Numerical solvers for Partial Differential Equations (PDEs) are of great interest in various domains, e.g., in aerodynamics or for transport equations in lectrochemistry [1], and the need for fine-grained solutions of on-linear PDEs is growing. While subspace methods allow for a dimensionality reduction, non-linear PDEs require linearization schemes, such as the Carleman-linearization [2], resulting in linear systems of exponential dimensionality, operating on the limits of classical methods. Motivated by Krylov-subspace methods [3], which find approximate solutions in iteratively growing subspaces, the aim of this talk is to investigate the potential of two existing methods from quantum computing to compose a NISQ-era hybrid quantum-classical algorithm. Firstly, non-linear quantum computing (QNPUs) [4,5] and secondly Quantum Subspace Expansion (QSE) [6], both promising tools towards more scalable computations. The use of QNPUs enables linearization in a tensor-product-subspace using ancilla qubits, while offering efficient gate-based implementations. On the other hand, QSE measures high-dimensional overlaps on a quantum computer. The combination of these two methods yields the possibility of encapsulating the high-dimensional steps of linearization and subspace projection on a quantum device. As a result, only a lower-dimensional subspace problem remains to be solved on classical hardware.
Numerical solvers for Partial Differential Equations (PDEs) are of great interest in various domains, e.g., in aerodynamics or for transport equations in lectrochemistry [1], and the need for fine-grained solutions of on-linear PDEs is growing. While subspace methods allow for a dimensionality reduction, non-linear PDEs require linearization schemes, such as the Carleman-linearization [2], resulting in linear systems of exponential dimensionality, operating on the limits of classical methods. Motivated by Krylov-subspace methods [3], which find approximate solutions in iteratively growing subspaces, the aim of this talk is to investigate the potential of two existing methods from quantum computing to compose a NISQ-era hybrid quantum-classical algorithm. Firstly, non-linear quantum computing (QNPUs) [4,5] and secondly Quantum Subspace Expansion (QSE) [6], both promising tools towards more scalable computations. The use of QNPUs enables linearization in a tensor-product-subspace using ancilla qubits, while offering efficient gate-based implementations. On the other hand, QSE measures high-dimensional overlaps on a quantum computer. The combination of these two methods yields the possibility of encapsulating the high-dimensional steps of linearization and subspace projection on a quantum device. As a result, only a lower-dimensional subspace problem remains to be solved on classical hardware.
The material design for batteries in industry and research is greatly facilitated by computational simulations on classical computers. A central tool are numerical solvers for Partial Differential Equations (PDEs), which arise as the transport equations of ions in the continuum-model of battery cells. However, these methods reach their limits when simulating the fine-grained heterogeneous structures of porous electrodes. Quantum computing is a promising candidate to achieve an advantage in scalability through the exponential growth of representable states. On current NISQ-devices promising candidates are Variational Quantum Algorithms (VQAs) to solve nonlinear PDEs. Experimental results demonstrate the capabilities of both space-time encodings with Quantum Non-linear Processing Units (QNPUs) and spectral feature mappings with Differentiable Quantum Circuits (DQCs).
Quantum computing has the potential to revolutionize computational problem-solving by leveraging the quantum mechanical phenomena such as superposition and entanglement, enabling fundamentally different computational strategies compared to classical computing. Classical methods for solving complex and high dimensional partial differential equations (PDEs), crucial for modeling diverse physical phenomena, suffer from an exponential growth in computational complexity known as the curse of dimensionality. This can limit their effectiveness for certain high-resolution simulations and real-time computational applications. In contrast, quantum computing inherently exploits superposition and entanglement to efficiently represent and simultaneously manipulate large-scale discretized PDEs, using exponentially fewer resources compared to classical systems. There are currently many quantum techniques available for solving partial differential equations (PDEs), which are mainly based on variational quantum circuits. However, the existing quantum PDE solvers, particularly those based on variational quantum eigensolver (VQE) techniques, suffer from several limitations. These include low accuracy, high execution times, and low scalability on quantum simulators as well as on noisy intermediate-scale quantum (NISQ) devices, especially for multidimensional PDEs. In this work, we propose an efficient and scalable algorithm for solving multidimensional PDEs. We present two variants of our algorithm: the first leverages finite-difference method (FDM), classical-to-quantum (C2Q) encoding, and numerical instantiation, while the second employs FDM, C2Q, and column-by-column decomposition (CCD). Both variants are designed to enhance accuracy and scalability while reducing execution times. We have validated and evaluated our proposed concepts using a number of case studies including multidimensional Poisson equation, multidimensional heat equation, Black Scholes equation for financial option pricing, and Navier-Stokes equation for computational fluid dynamics (CFD), achieving promising results. Our results demonstrate higher accuracy, higher scalability, and faster execution times compared to VQE-based solvers on noise-free and noisy quantum simulators from IBM. Additionally, we validated our approach on hardware emulators and actual quantum hardware, employing noise mitigation techniques. This work establishes a practical and effective approach for solving PDEs using quantum computing for engineering and scientific applications.
Quantum computing has the potential to transform computational problem-solving by leveraging quantum mechanical principles of superposition and entanglement. This capability is particularly important for the numerical solution of complex and/or multidimensional partial differential equations (PDEs). There are currently many quantum techniques available for solving PDEs, which are mainly based on variational quantum algorithms (VQAs). However, the existing quantum PDE solvers, particularly those based on VQAs suffer from limitations such as low accuracy, high execution times, and low scalability on both simulators as well as on real quantum devices. In this work, we propose an efficient and scalable algorithm for solving multidimensional PDEs. We present two variants of our algorithm: the first leverages finite-difference method (FDM), classical-to-quantum (C2Q) encoding, and numerical instantiation, while the second employs FDM, C2Q, and column-by-column decomposition (CCD). Both variants are designed to enhance accuracy and scalability while reducing execution times. We have validated our proposed algorithm by solving several practically useful PDEs such as Poisson, heat, Black-Scholes, and Navier-Stokes equations. Our results demonstrate higher accuracy, higher scalability, and faster execution times compared to VQA-based solvers on noise-free and noisy quantum simulators from IBM and achieved promising results on real quantum hardware.
To approximate solutions of complex nonlinear partial differential equations remains a computational challenge, especially for sets of equations relevant in industry, such as Euler or Navier-Stokes equations. Even the most sophisticated computational fluid dynamic algorithms coupled with powerful supercomputers can not find approximate solutions for several design challenges in both adequate time and scale-resolving accuracy. One difficulty arises from solving high dimensional, strongly nonlinear partial differential equations, such as the Navier-Stokes equations, which capture the underlying physics. For nearly all classical algorithms, methods closely related to Newton's method are used to approximate a solution to the problem. Approximately solving the large-scale linear systems of equations occurring in this iterative scheme is generally a main contributor to the total computational complexity. In this paper a new quantum linear system solver supporting Newton's classical method to solve nonlinear partial differential equations is introduced. We present a new variant of the HHL algorithm, requiring less apriori information regarding the eigenvalues of the corresponding matrix. We apply this quantum linear system solver in a hybrid quantum-classical fashion to solve nonlinear partial differential equations. Moreover, a resource estimation for advanced use-cases of practical relevance is provided. Our results demonstrate how quantum computation may improve existing classical methodologies for solving nonlinear partial differential equations. This approach provides another promising application of quantum computers and presents a possible way forward for handling nonlinearities on inherently linear quantum systems.
Quantum computing has the potential to solve certain compute-intensive problems faster than classical computing by leveraging the quantum mechanical properties of superposition and entanglement. This capability can be particularly useful for solving Partial Differential Equations (PDEs), which are challenging to solve even for High-Performance Computing (HPC) systems, especially for multidimensional PDEs. This led researchers to investigate the usage of Quantum-Centric High-Performance Computing (QC-HPC) to solve multidimensional PDEs for various applications. However, the current quantum computing-based PDE-solvers, especially those based on Variational Quantum Algorithms (VQAs) suffer from limitations, such as low accuracy, long execution times, and limited scalability. In this work, we propose an innovative algorithm to solve multidimensional PDEs with two variants. The first variant uses Finite Difference Method (FDM), Classical-to-Quantum (C2Q) encoding, and numerical instantiation, whereas the second variant utilizes FDM, C2Q encoding, and Column-by-Column Decomposition (CCD). We evaluated the proposed algorithm using the Poisson equation as a case study and validated it through experiments conducted on noise-free and noisy simulators, as well as hardware emulators and real quantum hardware from IBM. Our results show higher accuracy, improved scalability, and faster execution times in comparison to variational-based PDE-solvers, demonstrating the advantage of our approach for solving multidimensional PDEs.
Structural mechanics is commonly modeled by (systems of) partial differential equations (PDEs). Except for very simple cases where analytical solutions exist, the use of numerical methods is required to find approximate solutions. However, for many problems of practical interest, the computational cost of classical numerical solvers running on classical, that is, silicon-based computer hardware, becomes prohibitive. Quantum computing, though still in its infancy, holds the promise of enabling a new generation of algorithms that can execute the most cost-demanding parts of PDE solvers up to exponentially faster than classical methods, at least theoretically. Also, increasing research and availability of quantum computing hardware spurs the hope of scientists and engineers to start using quantum computers for solving PDE problems much faster than classically possible. This work reviews the contributions that deal with the application of quantum algorithms to solve PDEs in structural mechanics. The aim is not only to discuss the theoretical possibility and extent of advantage for a given PDE, boundary conditions and input/output to the solver, but also to examine the hardware requirements of the methods proposed in literature.