Classiq, a Tel Aviv-based startup that provides a model for building quantum algorithms, announced it is working with Rolls-Royce to implement novel computational fluid dynamics algorithms. Rolls-Royce will be able to create, enhance, and evaluate scalable quantum algorithms with the Classiq platform. Rolls-Royce will be able to apply computational fluid dynamics techniques in a hardware-independent manner.
Heavy-lifting, intricate numerical simulations of fluid and gas processes are the focus of computational fluid dynamics (CFD). CFD is essential for enhancing new equipment designs since it may be used to optimize aerodynamics and thermodynamics, among other things. By combining quantum and conventional computing methods, the cooperation will make use of each technology’s advantages.
Classiq’s synthesis engine implicitly explores a vast design space of potential circuits to meet the need of each user and provide state-of-the-art optimization—leaving users with more resources, whether it’s time, qubits, quantum gates, or accuracies. This functional-level exploration is possible only when synthesizing circuits from functional models, an approach fundamentally different from existing quantum solution schemes.
Capability building is an important step to take in order to be ready for this new computing age, which is predicted to bring about a computation speedup for quantum computers compared to conventional computers in the future. Rolls-Royce is putting into practice hardware-independent, optimized algorithms for today’s and tomorrow’s quantum computers with the assistance of Classiq.
In order to be prepared for the ‘edge’, when quantum machines can solve the same equations quicker than the fastest supercomputers, Rolls-Royce engineers will be working on methods to solve and predict fluid dynamics. The algorithms will cover CFD, which deals with heavy and complex numerical simulations of fluids and gases.
Harrow-Hassidim-Lloyd (HHL) quantum algorithm, which can solve Linear System Problems with exponential speed-up over the classical method, is the basic of many important quantum computing algorithms.
The HHL method is made to swiftly solve several linear equations. Its key asset is the availability of a hybrid environment, where programmers may write Python code and direct both conventional and quantum devices.
By using the HHL equations and applying them to fluid dynamics, it will be possible to solve the non-linear portions of the equations on a traditional supercomputer and then send the linear portions to a QPU (Quantum Processing Unit), which can complete the operation much more quickly.
While most discussions of quantum computing focus on when quantum computers will be able to consistently beat their classical counterparts, the reality of quantum computing will be a hybrid approach for many application cases.
“Rolls-Royce will use the Classiq platform to design quantum algorithms for CFD simulations. CFD is essential for many aerospace use cases that include airflow simulation. CFD is a very complex set of partial differential equations, that are unsolvable by classical computers and even HPCs (it takes an exponential time with the problem’s size). Quantum computers potentially offer an exponential speedup for these calculations. Python itself is good as many programmers are used to it. Having said that, python should be an envelope to a domain-specific language for quantum computing. This is exactly what we did with our QDL – quantum description language,” commented Nir Minerbi, CEO of Classiq.
Classiq addresses the challenges in the development of quantum computing by bridging the gap of complex quantum logic. The company builds a new layer of the quantum software stack, increasing the level of abstraction and allowing developers to implement their ideas and concepts without the need to design the specific quantum circuit at the gate level.
Computational Fluid Dynamics (CFD)
At an exponentially expanding pace, the modern industry creates and manufactures more complicated items. Production firms need tools that enable them to research and foresee potential issues in order to minimize potential mistakes throughout the design process and time to market. This is necessary for them to remain competitive.
When prototyping and producing the product as well as throughout the design and development phases, simulation enables the knowledge acquisition required to enhance the product.
Through the use of numerical methods, computational fluid dynamics enables the simulation of both the behavior of liquid and gaseous fluids. It is used in a variety of industries, including automotive, aerospace, and electronic cooling systems.
The numerical method that enables the computer-based study of fluid mechanics is known as computational fluid dynamics. The Navier Stokes equations, which mathematically define the fluid mechanics via its primary variables of pressure, temperature, density, velocity, and viscosity, may be solved using computational fluid dynamics (CFD).
Navier Stokes equations
The Navier-Stokes equations are the equations that describe the motion of a real fluid. They contemplate the contribution of all forces acting on an infinitesimal element of volume and its surface. Given a certain mass of fluid contained in a region of space, two types of forces act on it: volume forces and surface forces.
Volume forces are extensional forces produced by causes external to the region considered. These causes are gravity; actions due to electric and/or magnetic fields; non-inertial forces.
Since these forces are proportional to volume, they are expressed per unit volume. Surface forces are forces of an intensive nature and can be traced back to an interaction of the fluid under consideration with the rest of the considered physical system expressed through the boundary surfaces.
The Navier-Stokes equations are a system of three balance equations (partial derivative equations) of continuum mechanics, which describe a linear viscous fluid; in them Stokes’s law (in the kinematic balance) and Fourier’s law (in the energy balance) are introduced as constitutive laws of the material. The equations are named after Claude-Louis Navier and George Stokes.
Any equipment, machine, or structure whose functioning involves fluid interaction, whether for internal or exterior flows, may benefit from CFD fluid-dynamic simulation. Calculate wind loads on civil and military telecommunications antennas, roofs, tensile structures, and radomes, accurately simulating wind tunnel testing.
- To increase flow and heat transmission efficiency and uniformity, optimize the geometry of a machine’s internal ducts.
- Calculate heat transfer in fluids and solids, such as for cooling electrical or electronic equipment.
CFD studies enable the simulation of situations like tsunamis, meteorological occurrences, and environmental repercussions in addition to traditional industrial uses. Weather centers utilize supercomputers because this kind of study might need a significant amount of computational capacity.
CFD for quantum
It is necessary to reach a certain degree of abstraction that makes it easier to write algorithms for quantum computers and enables the execution of algorithms on various hardware platforms. A system will be developed to manage and automate the process as much as possible so that the upper layer can be independent of the hardware and operate in a hybrid environment, according to Classiq. The company will provide and generate optimized quantum circuits that are hardware agnostic, allowing them to be used on various quantum computing platforms that will be developed in the future.
All of this will also enable Rolls-Royce to achieve zero carbon emissions emissions due to ongoing, minor but crucial technological advancements at all levels.
Quantum machines will be trustworthy enough to do thorough analyzes in a few years. The objective of the algorithms is to increase the adaptability of the hardware to all industrial applications that require it.
A modern CPU is useless without an operating system and support software tools in today’s computer world. The same is true in a quantum computer. As important as the hardware is, the software is also crucial to powering a quantum revolution.
The complexity of writing quantum software has another unfortunate side effect: It is difficult to find experts in quantum programming, as this differs from classical programming. Quantum programming experts need to know about both software engineering and quantum physics.