The paper presents the basic aspects of the logic behind Quantum Computing and shows its fields of application and the state of the industry.
Better computation can be achieved with quantum algorithms in discrete and continuous optimization, logistics network optimization, warehouse
## Why Quantum? # The Optimization Gap: Why quantum’s most immediate industry application is decision making, not computing. Quantum-inspired data visualization with "The Optimization Gap" title, illustrating quantum optimization, decision-making, and complex problem solving in industry. In gate-based systems, that often leads to methods such as QAOA, which combine quantum state preparation with classical feedback loops.**. They differ technically, but both reflect serious attempts to apply quantum methods to the industrial decision problems companies already face. Through its work on gate-based methods, including QAOA, or the Quantum Approximate Optimization Algorithm, it has shown how optimization problems can be encoded into quantum circuits and refined through an iterative loop between quantum processing and classical feedback. Annealing and gate-based systems offer different routes into the same question: can quantum improve decision-making where complexity is starting to overwhelm even strong classical tools? Quantum optimization will matter only if it proves it can deliver better decisions in real operational settings, under real commercial constraints.
The proof-of-concept experiment elaborated below shows that AQT's quantum computers can be harnessed to solve real-world optimisation problems.
Optimization Applications as Quantum Performance Benchmarks. ACM Transactions on Quantum Computing, Volume 5, Issue 3. Combinatorial optimization is anticipated to be one of the primary use cases for quantum computation in the coming years. The Quantum Approximate Optimization Algorithm and Quantum Annealing can potentially demonstrate significant run-time performance benefits over current state-of-the-art solutions. Inspired by existing methods to characterize classical optimization algorithms, we analyze the solution quality obtained by solving Max-cut problems using gate-model quantum devices and a quantum annealing device. This is used to guide the development of an advanced benchmarking framework for quantum computers designed to evaluate the trade-off between run-time execution performance and the solution quality for iterative hybrid quantum-classical applications. The framework generates performance profiles through compelling visualizations that show performance progression as a function of time for various problem sizes and illustrates algorithm limitations uncovered by the benchmarking approach. As an illustration, we explore the factors that influence quantum computing system throughput, using results obtained through execution on various quantum simulators and quantum hardware systems.
“Good, classical heuristics exist that solve variants of the vehicle routing problem within 2–3% of optimal, but even an additional improvement of 0.5% could translate to millions of dollars per year in savings…” – MEGAN KOHAGEN, HONEYWELL DATA SCIENTIST, MATHEMATICIAN AND OPTIMIZATION THEORIST Smart Cities and Connected Ecosystem Fleet Management Distribution Center and Inventory Management Airline Routing Optimization Aircraft-on-Ground Logistics and Maintenance Scheduling 6 | www.honeywell.com | Quantum Computing for Optimization Applications SUPPLY CHAIN AND ROUTING OPTIMIZATION Many common optimization problems seek to route assets in a network with minimum cost while fulfilling operational constraints. 8 | www.honeywell.com | Quantum Computing for Optimization Applications 1Validating quantum computers using randomized model circuits, Quantum Milestone: How We Quadrupled Performance 2UK Pub Crawl with 24,727 cities , UK Pub Crawl of 49,687 cities 3Quantum Computing based Hybrid Solution Strategies for Large-scale Discrete-Continuous Optimization Problems, Quantum Impact: Optimization Solutions (Microsoft) 4Holographic quantum algorithms for simulating correlated spin systems BEYOND ROUTE OPTIMIZATION AND MOLECULAR SIMULATIONS Honeywell also is looking at other applications for applying our quantum solutions that could bring value on a longer horizon.
In addition to using quantum computers and quantum-inspired machines, it will also incorporate the integrated technology of hybrid quantum and classical approaches to solve high-complexity problems and to accelerate the quantum computing performance for real-time computing services. Develop high-performance hybrid quantum optimization technology to optimize quantum circuits and construct multi-objective and weighted financial investment portfolios, reducing search time, increasing returns, and lowering risk. 1. *Yao-Hsin Chou, Ching-Hsuan Wu, Pei-Shin Huang, Shu-Yu Kuo, Yu-Chi Jiang, Sy-Yen Kuo, and Ching-Ray Chang “Hybrid Quantum Annealing with Innovative Trend Ratio Model for Portfolio Optimization,” 2024 IEEE World Congress on Computational Intelligence (IEEE WCCI 2024), (Accepted for Publication), July 2024.*. *Reference:**Yun-Ting Zhang, Chin-Fu Nien, Chia-Wei Lin, Wen-Jui Chao, Chen-Yu Liu, Lien-Po Yu, Yuan-Ho Chen, “An Automated Toolchain for QUBO-based Optimization with Quantum-inspired Annealers,” 20th International SoC Design Conference (ISOCC), Jeju, Korea, Republic of, 2023, pp. This technology aims to develop an automated toolchain for quadratic unconstrained binary optimization (QUBO)-based quantum-inspired annealing optimization to solve combinatorial optimization problems (COP).
# Top 9 Quantum Computing Applications in Key Industries [2025]. Below are some of the most promising quantum computing applications. Quantum Computing Applications in Finance: Revolutionizing Risk Management. Quantum computers can process large datasets and optimize these models much more efficiently than classical computers. 2. Quantum Computing Applications in Finance: Revolutionizing Risk Management.png). 2. Quantum Computing Applications in Finance: Revolutionizing Risk Management.png). Quantum Computing Applications in Drug Discovery and Healthcare: Accelerating Innovation. Quantum Computing Applications in Drug Discovery and Healthcare. Quantum Computing Applications in Drug Discovery and Healthcare. Quantum Computing Applications in Cryptography. Quantum Computing Applications in Cryptography. Quantum Computing Applications in Quantum Simulations: Advancing Material Science. Quantum computers, however, can model these materials at the quantum level, potentially leading to the development of advanced materials with applications in everything from renewable energy to electronics. Quantum Computing Applications in Artificial Intelligence. Quantum Computing Applications in Artificial Intelligence. From healthcare to finance, logistics to materials science, the applications of quantum computing are vast and could drastically improve our ability to solve complex problems.