Analog then,
Digital now,
Quantum next.

We help organisations become quantum ready.


Barclays and IBM devise quantum algorithms for transaction settlement

Executive Summary On Oct 2019, researchers from Barclays and IBM published a research article on quantum algorithms for transaction settlement. Specifically, the authors extend the well-known Quantum Approximate Optimization Algorithms (QAOA) to solve the transaction settlement problem which is mapped onto a Mixed Binary Optimisation (MBO) problems. The authors demonstrate a proof-of-concept experiment on IBM’s 5-qubit machine obtaining results for three delivery-versus-payment transactions. Our opinions As mentioned in the paper, the transaction settlement problem, in the real-world situation, requires complex optimisation algorithm to settle as many transactions as possible or to maximise the total value of the settled transactions, while meeting both the legal constraints and optionality introduced by collateralising assets and utilising credit facilities. A full-fledged quantum computer can solve several optimisation problems exponentially faster than a classical computer using the standard Grover’s algorithm. However, current quantum devices, also known as noisy intermediate-scale quantum (NISQ) hardware, are small and very noisy. QAOA, used in the paper, is one of the standard quantum algorithms specifically designed for NISQ devices. It makes use of a classical feedback to passively correct noise in the quantum hardware. However, quantum advantage of QAOA, when applied to the real-world problems such as transaction settlement, is much less clear compared to Grover’s algorithm. In our opinion, the paper presented by IBM and Barclays provides an interesting mathematical foundation for using NISQ devices to tackle the transaction settlement problem. However, similar to other state-of-the-art works, both quantum software and hardware have to be improved before obtaining quantum advantage on real-world use cases.

IBM’s guides for quantum computing use cases for airlines

Executive Summary On March 2020, IBM published an article to discuss use cases of quantum computing for airlines. They predicted that signatures of “quantum advantage” would start to unfold from 2020 onwards. The term “quantum advantage” refers to the scenario where a quantum computer outperforms a start-of-the-art supercomputer at some tasks. In the next twenty years, air travel is expected to double, increasing operational complexity. This makes the airline industry particularly interesting to leverage quantum advantage. Benefits of the first movers are to first develop proprietary quantum software for industry-specific applications. Use cases predicted in the IBM report includes untangling operational disruption, enhancing contextual personalised services and optimising network planning globally. Our opinion The IBM report provides excellent and tangible explanation of how airlines could benefit from a quantum boost in the computational power. However, as also mentioned in the report, a quantum computer is not a magical machine that can solve every problem that cannot be solved by a classical computer. On the other hand, some problem that is easy for a classical computer may not be suitable for a quantum computer. Airlines should therefore partner with experts to start exploring use cases that are most relevant to them and most likely to gain quantum advantage from near-term quantum devices.


Lloyds develops quantum algorithms for linear PDES arising in Finance

Executive Summary On Dec 2019, researchers at Lloyds and Imperial College published an research article on hybrid quantum-classical algorithm to price European and Asian options. The authors do so by mapping the Black-Scholes model into the Schrodinger equation – the central equation in quantum mechanics. The authors then run numerical proof-of-concept experiments by simulating a 4-qubit quantum computer on a classical computer. Our opinions Pricing financial derivatives are important problems in quantitative finance that cannot be solved efficiently by a classical computer. With a fault-tolerant quantum computer, linear PDES such as the Black-Scholes model can be efficiently solved by the celebrated quantum algorithm known as HHL. However, such machine is still far reaching. In this paper, the authors devise a quantum algorithm for near-term quantum devices to tackle the Black-Scholes model in a scaled-down scenario. Similar to other state-of-the-art works, such algorithm can accommodate certain levels of noise in real quantum devices but still lack of mathematical proof of their quantum advantage, unlike the HHL algorithm. To reach the real-world application, both quantum software and hardware have to be improved. Nevertheless, this paper opens an exciting direction for further research on the use of quantum computing for quantitative finance.


Quantum computing for energy system optimisation

written by Dr. Jirawat Tangpanitanon, CQT & QTFT reviewed by Supharat Ridthichai, Venture Lead, Smart Electricity, PTT Innovation Lab (ExpresSo) The world’s energy consumption is growing about 2.3% per year according to Energy Information Administration and is expected to reach over 700 quadrillions Btu a year in 2040. Although Fossil fuels have been primary sources of the world’s energy, alternative renewable energy resources are urgently in need due to the environmental concern and the finite resources of the former. The most widely used renewable energy resources to date are hydro, wind and photovoltaic. According to Bloomberg New Energy Finance, wind and solar is predicted to supply 50% of the world’s energy consumption by 2050. Approximately 10 trillions of new investment is expected to go to renewable energy between now and 2050 Energy system optimisation The rising demand for renewable energy calls for highly-optimized energy management systems. Although renewable energy resources are ‘free’, they are hard to predict because of various factors such as fluctuation of solar irradiation, weather and wind speed. Recently, hybrid power systems utilizing the combination of solar, wind and hydro have been deployed to improve their reliability. The scale of which can range from a small unit supplying power for a single home to a large unit that can power a village or an island. Optimization methods are then used to find the cheapest combination of all power generators (both renewable and conventional) and the storage capacity that can support the expected demand with the minimum acceptable level of security. The models must also include meteorological data, solar, hydro, wind and battery systems. Renewable energy also naturally leads to distributed energy resources(DERs) such as rooftop solar PV units. DERs may include non-renewable generation, battery storage, electric vehicles, and other home energy management technologies involving internet of things in Smart Home. Optimization methods are needed to support the network or “smart grid” operation, i.e. finding the perfect balance between reliability, availability, efficiency and cost. Grid optimization ranges from power generation, transmission, distribution all the way to demand management. Doing such optimization on a large scale is computationally expensive. For example, only finding the optimal number of power generation units for a given demand requires a computational time that grows exponentially with the number of variables in the model. This means that, to find the best smart grid operation, the required computational resource will double every time a new node is added into the network. In practice, one likely has to settle for solutions that are not globally optimum. The challenge of large-scale optimization is also faced in other areas of the energy industry. For example, optimizing shale-gas supply chain networkcovering around 10,000 km square area may involve more than 50k variables and 50k constraints. A state-of-the-art supercomputer may take more than 15 hours or days in some cases to find a desirable solution. This high computational cost limits the efficiency of energy system optimization in national and global scales. Quantum computing Quantum computing provides a new paradigm to solve complex optimization problems. A quantum computer processes information differently than a conventional or ‘classical’ computer in two fundamental aspects. First, unlike a classical processing unit or a bit which can be either 0 or 1, a quantum processing unit known as a qubit can be 0 and 1 at the same time. The latter allows a quantum computer to simultaneously explore different solutions to a given problem before collapsing to the optimum one when measured. Second, two or more distant qubits can immediately ‘feel’ what happens to the other qubits without sending any signals. The phenomenon is known as quantum entanglement. The latter is a must ingredient for quantum speedup. Building a full-scale quantum computer is, however, far from trivial. Quantum hardware is extremely sensitive to noise. Most quantum hardware architectures require the qubits to be cooled down to sub-milli-kelvin, much colder than the outer space. Despite the far-reaching goal, tremendous progress has been made during the past two decades including Nobel-winning experiments. In the past few years, governments worldwide and giant tech companies such as Google, IBM, Microsoft, Alibaba, and many others have been investing heavily in quantum computing. Examples include 2 billion-euros quantum technology European flagship, 10 billion-USD china’s quantum center, and 1.2 billion-USD US’s quantum computing bill. Quantum computing for energy system optimization The promise of quantum computing on energy system optimization also attracts a lot of attention from the energy sector. For example, in January 2019, a giant gas company ExxonMobil has signed an agreement with IBM to develop next-generation energy and manufacturing technologies using quantum computing. In April 2019, U.S. Department of Energy announced a plan to provide $40 million USD for developing new algorithms and software for quantum computers. The same department also announced $37 million USD this month for materials and chemistry research in quantum information science. Recently, two scientists from Cornell University have conducted a systematic study of quantum computing for energy system optimization problems. Their results are published in Energy, July 2019. As a proof of concept, the authors employ IBM’s and Dwave’s cloud quantum computing platforms to solve simplified problems in various areas of the energy industry including facility-location allocation for energy systems infrastructure development, unit commitment of electric power systems operations, and heat exchanger network synthesis. For the facility-location allocation problem, the goal is to find optimum locations of facilities such as solar or wind power farms that minimize facility opening and transportation cost for given energy demand and resource availability. This problem is mapped to what is known as a quadratic assignment problem. The latter is difficult to optimize with a classical computer. The table below shows the runtimes of a single CPU core and D-wave’s quantum processor for different numbers of facilities. It shows that the computational time for the former grows exponentially with the number of facilities. However, it is not the case for the latter. For 14 facilities, the single CPU core takes more than 11 hours to run, while the Dwave processor only takes 16 minutes. This quantum speedup provides promising preliminary evidence of the potential of quantum computing on the energy industry. However, for other applications, the authors find that the D-wave quantum processor does not provide quantum advantage due to the noise and the limited connectivity of the current quantum hardware. As mentioned above, quantum computers are still in their very early stage, perhaps analogous to the vacuum-tube era of nowaday computers in the 1950s. However, the field is evolving fast and the promise is high. Industries and professionals should, therefore, keep track of all relevant information to make strategic choices.  

Quantum optimization for aircraft’s tail assignment problems

Executive Summary On May 2020, researchers at Chalmers University of Technology, Sweden, and Jeppesen Systems published a research article on quantum algorithms for the Tail Assignment Problem (TAP) . The latter is the problem of deciding which individual aircraft should operate each flight. Each flight’s route, a set of flights operated in sequence by the same aircraft, has to satisfy a number of constraints to be considered legal to operate. The goal is to minimise the total cost of route and unassigned flights The authors simulate up to 25 qubits using a classical computer and employ the so-called Quantum Approximate Optimization Algorithms (QAOA) for a proof-of-concept experiment. The scaled-down setup involves up to 278 flights and 25 routes. Our opinions A quantum computer can find the global optimum of the TAP exponentially faster than a classical computer, using the so-called Grover’s algorithm. However, such algorithm would require a fault-tolerant quantum computer which is still in its infancy state. The authors therefore use QAOA, the hybrid quantum-classical algorithm designed for near-term noisy quantum devices. The drawback is that quantum speed up of QAOA when applying to the TAP is unclear compared to Grover’s algorithm. In addition, the authors further simplify the optimisation problem to a problem of finding feasible solutions. Although the latter is still a hard problem for a classical computer, further modification on the QAOA protocol is needed to order to tackle a real-world TAP scenario.




Deploy advanced mathematical optimization with our hybrid quantum-digital engines to reduce operation cost of your business today.



Assess your cybersecurity risk of quantum attack and prepare for the incoming post-quantum cryptography standards.


Quantum Partners

Media Partners