut through the noise created by countless interfering data streams.

Given a set of jobs and a finite number of machines, this demo show how to schedule jobs on those machines such that all the jobs are completed at the earliest possible time.

Tohoku University isn’t just blazing their own path in the field of quantum computing, they’re recruiting others to work with them. As part of a public-private consortium across Japan and parts of western Europe, Tohoku University Quantum Annealing Research & Development (T-QARD) is tackling research in diverse areas such as stock market forecasting, public transit scheduling, black-box optimization, and calibrating transmission systems in cars.

Machine learning--giving computers the ability to learn new things without being pre-programmed with specific knowledge--plays an important role in a growing number of applications, including spam detection, natural language processing, and recommendation systems. USC and their research partners used the D-Wave 2000Q to apply quantum annealing to machine learning approaches to several problems in the life and physical sciences, including TF-DNA binding, cancer classification, and solving a Higgs boson optimization problem.

On the Volkswagen factory floor, the production process creates a series of constantly changing conditions. Delays, shifting wait times, and irregular requests all impact business objectives and outcomes. In order to find optimization solutions, Volkswagen is developing a hybrid algorithm that relies on both classical and quantum approaches to problem solving.

Optimization methods are critical to delivering enough radiation to kill cancerous cells while avoiding non-cancerous nearby cells. Reseachers at Roswell Park used a D-Wave system for the first application of quantum annealing for beamlet intensity optimization for IMRT

The spread of intelligent transportation systems in urban cities has caused heavy computational loads, requiring a novel architecture for managing large-scale traffic..the solutions to the global control method obtained with the quantum annealing machine are better than those obtained with conventional simulated annealing.

Data Reply studied the ability of the D-Wave system to solve the number partitioning problem, a well-known NP-hard optimization model that poses some challenges typical of those encountered in real-world applications. The results showed remarkable results in most instances of the model; for the most complex ones, they investigated further the D-Wave Hybrid suite.

We present a comparison study of state-of-the-art classical optimisation methods to a D-Wave 2000Q quantum annealer for the planning of Earth observation missions. The problem is to acquire high value images while obeying the attitude manoeuvring constraint of the satellite.

Using optimal phasor measurement unit placement as a prototypical problem, we assess the computational viability of the current generation D-Wave Systems 2000Q quantum annealer for power systems design problems.

This paper describes a quantum isomer search procedure for determining all the structural isomers of alkanes.

The use of quantum computing in graph community detection and regularity checking related to Szemeredi’s Regularity Lemma (SRL) are demonstrated with D-Wave Systems’ quantum annealer and simulations. We demonstrate the capability of quantum computing in solving hard problems relevant to big data

Here we studied the ability of a quantum machine learning approach to classify and rank binding affinities. Using simplified data sets of a small number of DNA sequences derived from actual binding affinity experiments, we trained a commercially available quantum annealer to classify and rank transcription factor binding.

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 DW2000Q quantum annealing machine.

Here, we study the ability of the quantum hardware to solve the number partitioning problem, a well-known NP-hard optimization model that poses some challenges typical of those encountered in real-world applications. 

A novel quantum-classical hybrid scheme is proposed to efficiently solve largescale combinatorial optimization problems. The key concept is to introduce a Hamiltonian dynamics of the classical flux variables associated with the quantum spins of the transverse-field Ising model. Molecular dynamics of the classical fluxes can be used as a powerful preconditioner to sort out the frozen and ambivalent spins for quantum annealers

This paper describes the logic and creativity needed in order to have high probability of solving discrete optimization problems on a quantum annealing computer

This article assesses the performance of the D-Wave 2X quantum annealer on two NP hard graph problems, in particular clique finding and graph partitioning.

Mathematical optimization problems with real business data raise various implementation issues that are not described in a paper, such as communication delay. In the case of slow communication over the network, it is often the case that delays accumulate due to iterations of Request and Response, which prevent smooth computation. In this presentation, we introduce a technical trick for communication delay reduction that can be used in combination with various algorithms. These activities are just one of the examples of D-Wave/mathematical optimization at NTT DATA.

With quantum computers still under heavy development, already numerous quantum machine learning algorithms have been proposed for both gate-based quantum computers and quantum annealers. Recently, a quantum annealing version of a reinforcement learning algorithm for grid-traversal using one agent was published. We extend this work based on quantum Boltzmann machines, by allowing for any number of agents. We show that the use of quantum annealing can improve the learning compared to classical methods. We do this both by means of actual quantum hardware and by simulated quantum annealing.

We analyze 3,171 US common stocks to create an efficient portfolio based on the Chicago Quantum Net Score (CQNS) and portfolio optimization. We begin with classical solvers and incorporate quantum annealing. We add a simulated bifurcator as a new classical solver and the new D-Wave Advantage(TM) quantum annealing computer as our new quantum solver.

Groovenauts' MAGELLAN BLOCKS is the world's first service to market that combines optimizations run on a quantum computer with AI-based predictions. We began by successfully applying our service to various corporate issues like logistics and delivery optimization, as well as factory optimization and construction site optimization. We then expanded this practice of corporate optimization at scale to transform entire municipal service chains. Join us to learn more about our "City as a Service" solution through city-wide use cases that implement big data, AI, and quantum computing to optimize city activities.

We report on a method for globally controlling traffic signals arranged on a square lattice by means of the D-Wave quantum annealer. We consider a situation in which many cars moving on a lattice network are controlled via traffic signals installed at each intersection. This study makes three contributions to signal optimization.

Quantum computers present a unique opportunity to simulate quantum phenomena efficiently and its implied that quantum computers will be able to simulate quantum chemistry problems more efficiently than classical computers. This global search is very difficult, but by using Ising machines (quantum annealers) the ground state energy can be reliably found. We show that a quantum annealer accelerates the calculation by reducing the number of optimization steps and thus enabling a faster convergence to the ground state. This has important implications for finding the true optimized molecular states of excited states or complex ground states.

We study the interplay between quantum annealing parameters in embedded problems, providing both deeper insights into the physics of these devices and pragmatic recommendations to improve performance on optimization problems. We choose as our test case the class of degree-bounded minimum spanning tree problems

We will share our preliminary results of the D-Wave Advantage beta testing on the Hamiltonian path problem for genome variant analysis and the time-scheduled vehicle routing problem with multiple time-window. Besides, our hybrid quantum annealing (HQA) solver is presented. This talk includes the results of the D-Wave Advantage Beta Test.

D-Wave quantum computer is used to optimize the lockdown schedule as disease evolution is modeled using a SEIRD model. The already known QUBO formulation for the knapsack problem is used along with a transfer function to link the evolving diseases betweeen cities. An alternating schedule of disease evolution along with city lockdown optimization is then compared between classical probabilistic and quantum annealing methods. Results show that the knapsack formulation is able to substantially reduce morbidity while reducing the economic disruption

In this talk, I would like to talk about the recent advance of quantum hybrid systems, especially the scaling and performance of quadratic unconstrained binary optimization (QUBO) formulation of Time-scheduled Vehicle Routing Problem with multiple time-window. This talk includes the results of D-Wave Advantage Beta Test.

In this talk, we present 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.

We format three problems essential to mitigating the effects of the COVID-19 pandemic to fit the quantum annealing regime, namely, the Nurse Scheduling Problem (NSP), the Physician Scheduling Problem (PSP), and the Nurse-Physician Scheduling Problem (NPSP).

We present results of solving various types of optimization problems on the Advantage QPU and on the HSS version 2. The first set of optimization problems consists of garden optimization problems, sizeable optimization problems with constraints. The task is to fill and place pots taking into account the characteristics of neighboring plants. The number of variables is given by the number of pots multiplied by the number of plant species and the number plants is equal to the number of pots (one plant per pot). The second set of optimization problems comprises exact cover problems and the third set 2-SAT problems.

In this talk, we will introduce new use cases of Advantage in real problems from global brand clients. See how the use of D-wave quantum computing in our product, MAGELLAN BLOCKS, solves combinational optimization problems to radically improve business outcomes for our clients.

Molecular Docking is an important step of the drug discovery process which aims at calculating the preferred position and shape of one molecule to a second when they are bound to each other. It is possible to identify three phases of Molecular Docking: 1) Ligand Expansion, i.e. unfolding of the ligand molecule; 2) Initial Placement, i.e. placement of the ligand into the protein pocket with rigid roto-translations; 3) Shape Refinement, i.e. rotation of the ligand bonds in order to match the protein pocket. In. our work, we focused on the Ligand Expansion phase and considered a quantum annealing approach to the Molecular Unfolding (MU) problem.

Different quantum algorithms have been built to find currency arbitrage opportunities, perform feature selection in credit scoring, and optimize trading trajectories. Hybrid techniques have been explored in order to overcome the limitations in the size of the problem, taking into consideration the number of qubits and connectivity in the Chimera architecture in D-Wave processors.

Many problems across several disciplines, ranging from production planning over DNA sequencing, can be represented as constrained integer optimization problems. Utilizing classical algorithms, generally exact solutions to integer linear programming (ILP) problems are exponentially hard to obtain, and heuristic algorithms are employed to find approximate solutions. This talk demonstrates a new algorithm that solves such constrained ILP problems on a quantum annealer.

In this talk, I will present 4 new hybrid quantum annealing algorithms for solving the Vehicle Routing Problem and its variant: the Capacitated Vehicle Routing Problem. Both are NP-hard combinatorial optimization problems with important applications in logistics.

We show our collaborations with various companies in T-QARD research network. We generate several seeds of the application of quantum annealing in the industry. In addition, we demonstrate several techniques for solving wider optimization problems by using quantum annealer.

In this work we propose a novel Ising model for benchmarking optimization algorithms called the Corrupted Biased Ferromagnets (CBFM). A detailed evaluation of the CBFM instance class across six classical algorithms suggest that it is a challenging problem for both heuristics and global optimization algorithms.

Unfolding is the procedure of correcting for distortions due to limited resolution of a measuring device or transmission medium. High-energy physics is replete with hard computational problems and unfolding is one of the areas where quantum computing could be used to speed up calculations. In this talk, we present an implementation of likelihood-based unfolding on a DWave quantum computer.

Here we use portfolio optimization as a case study by which to benchmark quantum annealing controls and their relative effects on computational accuracy. We compare empirical results from the D-Wave 2000Q quantum annealer to the computational ground truth for a variety of portfolio optimization instances.

A key problem in financial mathematics is the forecasting of financial crashes: If we perturb asset prices, will financial institutions fail on a massive scale? This was recently shown to be a computationally intractable (NP-hard) problem...In this Rapid Communication, we show how this problem can be handled by quantum annealers.

In this work, we consider a practical railway dispatching problem: delay and conflict management on a single- track railway line. We examine the issue of train dispatching consequences caused by the arrival of an already delayed train to the segment being considered. This problem is computationally hard and often needs to be solved timely. Here, we introduce a quadratic unconstrained binary optimization (QUBO) model of the problem in question, compatible with the emerging quantum annealing technology. The model’s instances can be executed on present-day quantum annealers. As a proof-of-concept, we solve selected real-life problems from the Polish railway network using D-Wave quantum annealers.

Quantum annealing has emerged in the last few years as a promising quantum computing approach to solving large-scale combinatorial optimization problems. In this paper, we formulate the problem of correctly placing pressure sensors on a Water Distribution Network (WDN) as a combinatorial optimization problem in the form of a Quadratic Unconstrained Binary Optimization (QUBO) or Ising model. Optimal sensor placement is indeed key to detect and isolate fault events. We outline the QUBO and Ising formulations for the sensor placement problem starting from the network topology and few other features. We present a detailed procedure to solve the problem by minimizing its Hamiltonian using PyQUBO, an open-source Python Library. We then apply our methods to the case of a real Water Distribution Network. Both simulated annealing and a hybrid quantum-classical approach on a D-Wave machine are employed.

As consequences of disruptions in railway traffic affect passenger experience/satisfaction, appropriate dispatching decisions are necessary. The problem of optimal scheduling the order of trains is known to be NP-hard, given the numerous restrictions of traffic nature. With the recent advances in quantum technologies, quantum annealing has become an alternative method to solve optimization problems...We demonstrate the implementation on the D-Wave Quantum Processing Unit and its hybrid solver.

Spontaneous structural rearrangements play a central role in the organization and function of complex biomolecular systems. In principle, physics-based computer simulations like Molecular Dynamics (MD) enable us to investigate these thermally activated processes with an atomic level of resolution. However, rare conformational transitions are intrinsically hard to investigate with MD, because an exponentially large fraction of computational resources must be invested to simulate thermal fluctuations in metastable states. Path sampling methods like Transition Path Sampling hold the great promise of focusing the available computational power on sampling the rare stochastic transition between metastable states. In these approaches, one of the outstanding limitations is to generate paths that visit significantly different regions of the conformational space at a low computational cost. To overcome these problems we introduce a rigorous approach that integrates a machine learning algorithm and MD simulations implemented on a classical computer with adiabatic quantum computing. First, using functional integral methods, we derive a rigorous low-resolution representation of the system's dynamics, based on a small set of molecular configurations generated with machine learning. Then, a quantum annealing machine is employed to explore the transition path ensemble of this low-resolution theory, without introducing un-physical biasing forces to steer the system's dynamics. Using the D-Wave quantum computer, we validate our scheme by simulating a benchmark conformational transition in a state-of-the-art atomistic description. We show that the quantum computing step generates uncorrelated trajectories, thus facilitating the sampling of the transition region in configuration space. Our results provide a new paradigm for MD simulations to integrate machine learning and quantum computing.