Hundreds of Quantum Applications

Employing D-Wave's annealing quantum computing solutions as accelerators in the speed and quality of training our generative AI-driven, drug discovery (DD) analysis systems, we constructed a hybrid quantum and classical computer system, aiming to extend the applicable range of molecular-generation AI systems in the DD field (i.e., AI-driven Drug Design). The goal of our project is to pioneer a new process for discovering 'first-in-class' pharmaceutical small compounds, which is performed with supports by D-Wave's team of professional services experts. In my talk, we report substantial aspects of our projects, based on the above-mentioned strategy and present progress.

Maintaining steady connections on a mobile network entails a lot of behind-the-scenes effort. Work done by Japanese telecommunications company NTT DOCOMO demonstrated that quantum computing can help mitigate potential performance issues on mobile networks during periods of heavy use. This is now an in-production application for NTT DOCOMO.

Supreeth Mysore Venkatesh, Antonio Macaluso, Marlon Nuske, Matthias Klusch, Andreas Dengel 

Deutsches Forschungszentrum für Künstliche

https://arxiv.org/abs/2408.06007

The increasing number of Low Earth Orbit (LEO) satellites, driven by lower manufacturing and launch costs, is proving invaluable for Earth observation missions and low-latency internet connectivity. However, as the number of satellites increases, the number of communication links to maintain also rises, making the management of this vast network increasingly challenging and highlighting the need for clustering satellites into efficient groups as a promising solution. This paper formulates the clustering of LEO satellites as a coalition structure generation (CSG) problem and leverages quantum annealing to solve it. We represent the satellite network as a graph and obtain the optimal partitions using a hybrid quantum-classical algorithm called GCS-Q. The algorithm follows a top-down approach by iteratively splitting the graph at each step using a quadratic unconstrained binary optimization (QUBO) formulation. To evaluate our approach, we utilize real-world three-line element set (TLE/3LE) data for Starlink satellites from Celestrak. Our experiments, conducted using the D-Wave Advantage annealer and the state-of-the-art solver Gurobi, demonstrate that the quantum annealer significantly outperforms classical methods in terms of runtime while maintaining the solution quality. The performance achieved with quantum annealers surpasses the capabilities of classical computers, highlighting the transformative potential of quantum computing in optimizing the management of large-scale satellite networks.

Abhishek Awasthi∗, Nico Kraus†, Florian Krellner‡, David Zambrano†

*BASF Digital Solutions GmbH, Ludwigshafen am Rhein, Germany

†Aqarios GmbH, Munich, Germany

‡SAP SE, Walldorf, Germany

This work is a benchmark study for quantum- classical computing method with a real-world optimization problem from industry. The problem involves scheduling and balancing jobs on different machines, with a non-linear objective function. We first present the motivation and the problem description, along with different modeling techniques for classical and quantum computing. The modeling for classical solvers has been done as a mixed-integer convex program, while for the quantum-classical solver we model the problem as a binary quadratic program, which is best suited to the D-Wave Leap’s Hybrid Solver. This ensures that all the solvers we use are fetched with dedicated and most suitable model(s). Henceforth, we carry out benchmarking and comparisons between classical and quantum-classical methods, on problem sizes ranging till approximately 150, 000 variables. We utilize an industry grade classical solver and compare its results with D-Wave Leap’s Hybrid Solver. The results we obtain from D-Wave are highly competitive and sometimes offer speedups, compared to the classical solver.

Supply Chain/ Logistics SavantX with videoQuantum Computing has shown how it can make a difference in optimizing logistics. SavantX developed the HONE (Hyper Optimized Nodal Efficiency) Engine, which uses proprietary data science and quantum computing to present real-time optimized solutions for use at the second largest container terminal at the Port of Los Angeles, Pier 300. SavantX leverages the D-Wave quantum computer repeatedly in transportation applications in real-time. Hear about lessons learned in applying quantum solutions to a real-world application and how quantum compares to the classical approach.

Having the ability to efficiently detect the location where a leak occurs in a network is high on the bucket-list of any water distribution company worldwide. The determination of “how many” pressure-sensors to put “where” in the network is known to be an NP-hard problem. delaware in Belgium joined forces with De Watergroep (Belgium) and KWR (The Netherlands) and developed a tractable approach to determine “how many” and “where”. The QUBO-formulation of this problem allows for a fast determination of the optimal number and location of pressure-sensors via D-Wave’s quantum solver, which now becomes feasible in days instead of centuries. This webinar explains the steps taken and the outcome reached.

The Port of Los Angeles is known as America's busiest port. In a time where we are facing global supply chain challenges, it's essential that the Port operates to its maximum capacity with the highest level of efficiency possible. In conjunction with Fenix Marine Services, SavantX developed and deployed new technology using AI and D-Wave’s quantum computer to optimize terminal operations. The results of their work doubled cargo handling equipment productivity and produced more predictable cargo flows. Watch this webinar to learn more.

Pattison Food Group has over 100 stores that offer e-commerce delivery. 3-4 people are dedicated to manually creating driver schedules each week. Pattison worked with D-Wave to create these schedules automatically, taking scheduling rules (seniority, preferences/history, and policies) into account. As a result, weekly manual efforts for initial scheduling creation was reduced by ~80%​.

Dixit, V.V., Niu, C. Quantum computing for transport network design problems. Sci Rep13, 12267 (2023).

Transport network design problem (TNDP) is a well-studied problem for planning and operations of transportation systems. They are widely used to determine links for capacity enhancement, link closures to schedule maintenance, identify new road or transit links and more generally network enhancements under resource constraints. As changes in network capacities result in a redistribution of demand on the network, resulting in changes in the congestion patterns, TNDP is generally modelled as a bi-level problem, which is known to be NP-hard. Meta-heuristic methods, such as Tabu Search Method are relied upon to solve these problems, which have been demonstrated to achieve near optimality in reasonable time. The advent of quantum computing has afforded an opportunity to solve these problems faster. We formulate the TNDP problem as a bi-level problem, with the upper level formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem that is solved using quantum annealing on a D-Wave quantum computer. We compare the results with Tabu Search. We find that quantum annealing provides significant computational benefit. The proposed solution has implications for networks across different contexts including communications, traffic, industrial operations, electricity, water, broader supply chains and epidemiology.

Satellite image acquisition scheduling is a problem that is omnipresent in the earth observation field; its goal is to find the optimal subset of images to be taken during a given orbit pass under a set of constraints. This problem, which can be modeled via combinatorial optimization, has been dealt with many times by the artificial intelligence and operations research communities. However, despite its inherent interest, it has been scarcely studied through the quantum computing paradigm. Taking this situation as motivation, we present in this paper two QUBO formulations for the problem, using different approaches to handle the non-trivial constraints. We compare the formulations experimentally over 20 problem instances using three quantum annealers currently available from D-Wave, as well as one of its hybrid solvers. Fourteen of the tested instances have been obtained from the well-known SPOT5 benchmark, while the remaining six have been generated ad-hoc for this study. Our results show that the formulation and the ancilla handling technique is crucial to solve the problem successfully. Finally, we also provide practical guidelines on the size limits of problem instances that can be realistically solved on current quantum computers.

A few minutes spent browsing the web is enough to expose most internet users to lots of advertisements, most of which have been targeted to some extent based on the viewer’s preferences and interests. But even if this has become one of the predominant form of marketing in the 21st century, the global market value for TV advertising exceeded $225B in 2022.

Earth observation satellites (EOS) collect vital data for various applications such as weather forecasting, disaster management, environmental monitoring, etc. Maximizing the value of this data requires designing optimal EOS missions to capture targets with high business value or priority while satisfying complex constraints such as storage capacity, energy limits, weather, etc. However, traditional computing methods often struggle with the complexity of optimizing EOS mission schedules, leading to suboptimal target selection and reduced data collection efficiency.

In this paper, we demonstrate the potential of our quantum algorithm to optimize EOS mission schedules and improve the efficiency of multiple EOS in real-time. The aim is to maximize the acquisition of high-priority targets with significant business value within the constraints of limited resources. We evaluated the performance of our quantum algorithm by comparing it with two classical optimization algorithms: simulated annealing and Gurobi optimizer. Our quantum algorithm outperformed the Gurobi optimizer by 23.46%.

NEC Australia and D-Wave were selected by the Australian Department of Defence to demonstrate the use of hybrid quantum computing technology to solve a “last mile resupply” problem. The technology optimised how autonomous vehicles were used to resupply army forces from a central base. A live demonstration is included in this talk.

Modelling behaviors in networks is highly complicated but essential for understanding dynamics of the current world, especially the adoption of new technologies. In this talk, experts from Bank of Canada and Multiverse Computing extend Jackson and Watts (2002) to study how firms located in a trade network adopt cryptocurrency as a method of payment

Recommender systems help users navigate the vast number of options at their disposal in this era of information. A challenging step is to understand how to select item attributes that best represent the user perception, which is often time consuming and requires domain knowledge and understanding of user behavior. They represent the feature selection as an optimization problem and solve it on a D-Wave system.

DirectFood.store (DFS) is a truly farm to table solution that pickups food from local farms and vendors across a region and delivers customer the same day it was picked up. This is done without the use of warehouses and all of the food a customer ordered arrives at the same time. Scaling this delivery model, which they call many-to-many delivery, is made more complex when various business rules (constraints) are added to the equation. During this talk, they discuss how DFS successfully worked with D-Wave to scale this delivery model.

Accenture is working with telecom companies on the application of quantum computers to complex business problems. Watch this talk and find out more.

In this talk we present a short overview of recent quantum applications in VW. Using the Quantum Shuttle project as motivation, we show how to construct live quantum optimization services for production applications. Specifically, we showcase a new application—the multi-car paint shop problem—and provide a live demo of this optimization system with real-world data.

Reverse translation of polypeptide sequences to expressible mRNA constructs is a NP- hard combinatorial optimization problem. This work investigates the potential impact of leveraging quantum computing technology for codon optimization. An adiabatic quantum computer (AQC) is compared to a standard genetic algorithm (GA) programmed with the same objective function. The AQC is found to be competitive in identifying optimal solutions and future generations of AQCs may be able to outperform classical GAs.

Western Canadian grocery retailer Save-On-Foods is using hybrid quantum algorithms to bring grocery optimization solutions to their business, with pilot tests underway. The company has been able to reduce the time an important optimization task takes from 25 hours to a mere 2 minutes of calculations each week. Even more important than the reduction in time is the ability to optimize performance configuring all interactions - this is simply not possible using traditional methods.

Recruit Communications provides a wide variety of online services including online advertising, job search, travel, dining, hotel reservations and more. In this project, Recruit used several new methods to optimize how an advertiser's budget is allocated over time for display advertising. Their results showed that the D-Wave quantum annealer found a better solution than the often-used greedy method.

Accenture has explored more than 150 use cases and developed 10 functional cross-industry applications for quantum computers. One of the most promising involves finding optimal donor-patient matches in a kidney exchange network, as Accenture aims to develop a robust algorithm to be used by hospital networks and government agencies nationwide.

In this paper, researchers from Volkswagen present a real-world application that uses quantum technologies. Specifically, they show how to map certain parts of a real-world traffic flow optimization problem to be suitable for quantum annealing. They show that time-critical optimization tasks, such as continuous redistribution of position data for cars in dense road networks, are suitable candidates for quantum computing.

Peptides are mid-size molecules composed of amino acids and constitute some of nature's best drugs. Designing these mid-size molecules computationally remains a challenge due to the astronomical search space and complex energy dynamics. Here, we explore how quantum annealing and hybrid approaches may offer a new alternative toolkit for designing peptide therapeutics that could hold the key to groundbreaking new drugs.

Keeping GE’s robust R&D program running presents significant logistical challenges, especially when it comes to asset sustainment. Using D-Wave’s quantum computing system, the company set out to improve resource allocations for machine repairs and minimize resource contention when multiple repairs are requested at once.

As part of their work supporting Innovate UK, British multinational defense, security, and aerospace company BAE Systems undertook an assessment of how to optimize planning and scheduling of operations, distribution, and telecommunications across dozens of major industries using quantum computing.

When BMW began looking for ways to optimize their production lines, they turned to D-Wave. Using quantum algorithms, the company streamlined the complexity and requirements for programming robots used in PVC application and joint sealing.

Several finance-related applications are discussed in this talk including (1) A Multi-Period Optimal Trading Strategy, (2) Quantum-Ready Hierarchical Risk Parity (QHRP), (3) Real-Time Optimization Framework, and (4) Tax Loss Harvesting

In addition to creating smarter, safer, more environmentally friendly automotive components, DENSO envisions incorporating quantum computing into everything from production and robotics to scheduling and logistics for product delivery. Work is already underway to make that vision a reality. DENSO recently completed proof-of-concept work using D-Wave's quantum platform, aimed at optimizing control of Automated Guided Vehicles (AGVs) on their factory floors.

The study of protein-ligand binding is a critical part of drug development, the development of novel materials for medical applications, and understanding many of the processes that take place in the human body. Booz Allen Hamilton has taken the next step on that road by applying the power of quantum computing to these important chemical processes.

Resource allocation and planning problems in telecommunications—such as network layouts, job scheduling, configuring overlapping calls, are often algorithmically hard to address. Using quantum annealing, British Telecom’s Applied Research division uncovered how D-Wave’s 2000Q could dramatically transform the industry.

The Large Hadron Collider (LHC) circulates counter-rotating beams of protons in closely packed bunches that cross at designated interaction points. Particle physics apparatuses at these interaction points detect particles created at each proton-proton (p-p) collision and are used to reconstruct physics at the TeV energy scale. We use the D-Wave 2000Q to perform this reconstruction on artificial events where the positions of p-p collisions and tracks are drawn from measured LHC distributions.

A typical day at the Frankfurt Airport involves coordinating the real-time movements of hundreds of aircraft carrying more than 170,000 passengers between 278 gates. DLR applied quantum annealing to the problem of optimizing gate assignments, with the goal of reducing total passenger transit time.

Groovenauts and Mitsubishi Estate used a D-Wave system to optimize truck routes for waste collection, reducing total distance traveled from 2,300 km to just 1,000 km. As a result, CO2 emissions would be reduced by approximately 57%, and the number of vehicles reduced by approximately 59%.

D-Wave's annealer has mostly been applied to QUBO problems with integer variables, and CINECA sought to explore problems with real variables, which require a large number of binary variables. CINECA sought to use D-Wave's latest annealing features (anneal pause and reverse anneal) to explore one problem with real variables, low-rank matrix factorization.

For almost four years, Susan Mniszewski and colleagues at Los Alamos National Laboratory have been exploring the well-known graph partitioning problem with the D-Wave quantum computer. This recent report, involving a new multilevel algorithm, shows continued good results when compared to the industry-standard classical algorithms.

In this work the author shows how to solve non-linear integer programs using a novel hybrid quantum-classical approach, and present exciting numerical results on D-Wave that outclass best-in-class commercial classical software; how to compile using novel methods from algebraic Ggeometry; and analyzes quantum speedups by reframing it in the language of birth and death of anti-crossings and algebraic topology.

In the world of finance, even small improvements in Return on Assets (ROA) can make a massive impact. Powered by D-Wave's infrastructure, CogniFrame's quantum computing software allows financial institutions to make better investment and management decisions. Using a hybrid approach, CogniFrame demonstrated a dramatic decrease in the amount of time needed to find optimal solutions for complex problems involving insurance, pensions, and asset management.

"One-hot constraints" are common in QUBO formulations - these are constraints in which exactly one out of many choices must be selected. Using the Volkswagen traffic flow problem as an example, DENSO sought to find an improved QUBO formulation for one-hot problems. Their results are promising for certain types of problems, and their research continues.

In partnership with Reply Data, FS Italiane has begun to explore quantum solutions to a number of hard optimization problems, including scheduling, assigning platforms to incoming trains, worker deployment, maintenance, and freight load distribution.

In 2019, DENSO and Tohoku University announced the creation of an algorithm designed to improve the ability of a D-Wave system to handle complicated problems. The algorithm partitions an original, large problem into a group of subproblems. The D-Wave system then iteratively optimizes each subproblem to eventually solve the original, larger one. DENSO plans to use the algorithm to address everything from public transportation to the coordination of robotic systems in factories.

The idea of “forward-reverse error mitigation” (FREM) was formulated during an open forum session at the Qubits North American 2018 meeting. We hence explored FREM sampling as a novel way to try to improve ground-state sampling statistics on quantum annealers with no cost in ancilla qubits.

Many financial investors today are including cryptocurrency as part of their portfolios. At the University of Tennessee, researchers investigated how quantum computing can be used to optimize these investment selections to minimize risk, maximize returns, and stay on budget.

The 3-dimensional fold of an RNA molecule is largely determined by patterns of intramolecular hydrogen bonds between bases. Predicting the hydrogen bonding network from the sequence, also referred to as RNA secondary structure prediction or RNA folding, is a nondeterministic polynomial-time (NP)-complete computational problem. An Adiabatic Quantum Computer (AQC) is compared to a Replica Exchange Monte Carlo (REMC) algorithm programmed with the same objective function, with the AQC being shown to be highly competitive at rapidly identifying low energy solutions. The method proposed in this study was compared to three algorithms from literature and was found to have the highest success rate.

VW is leading the way in the application of quantum computing in the automotive industry. In addition to using D-Wave's 2000Q to optimize factory production, the company has tested a wide range of applications, including the optimization of traffic flow via GPS, the development of neural networks and machine learning for self-driving cars, discovery of new materials for manufacturing, and catalyzing improvements in automotive design.

In this work, the authors propose using quantum annealing for the theory of cuts, a field of paramount importance in theoretical computer science. They have proposed a method to formulate the Minimum Multicut Problem into the QUBO representation, and the technical difficulties faced when embedding and submitting a problem to the quantum annealer processor.

This is a heuristic approach on how to optimally schedule jobs using a quantum computer. Given a set of jobs and a finite number of machines, how should we schedule the jobs on those machines such that all the jobs are completed at the earliest possible time?

Toyota CRDL developed a hybrid quantum-classical algorithm for model predictive control (MPC). Unlike the usual application scenario for MPC algorithms the research team used the D-Wave system for dynamic control problems in which solutions must be obtained at high speed, specifically stabilization of a spring-mass-damper system, such as the ones in our cars that absorb shock on uneven roads; and dynamic audio quantization for radios and GPS devices. In both cases, the D-Wave method exhibited better performance than two classical approaches.

This paper provides a quantum annealing algorithm in QUBO form for a dynamic asset allocation problem using expected shortfall constraint.

In this paper we tackle the problem of dynamic portfolio optimization, i.e., determining the optimal trading trajectory for an investment portfolio of assets over a period of time, taking into account transaction costs and other possible constraints. This problem, well-known to be NP-Hard, is central to quantitative finance.

In this paper a quadratic problem in finance is taken, the Portfolio Optimisation problem. Here, a set of assets is chosen for investment, such that the total risk is minimised, a minimum return is realised and a budget constraint is met. This problem is solved for several instances in two main indices, the Nikkei225 and the S&P500 index, using the state-of-the-art implementation of D-Wave’s quantum annealer and its hybrid solvers. The results are benchmarked against conventional, state- of-the-art, commercially available tooling. Results show that for problems of the size of the used instances, the D-Wave solution, in its current, still limited size, comes already close to the performance of commercial solvers.

Chicago Quantum (SM) has built an equity portfolio optimization application that helps investors and investment managers to select groups of stocks to buy and hold. This application selects groups of stocks out of a universe of 60 liquid, US stocks that have expected returns that outweigh the expected variance of returns based on one year of historical trading data. Initial results are positive and compelling.

Previous work by GE Research has looked at using quantum computing to do resource and path planning to improve the efficiency of our asset sustainment operations around things like jet engines,  wind turbines and medical equipment repairs.  The work was meant to be exploratory and our projections showed it taking a few more years before it would be competitive with other approaches.  With access to the latest generation of hardware and hybrid algorithms we are beginning to believe that our projections are too conservative and that the time for industrial use of quantum computing has already started.

Recent work that solves the equilibrium of a nonlinear financial network and configuration of an optimal logistic network. (1) 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.(2) The logistic network design is an abstract optimization problem which, under the assumption of minimal cost, seeks the optimal configuration of infrastructures and facilities of the supply chain based on customer demand.

Addressing the world’s climate emergency is an uphill battle, requiring a multifaceted approach including optimal deployment of green-energy alternatives. Such undertakings often involve computationally-expensive optimisation of black-box models in a continuous parameter space. We observe good performance on the DW-2000Q QPU - even using default settings - and higher sensitivity of performance to number of samples and annealing time for the Advantage QPU. These results are promising and potentially justify further investment in hardware, software, and optimisation research, in particular, in the area of solvers for dense QUBOs

We continue to investigate the use of quantum computers for building an optimal portfolio out of a universe of 60 U.S. listed, liquid equities. Starting from historical market data, we apply our unique problem formulation on the D-Wave Systems Inc. D-Wave 2000Q (TM) quantum annealing system (hereafter called D-Wave) to find the optimal risk vs return portfolio. We approach this first classically, then using the D-Wave, to select efficient buy and hold portfolios. Our results show that practitioners can use either classical or quantum annealing methods to select attractive portfolios. This builds upon our prior work on optimization of 40 stocks.

The possibility of using quantum computers for electronic structure calculations has opened up a promising avenue for computational chemistry. Towards this direction, numerous algorithmic advances have been made in the last five years. The potential of quantum annealers, which are the prototypes of adiabatic quantum computers, is yet to be fully explored. In this work, we demonstrate the use of a D-Wave quantum annealer for the calculation of excited electronic states of molecular systems. These simulations play an important role in a number of areas, such as photovoltaics, semiconductor technology and nanoscience.

This paper discusses how to enhance often time-consuming RBM training processes based on the commonly used Gibbs sampling using an improved version of quantum sampling. To prevent overfitting, they propose some solutions that help acquire less probable samples from the distribution by adjusting D-wave control and embedding parameters.