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Radioelectronics Nanosystems Information Technologies

Capabilities of an adiabatic quantum computer for pattern recognition

Evgenii V. Burlakov

Published Apr 15, 2025• 1 citation
Publisher

Abstract

This study compares three discrete-variable models: the fully connected Ising model, the quadratic unconstrained binary optimization model, and the Hopfield neural network. The analysis evaluates their potential interchangeability in solving optimization and pattern recognition tasks, as well as their applicability to quantum computing. A novel method for pattern storage and recognition is proposed, based on the dynamics of forward and reverse quantum annealing. This method has been successfully tested on a D-Wave adiabatic quantum computer. An optimal annealing parameter was empirically determined, maximizing recognition accuracy while minimizing annealing time. The study also highlights limitations related to the size and noise levels of the patterns, imposed by the quantum processor's architectural constraints. Finally, conclusions are drawn regarding the effectiveness implications of the proposed approach.

DOI
10.17725/j.rensit.2025.17.201

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This study compares three discrete-variable models: the fully connected Ising model, the quadratic unconstrained binary optimization model, and the Hopfield neural network. The analysis evaluates their potential interchangeability in solving optimization and pattern recognition tasks, as well as their applicability to quantum computing. A novel method for pattern storage and recognition is proposed, based on the dynamics of forward and reverse quantum annealing. This method has been successfully tested on a D-Wave adiabatic quantum computer. An optimal annealing parameter was empirically determined, maximizing recognition accuracy while minimizing annealing time. The study also highlights limitations related to the size and noise levels of the patterns, imposed by the quantum processor's architectural constraints. Finally, conclusions are drawn regarding the effectiveness implications of the proposed approach.
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We simulate Quantum Annealing on a variational manifold defined by a parametric family of wavefunctions represented by a Restricted Boltzmann Machine architecture. By iteratively lowering the transverse field and optimizing the neural network parameters, we prove the effectiveness of our methods for a challenging class of real-world Quadratic Unconstrained Binary Optimization (QUBO) instances, namely the Job Shop Scheduling Problem. Our methods outperform a straightforward optimization of the same problem without simulated quantum fluctuations. Despite the QUBO model being equivalent to a frustrated Ising spin glass, we obtain exact solutions up to 50-100 binary variables and empirically outperform results obtained with D-Wave quantum annealers.
The storage capacity of the conventional Hopfield network is the 0.14 times of the number of neurons(P=0.14N). Because of the huge difficulty in recognizing a large number of images or patterns, researchers are looking for new methods at all times. Quantum neural network (QNN) is a young and outlying science built upon the combination of classical neural network and quantum computing. A Quantum Hopfield neural network (QHNN) whose elements of the storage matrix are distributed in a probability way on the base of quantum linear superposition is presented for speeding up the images recognition and increasing the number of the images recognition. Contrasting to the conventional Hopfield neural network, the storage capacity of the QHNN is increased by a factor of 2N, where N is the number of neurons. Besides, the case analysis and simulation tests have been carried out for the recognition of images in this paper. The result indicates that QHNN can recognize the images or patterns effectively and its working process accords with quantum evolvement process.
Quantum annealing is a promising paradigm for building practical quantum computers. Compared to other approaches, quantum annealing technology has been scaled up to a larger number of qubits. On the other hand, deep learning has been profoundly successful in pushing the boundaries of AI. It is thus natural to investigate potentially game changing technologies such as quantum annealers to augment the capabilities of deep learning. In this work, we explore binary neural networks, which are lightweight yet powerful models typically intended for resource constrained devices. Departing from current training regimes for binary networks that smooth/approximate the activation functions to make the network differentiable, we devise a quadratic unconstrained binary optimization formulation for the training problem. While the problem is intractable, i.e., the cost to estimate the binary weights scales exponentially with network size, we show how the problem can be optimized directly on a quantum annealer, thereby opening up to the potential gains of quantum computing. We experimentally validated our formulation via simulation and testing on an actual quantum annealer (D-Wave Advantage), the latter to the extent allowable by the capacity of current technology.
Abstract This study explores the application of adiabatic quantum computing to portfolio optimization, a critical problem in finance. By formulating the problem as a Quadratic Unconstrained Binary Optimization (QUBO) model, the study integrates constraints such as expected return, transaction cost, and Environmental, Social, and Governance (ESG) scores. Simulations using D-Wave’s quantum annealer demonstrate the feasibility of optimizing portfolios comprising hundreds of assets with reduced computational time compared to classical methods. While current quantum devices face limitations in precision, this work lays the foundation for leveraging quantum technologies in financial optimization.
Abstract The reconstruction of charged particles will be a key computing challenge for the high-luminosity Large Hadron Collider (HL-LHC) where increased data rates lead to a large increase in running time for current pattern recognition algorithms. An alternative approach explored here expresses pattern recognition as a quadratic unconstrained binary optimization (QUBO), which allows algorithms to be run on classical and quantum annealers. While the overall timing of the proposed approach and its scaling has still to be measured and studied, we demonstrate that, in terms of efficiency and purity, the same physics performance of the LHC tracking algorithms can be achieved. More research will be needed to achieve comparable performance in HL-LHC conditions, as increasing track density decreases the purity of the QUBO track segment classifier.