Classiq, a Tel Aviv-based startup that provides a model for building quantum algorithms, announced that it is working with Rolls-Royce to implement new computational fluid dynamics algorithms. Rolls-Royce will be able to create, improve 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 and complex numerical simulations of fluid and gas processes are at the center of computational fluid dynamics (CFD). CFD is essential to improve the design of new equipment because it can be used to optimize aerodynamics and thermodynamics, among others. By combining quantum and conventional computing methods, the cooperation will leverage the advantages of each technology.
Classiq’s synthesis engine implicitly explores a vast space of potential circuit design to meet each user’s needs and provide state-of-the-art optimization, leaving users with more resources, be it time, qubits, quantum or precision gates. This exploration at the functional level is only possible when synthesizing circuits from functional models, a fundamentally different approach from existing quantum solution schemes.
Capacity building is an important step to take to be ready for this new computing era, which is expected to result in faster computations for quantum computers compared to conventional computers in the future. Rolls-Royce puts optimized, hardware-independent algorithms into practice for today’s and tomorrow’s quantum computers with the help of Classiq.
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In order to be prepared for the “edge”, when quantum machines can solve the same equations faster than the fastest supercomputers, Rolls-Royce engineers will work 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.
The Harrow-Hassidim-Lloyd (HHL) quantum algorithm, which can solve linear system problems with exponential speedup compared to the classical method, is the basis of many important quantum computer algorithms.
The HHL method is designed to quickly solve several linear equations. Its main strength is the availability of a hybrid environment, where programmers can write Python code and drive both conventional and quantum devices.
By using the HHL equations and applying them to fluid dynamics, it will be possible to solve the nonlinear parts of the equations on a traditional supercomputer and then send the linear parts to a QPU (Quantum Processing Unit), which can complete the operation much faster.
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, unsolvable by classical computers and even HPCs (it takes exponential time with the size of the problem). Quantum computers potentially offer exponential speed-up for these calculations. Python itself is good because many programmers are used to it. That said, python should be a wrapper to a domain-specific language for quantum computing. This is exactly what we have done with our QDL – Quantum Description Language,” commented Nir Minerbi, CEO of Classiq.
Classiq addresses the challenges of quantum computing development by bridging the gap in 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 having to design the specific quantum circuit at the gate level.
Computational Fluid Dynamics (CFD)
At an exponentially expanding rate, modern industry creates and manufactures more complicated items. Manufacturing companies need tools that allow them to find and predict potential issues to minimize potential errors throughout the design process and time to market. It is necessary for them to remain competitive.
During prototyping and production of the product as well as throughout the design and development phases, simulation allows the acquisition of the knowledge necessary to improve the product.
Through the use of numerical methods, computational fluid dynamics can simulate 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 allows the computational study of fluid mechanics is known as computational fluid dynamics. The Navier Stokes equations, which mathematically define fluid mechanics via its main variables of pressure, temperature, density, velocity, and viscosity, can 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 extension forces produced by causes external to the region under consideration. These causes are gravity; actions due to electric and/or magnetic fields; non-inertial forces.
Since these forces are proportional to the volume, they are expressed per unit volume. Surface forces are forces of an intensive nature and can be attributed to an interaction of the considered fluid with the rest of the considered physical system expressed through boundary surfaces.
The Navier-Stokes equations are a system of three equilibrium equations (partial differential equations) from continuum mechanics, which describe a linear viscous fluid; Stokes’ law (in the kinematic balance) and Fourier’s law (in the energy balance) are introduced there as constitutive laws of the material. The equations are named after Claude-Louis Navier and George Stokes.
Any equipment, machine or structure whose operation involves fluid interaction, whether for internal or external flows, can benefit from CFD fluid dynamics simulation. Calculate wind loads on civilian and military telecommunications antennas, rooftops, tensile structures, and radomes, accurately simulating wind tunnel testing.
- To increase the efficiency and uniformity of flow and heat transfer, optimize the geometry of a machine’s internal ducts.
- Calculate heat transfer in fluids and solids, for example for cooling electrical or electronic equipment.
CFD studies allow the simulation of situations such as tsunamis, weather events and environmental impacts in addition to traditional industrial uses. Meteorological centers use supercomputers because this type of study can require significant computing capacity.
CFD for quantum
It is necessary to achieve a certain degree of abstraction that facilitates the writing of algorithms for quantum computers and allows 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 top layer can be hardware-agnostic and operate in a hybrid environment, according to Classiq. The company will provide and generate hardware-independent optimized quantum circuits, allowing them to be used on various quantum computing platforms to be developed in the future.
All of this will also enable Rolls-Royce to achieve zero carbon dioxide emissions thanks to ongoing minor but crucial technological advancements at all levels.
Quantum machines will be reliable enough to perform in-depth analyzes in a few years. The objective of the algorithms is to increase the adaptability of the material to all industrial applications that require it.
A modern processor is useless without an operating system and supporting software tools in today’s computing world. The same is true in a quantum computer. As important as hardware is, software is also crucial in fueling a quantum revolution.
The complexity of writing quantum software has another unfortunate side effect: it’s hard to find experts in quantum programming, because it’s different from classical programming. Experts in quantum programming must know both software engineering and quantum physics.
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