Originally posted on technologyreview.
Quantum computing provides a new way of optimizing business processes with use cases spanning finance, logistics, telecommunications, and transport operations.
In the business world, the opportunities for applying quantum technology relate to optimization: solving difficult business problems, reconfiguring complex processes, and understanding correlations between seemingly disparate data sets. The main purpose of quantum computing is to carry out computationally costly operations in a very short period of time, while at the same time accelerating business performance.
Quantum computing can optimize business processes for any number of solutions, for example maximizing cost/benefit ratios or optimizing financial assets, operations and logistics, and workforce management—usually delivering immediate financial gains. Many businesses are already using (or planning to use) classic optimization algorithms. And with four international case studies, Reply has proven that a quantum approach can give better results than existing optimization techniques.
Speed and computational power are key components when working with data. The use of accelerated hardware such as quantum processing units or graphic processing units provide the performance needed for operations that require high computing power. Sectors such as logistics, finance, and manufacturing can benefit significantly from this approach. And the qubit, which is a key element of quantum computing, allows organizations to approach their challenges in a completely new way. As the technology matures, the prospect of quantum computing being at the full disposal of businesses—so-called quantum supremacy—seems to be getting closer.
In recent years, Reply has been involved in developing proof of concepts and projects that apply quantum algorithms to real use cases in many business areas, providing customers with concrete and effective answers to their problems.
In 2018, Reply obtained a research grant to use the Universities Space Research Association’s D-Wave quantum annealer, which is part of the Quantum Artificial Intelligence Research Programme that include participants like NASA and Google. And recently, Reply published a study in the Springer Quantum Machine Intelligence peer reviewed journal on how a quantum annealer can accurately solve complex optimization problems.
There are a number of ways that quantum computing can be deployed in real world settings, including in finance, logistics, and transport operations.
Quantum for finance optimization
Quantum computing is used for optimization in the finance realm. Thanks to a quantum algorithm developed with Reply, a credit institution was able to optimize daily collateral costs related to over-the-counter (OTC) derivatives trading, taking account of non-linearities in the model and implementing a dedicated simulation-based optimization tool to plan for multiple scenarios.
Quantum computing can be used also for portfolio optimization: Reply was able to help the institution limit its exposure by defining a set of assets with minimal correlation between them. Analyzing data relating to the correlations between the assets created a non-linear model, while the quantum algorithm made it possible to find the optimal portfolio allocation.
Quantum for logistics: delivery and workforce optimization
Another key area that Reply’s experts have focused on is the freight delivery and workforce management world. Identifying the optimal route for goods deliveries or workforce operations is a complex process, due to the high number of variables that come into play.
Reply interpreted both of these optimization problems in the form of a quantum algorithm, achieving far better results than those typically obtained using traditional techniques. Quantum solutions allow models to be continually refined—enhancing the realism and quality of solutions or adding new constraints such as narrower delivery windows—without having a significant effect on the computation times and at the same time optimizing ratios around route distance and productivity.
Quantum for train platform optimization
Reply has been involved in testing quantum technologies in rail transport. For a railway station that manages the arrival of 300 trains on 20 lines within a one-hour timeframe, a solution that identifies the best arrival track, optimizes passenger connections, and manages operations such as maintenance and movement of the trains solves many logistical challenges. In this specific case, optimizing means being able to accommodate more trains, guarantee more journeys, and thus increase revenue. The project, presented at the World Congress on Railway Research held in Tokyo in November 2019, made it possible to achieve the best and the most efficient combination and maximize the simultaneous arrival of different trains.
Quantum for telecoms: network planning
The telecommunications sector has also successfully launched quantum computing trials. In this field, Reply worked with a telecoms operator to optimize radio cell planning, implementing an algorithm for planning 4.5G and 5G network parameters with 10 times faster process optimization compared to traditional methods. This also made it possible to fine-tune radio cell planning, providing a higher performing and more reliable mobile service and enhanced customer experience.
Quantum computing: what’s next?
The range of problems that can be addressed through quantum formalism is broad: it does not stop at combinatorial optimization but, instead, crosses into other areas such as machine learning and quantum security. Quantum neural networks and quantum internet networks are just two of the more interesting ones.
The performance of quantum computers far outweighs current possibilities, opening us up to a new age of knowledge. This is undoubtedly a positive development. However, cybersecurity remains a primary concern, which is why quantum cybersecurity has become a highly relevant topic, and a number of sophisticated measures to protect commercial transactions and data transmission have emerged. These include the distribution of quantum keys, quantum security algorithms, and quantum random number generators.
Quantum machine learning (QML), on the other hand, makes the most of the advantages of two current themes: quantum computing and machine learning. Although QML is still in its early stages, it nevertheless offers a whole new world of opportunities, combining the new knowledge provided by machine learning with the accelerated calculation potential and the enhanced accuracy of quantum calculations.