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.

Drug discovery is a high-risk, high-reward field, where a blockbuster therapy can deliver life-changing clinical benefits to millions of patients and generate billions of dollars in profit—but more than 90% of new drug development efforts ultimately prove fruitless. PolarisQB is using quantum computing with AI and precision medicine to search chemical space and create novel molecular drugs for specific proteins and diseases much more quickly than using traditional methods. In order to find a new preclinical lead candidate, the first step is to build a library of small molecules that may interact with the target protein binding pocket. Traditionally, library design involves wet lab testing that takes years, costs millions, and is limited to a narrow chemical space. With the power of D-Wave’s quantum technology, PolarisQB finds optimized molecules in days from a highly diverse library of 10^30 possibilities. This size of the library is 20 orders of magnitude bigger than the current industry standard.

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.

Digital transformation is altering our lives in many positive ways. This acceleration of digital transformation, however, has also led to an increased potential risk in identity fraud, as well as social engineering and data theft to businesses, consumers and the broader ecosystem. In this talk, learn how PayPal is tapping advances in quantum computing to innovate on feature selection, a critical step in building powerful models to fight sophisticated fraud.

In this session, the an Mphasis expert will discuss how quantum computing can reduce the execution time for algorithms, achieve better accuracy, identify complex patterns in data to improve accuracy, and help solve complex problems. It will also showcase live quantum computing demos running on quantum hardware to solve targeted industry challenges.

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.

In this talk, hear a summary of current practices in asset liability modelling (ALM) within the insurance sector and an insight into what ALM may be like in the immediate future by demonstration of how quantum annealing can be used for ALM.

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. Using D-Wave's quantum system, results and performances are compared with state of art classical solvers.

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.

An artificial spin ice is a manufactured physical system defined by interacting binary variables. Interesting physical properties arise when all constraints defining the interactions cannot be satisfied, namely the system is frustrated. Researchers from Los Alamos National Laboratory (LANL) presents the first realization of artificial spin ice in a lattice of coupled qubits to recreate physical phenomena found in actual magnetic materials.

Lattice gauge theory is an essential tool for strongly interacting non-Abelian elds, such as those in quantum chromodynamics where lattice results have been of central importance for several decades. In this work, researchers from York University implement SU(2) pure gauge theory on a quantum annealer for lattices comprising a few plaquettes in a row with a periodic boundary condition.

Researchers from University of California, Berkeley, present a new lossy compression algorithm for statistical floating-point data through a representation learning with binary variable. Calculations from quantum annealing show promising results and hardware comparison is further studied between the previous generation D-Wave 2000Q and the current D-Wave Advantage system.

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.

Understanding gene regulatory pathways is critical in forming concepts of how organisms grow and develop. The authors sought to use Bayesian network machine learning in tandem with the D-Wave quantum computer on a particular genetic problem.

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.

The authors develop a method to optimize control schedules for general adiabatic quantum computation (AQC) algorithms that is scalable/efficient, practical, and robust.

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.

This work describes quantum programming, compilation, and execution with XACC, a compiler framework and software tools developed at ORNL. XACC is available as an open source project on Github.

The author present a compilation framework called “C to D-Wave” that enables programmers to write programs in a stylized subset of the C programming language and compile these to a minimization problem in the form of a D-Wave quantum machine instruction. The solution to the minimization problem corresponds to the output of the C program.

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.

The Ising Hamiltonian offers a versatile model for studying the microscopic behavior of several material systems. Here we study an Ising Hamiltonian on a geometrically-frustrated Shastry-Sutherland lattice which has been used to explain the magnetic properties in the rare-earth tetraborides. Using quantum Monte-Carlo calculations to validate our results, this work indicates that quantum annealing provides a versatile method of material simulation that can accelerate scientific discovery.

OTI specializes in developing advanced materials to solve large s cale industrial problems for displays and lighting in smart devices, televisions, VR headsets, and more. By using D-Wave's quantum platform to better understand and predict the structure-property relationship of new materials, OTI is breaking new ground in the world of quantum chemistry and materials development.

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.

Identifying new methods for integer factorization plays an important role in modern information security. Shor’s algorithm is perhaps the most well-known method for integer factorization. An equally powerful model of quantum computing is the adiabatic quantum computing (AQC) model, which can also solve the integer factorization problem. In this work, the authors developed a framework to convert an arbitrary integer factorization problem to an executable Ising model and tested it on the D-Wave 2000Q.

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.

In 2018, D-Wave scientists announced the first large-scale quantum simulation of topological state of matter, simulating the phenomenon behind the 2016 Nobel Prize

Mathematics play a key role in how data is expressed and analyzed in the sciences and engineering--for example, in describing the relationships between subatomic particles in nuclear physics. In the case of high-dimensional functional spaces (a way of modelling datasets with staggeringly high numbers of attributes), the calculations required to solve systems of equations are extremely complex, and are often beyond the capabilities of classical algorithms to solve. Riken turned to quantum computing for new ways to process these problems.

The mathematical procedure of minor embedding is used to "reshape" a user's QUBO or Ising problem onto the Chimera graph of the D-Wave QPU. D-Wave's embedding algorithm, published in 2013 by Cai & Macready & Roy, is effective, but reseachers have questioned its efficiency. Prasanna Date, of RPI, developed an embedding algorithm which ran faster, and also used fewer qubits.

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.

New users of the D-Wave QPU are often surprised to learn that the QPU doesn't find the global minimum in all of its returned samples. The QPU is a probabilistic device; and there are many algorithmic and numeric approaches to increasing the number of "correct" solutions. John Dorband of UMBC has been studying the D-Wave QPU for a few years, and has presented a numeric algorithm for optimizing the returned solutions.

What if computers could do more for materials science research than just run and evaluate pre-programmed simulations? The University of Tokyo set out to answer that question with the goal of accelerating and eventually automating the design of metamaterials. These artificial substances have properties that do not exist in nature, which are derived from the structure of the material rather than its composition. Using D-Wave's 2000Q quantum annealer, researchers sought optimal solutions for designing a thermal radiator.

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.

Shopping for the right car can be a complicated process, but software engineers at Volkswagen are hoping to make it just a little bit easier. Using a hybrid classical-quantum approach, they’ve built a recommendation system that understands customer biases, preferences, and behaviors, then helps them select and customize their ideal vehicle. The initial results show rapid convergence and look very promising.

By combining multiple A.I. strategies with the power of quantum computing, WuXi NextCODE Genomics can identify previously unseen patterns and causal dependencies in biological data that allow for faster, more cost-effective, and more efficient drug development. These medications have the potential to make a profound impact on the treatment of cancer, heart disease, Alzheimer’s, and liver disease.

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

QMASM fills a gap in the software ecosystem for D-Wave's adiabatic quantum computers by shielding the programmer from having to know system-specific hardware details while still enabling programs to be expressed at a fairly low level of abstraction. It is therefore analogous to a conventional macro assembler and can be used in much the same way: as a target either for programmers who want a great deal of control over the hardware or for compilers that implement higher-level languages.

Motivated by graph-based methods for quantum molecular dynamics (QMD) simulations, researchers explored graph partitioning/clustering methods and implementations that run on the D-Wave. The demonstrated “proof of principle” results on benchmark graphs, example graphs and electronic structure graphs and showed results that equal or out-perform current “state of the art” methods.

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.

XACC is an extensible compilation framework for hybrid quantum-classical computing architectures. It provides extensible language frontend and hardware backend compilation components glued together via a novel quantum intermediate representation.

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. 

The authors investigate whether quantum annealers with select chip layouts can outperform classical computers in reinforcement learning tasks.

As the results of the 2016 US Presidential Election were finalized, it was clear that the majority of professional polling groups had significantly overestimated their predictions. This work investigated the use case of quantum computation to train Boltzmann machines for predicting the 2016 Presidential election.

In 2018, D-Wave scientists announced a breakthrough in materials simulation, demonstrating a large-scale programmable quantum simulation on the D-Wave system. 

Researchers from 1QBit developed a method to perform reinforcement learning using the D-Wave 2000Q system. The experimental results showed that their technique is a promising method for harnessing the power of quantum sampling in reinforcement learning tasks.

Machine learning relies heavily on sampling from complex probability distributions. NASA scientists successfully trained the D-Wave 2X system on image data sets in a generative unsupervised learning setting, one of the most difficult paradigms in machine learning.

Simulating electronic structure properties is important for different areas of research and industry but numerical approximations rapidly become unfeasible on classical systems. In this project, VW investigated the use of a D-Wave system to find advanced materials. 

Since the difficulty of automated materials design grows exponentially in the number of variables, designing complex materials is often beyond the ability of classical algorithms. We show how quantum annealing can be incorporated into automated materials discovery and conduct a proof-of-principle study on designing complex thermofunctional metamaterials.

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.

Recent advances in high-throughput genomic technologies coupled with exponential increases in computer processing and memory have allowed us to interrogate the complex aberrant molecular underpinnings of human disease from a genome-wide perspective. Inspired in part by recent advances in physical quantum processors, we evaluated several unconventional machine learning (ML) strategies on actual human tumor data. Here we show for the first time the efficacy of multiple annealing-based ML algorithms for classification of high-dimensional, multi-omics human cancer data from the Cancer Genome Atlas.

The Griffiths-McCoy singularity is a phenomenon characteristic of low-dimensional disordered quantum spin systems, in which the magnetic susceptibility shows singular behavior as a function of the external field even within the paramagnetic phase. We study whether this phenomenon is observed in the transverse-field Ising model with disordered ferromagnetic interactions on the quasi-two-dimensional diluted Chimera graph both by quantum Monte Carlo simulations and by extensive experiments on the D-Wave quantum annealer used as a quantum simulator.

In this paper, we apply a recursive quantum algorithm to a D-Wave quantum annealer housed at the University of Southern California to recover a sound wave speed profile, typical in seismic inversion experiments.

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.

Understanding how the D-Wave quantum computer could be used for machine learning problems is of growing interest. Our work, in particular, evaluates the feasibility of using the D-Wave as a sampler for machine learning. We describe a hybrid system that combines a classical deep neural network autoencoder with a quantum annealing Restricted Boltzmann Machine (RBM) using the D-Wave.

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.

We show how to leverage quantum annealers to better select candidates in greedy algorithms. Unlike conventional greedy algorithms that employ problem-specific heuristics for making locally optimal choices at each stage, we use quantum annealers that sample from the ground state(s) of a problem-dependent Ising Hamiltonians at cryogenic temperatures and use retrieved samples to estimate the probability distribution of problem variables.

We evaluated these implementations of ML decoding for lowdensity parity-check (LDPC) codes, analyzing the number of spins and connections and comparing the decoding performance with belief propagation (BP) decoding and brute-force ML decoding with classical computers.

We demonstrate experimentally the ability of a quantum annealer to distinguish between sets of non-isomorphic graphs that share the same classical Ising spectrum.

We show that the symmetry-breaking gap of the quantum Ising model in the transverse field can be extracted from free evolution of the longitudinal magnetization taking place after a gradual quench of the magnetic field. Our results should be relevant for ongoing cold atom and ion experiments targeting either equilibrium or dynamical aspects of quantum phase transitions.

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.

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.

New cryptosystems are being designed and standardised for the post-quantum era, and a significant proportion of these rely on the hardness of problems like the Shortest Vector Problem to a quantum adversary. In this paper we describe two variants of a quantum Ising algorithm to solve this problem.

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. 

In this manuscript we explore an experimentally observed statistical phenomenon by which domain walls on an Ising chain programmed onto a flux qubit quantum annealer tend toward a non-uniform distribution.

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

We introduce the reinforcement quantum annealing (RQA) scheme in which an intelligent agent interacts with a quantum annealer that plays the stochastic environment role of learning automata and tries to iteratively find better Ising Hamiltonians for the given problem of interest

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

We present an algorithm for quantum-assisted cluster analysis that makes use of the topological properties of a D-Wave 2000Q quantum processing unit. 

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.

Kernel-based support vector machines (SVMs) are supervised machine learning algorithms for classification and regression problems. We introduce a method to train SVMs on a D-Wave 2000Q quantum annealer and study its performance in comparison to SVMs trained on conventional computers.

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 will present several case studies of QC Ware professional services helping out large enterprise customers with their quantum computing initiatives. Examples include Airbus in aerospace,  Equinor in Oil and Gas,  and BMW and Aisin in automotive. We will also demonstrate our QC Ware Forge platform, a cloud service, that bundles performance boosting algorithms with hardware access. 

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 developed the feature selection module for the internal automated machine learning (AutoML) system by using D-Wave Advantage. The internal AutoML system, called Crafto, allows non-expert users to build a ML pipeline from the data on various environments to the ready-to-use ML models. With the help of more available qubits of D-Wave Advantage, raw data with more features can be handled by QAFS. This is an important property in the view of being used in the production system.

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.

The fundamental goal of homomorphic encryption is to enable (untrusted) Oscar to perform a computation for Alice without Oscar knowing the input to the computation or the output from the computation. We describe an approach to homomorphic encryption for quantum annealing based on spin reversal transformations and show that it comes with little or no performance penalty. This is in contrast to approaches to homomorphic encryption for classical computing, which incur a significant additional computational cost. If sensitive information, such as health-related data subject to the Health Insurance Portability and Accountability Act, is to be processed with annealers, such a technique could be useful.

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 presentation, we present an application on multimodal container planning. We show how to map this problem to a QUBO problem formulation and how the practical implementation can be done on the quantum annealer produced by D-Wave Systems.

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 discuss here three use studies of domain wall encoding. One is the flight gate scheduling problem, another is the network routing problem and the third is the 3-color problem. The data for the flight gate problem is retrieved from database of a medium size

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.

We explore a combined approach of associated memory and quantum annealing (QA) for pattern matching. In particular, we consider on high energy applications of this approach to binary classification, where the objective is to distinguish signal and background detection events.

This talk will give updates on achievements and extensions of project PlanQK (Platform and Ecosystem for Quantum-assisted Artificial Intelligence), funded by Federal Ministry for Economic Affairs and Energy. Furthermore, we will present novel research results, including - Insights on Training Neural Networks for QUBO Tasks - Approximating Archetypal Analysis Using Quantum Annealing - A Quantum Annealing Algorithm for Finding Pure Nash Equilibria in Graphical Games - Approximate Approximation on a Quantum Annealer

The ability to accurately classify disease subtypes is of vital importance, especially in oncology where this capability could have a life saving impact. Here we report a classification between two subtypes of non-small cell lung cancer, namely Adenocarcinoma versus Squamous cell carcinoma.

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.

What is the form and the nature of the distribution produced by quantum annealers? It turns out that much can be said by analysing the statistics of a single-qubit problem. We estimate the effective local field that describes the output distribution of a single-qubit problem for different input problem instances.

We design and implement a quantum laboratory to experimentally observe and study dynamical processes in quantum mechanics and quantum field theories. Our approach encodes the wave-function in an Ising model, which is then solved by a quantum annealer. As a proof-of-concept, we measure the probability for tunnelling from the false to true minimum for various tunnelling times, vacuum displacements and potential profiles.

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.

We demonstrate our research activities with several companies spreading the capability of quantum annealing. In addition, we show our products deepening the potential of quantum annealing. One of the products is "New Normal Scheduler", creating the most appropriate schedule while reducing the ratio of the office workers and maintaining the occasion of communication among all the employees.

Quantum computing is currently entering an era of real-world application development. Here we present our work on developing one of the first such applications by simulating an experiment, which exhibits quantum tunnelling between different electronic configurations. The material system we deal with is 1T-TaS2 in a metastable state, where electrons crystalize into a mosaic of lattices separated by domain walls. We developed a strongly correlated model which is able to describe the configurational ordering of electrons.

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.

It has been recently been shown that the new lower noise model of D-Wave QPU can perform better than previous versions at a solving spin glass problems. In the work I present here we investigate why and explore the effect of reduced noise on reverse annealing protocols by performing the same experiment on both lower noise and higher noise QPUs.

We experimentally quantify the impact that the quality of graph embeddings, used in programming a QAP, has on the sample distribution collected from the device. Quantum annealers have been shown, and we have experimentally corroborated, to sample from a distribution close to Boltzmann.

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.

Prediction of financial crashes in a complex financial network is known to be an NP-hard problem, i.e., a problem which cannot be solved efficiently with a classical computer. We experimentally explore a novel approach to this problem by using a D-Wave quantum computer to obtain financial equilibrium more efficiently... Our experiment paves the way to study quantitative macroeconomics, enlarging the number of problems that can be handled by current quantum computers.

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.

D-Wave 2000Q offers annealing options that allow a better control on the annealing parameters, which options can be used to mitigate the effects of hardware biases or to get additional insight into the dynamics of the process. We discuss our experience with three such features--spin reversal, quenching, and h-gain--for three different purposes.

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.

At this webinar, Hyperion Research SVP Bob Sorensen discussed the results and his analysis of new survey data on the challenges and opportunities experienced by early adopters of commercial quantum computing (QC). The intent of this effort is to explore and more clearly define potential quantum computing growth and expansion paths within organizations that have already taken their first steps in exploring the potential of quantum computing.

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.