Benchmarking Simulated and Physical Quantum Processing Units Using Quantum and Hybrid Algorithms

Self Cite

Image classification, a pivotal task in multiple industries, faces computational challenges due to the burgeoning volume of visual data. This research addresses these challenges by introducing two Quantum Machine Learning models that leverage the principles of quantum mechanics for effective computations. Our first model, a Hybrid Quantum Neural Network with parallel quantum circuits, enables the execution of computations even in the Noisy Intermediate-Scale Quantum era, where circuits with a large number of qubits are currently infeasible. This model demonstrated a record-breaking classification accuracy of 99.21% on the full MNIST dataset, surpassing the performance of known quantum-classical models, while having eight times fewer parameters than its classical counterpart. Also, the results of testing this hybrid model on a Medical MNIST (classification accuracy over 99%), and on CIFAR-10 (classification accuracy over 82%), can serve as evidence of the generalizability of the model and highlights the efficiency of quantum layers in distinguishing common features of input data. Our second model introduces a Hybrid Quantum Neural Network with a Quanvolutional layer, reducing image resolution via a convolution process. The model matches the performance of its classical counterpart, having four times fewer trainable parameters, and outperforms a classical model with equal weight parameters. These models represent advancements in quantum machine learning research and illuminate the path towards more accurate image classification systems.

Section: Training and Resultsmentioning

confidence: 99%

Quantum machine learning for image classification

Senokosov,

Sedykh,

Sagingalieva

et al. 2024

Self Cite

“…The training process of the hybrid PINN does not differ from that of the classical PINN except in the following ways. Firstly, all calculations are done on the classical simulator of quantum hardware, the QMware server [63], which has recently been shown to be quite good for running hybrid algorithms [64].…”

Section: Training Hqpinnmentioning

confidence: 99%

“…However, with each additional qubit, the runtime of the quantum circuit execution will approximately double. That means, according to the benchmarks [64], the runtime will become significant beyond 20 qubits circuits. Nonetheless, with the development of quantum computers, the scaling is expected to be more favorable as the runtime on a quantum chip would stay constant in case the circuit depth is fixed.…”

Section: Training Hqpinnmentioning

confidence: 99%

Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes

Sedykh,

Podapaka,

Sagingalieva

et al. 2024

Finding the distribution of the velocities and pressures of a fluid by solving the Navier-Stokes equations is a principal task in the chemical, energy, and pharmaceutical industries, as well as in mechanical engineering and the design of pipeline systems. With existing solvers, such as OpenFOAM and Ansys, simulations of fluid dynamics in intricate geometries are computationally expensive and require re-simulation whenever the geometric parameters or the initial and boundary conditions are altered. Physics-informed neural networks are a promising tool for simulating fluid flows in complex geometries, as they can adapt to changes in the geometry and mesh definitions, allowing for generalization across fluid parameters and transfer learning across different shapes. We present a hybrid quantum physics-informed neural network that simulates laminar fluid flows in 3D $Y$-shaped mixers. Our approach combines the expressive power of a quantum model with the flexibility of a physics-informed neural network, resulting in a 21\% higher accuracy compared to a purely classical neural network. Our findings highlight the potential of machine learning approaches, and in particular hybrid quantum physics-informed neural network, for complex shape optimization tasks in computational fluid dynamics. By improving the accuracy of fluid simulations in complex geometries, our research using hybrid quantum models contributes to the development of more efficient and reliable fluid dynamics solvers.

“…The IonQ implements a high-fidelity gate-based quantum processing unit through a process known as laser pumping trapped-ions explained in [36]. The hardware was shown to be one of the most accurate in recent benchmarking tests [37]. We specifically used the hardware introduced in [38] with a single-qubit fidelity of 0.997 and a two-qubit fidelity of 0.9725.…”

Section: Trainingmentioning

confidence: 99%

An exponentially-growing family of universal quantum circuits

Kordzanganeh

Sekatski

Fedichkin

et al. 2023

Quantum machine learning has become an area of growing interest but has certain theoretical and hardware-specific limitations. Notably, the problem of vanishing gradients, or barren plateaus, renders the training impossible for circuits with high qubit counts, imposing a limit on the number of qubits that data scientists can use for solving problems. Independently, angle-embedded supervised quantum neural networks were shown to produce truncated Fourier series with a degree directly dependent on two factors: the depth of the encoding and the number of parallel qubits the encoding applied to. The degree of the Fourier series limits the model expressivity. This work introduces two new architectures whose Fourier degrees grow exponentially: the sequential and parallel exponential quantum machine learning architectures. This is done by efficiently using the available Hilbert space when encoding, increasing the expressivity of the quantum encoding. Therefore, the exponential growth allows staying at the low-qubit limit to create highly expressive circuits avoiding barren plateaus. Practically, parallel exponential architecture was shown to outperform the existing linear architectures by reducing their final mean square error value by up to 44.7% in a one-dimensional test problem. Furthermore, the feasibility of this technique was also shown on a trapped ion quantum processing unit.