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IEEE Access

TensorFlow Quantum: Impacts of Quantum State Preparation on Quantum Machine Learning Performance

Daniel Sierra-Sosa, Michael Telahun, Adel Said Elmaghraby

Published Jan 1, 2020• 46 citations
Publisher

Abstract

Learning methodologies on quantum devices have shown that there are advantages in utilizing quantum properties. A requirement for using quantum computing in machine learning techniques is the data representation as quantum states. In Quantum Machine Learning, quantum state preparation is paramount to attain a functional pipeline in a model. One state preparation method, amplitude encoding, allows a dataset to be mapped or encoded more robustly and enhances the learning of quantum models. Albeit more densely represented, a dataset which has been prepared by amplitude encoding provides a more learnable input to a model. The two main advantages from using amplitude encoding are an increase in classification accuracy and reduced variability of learning epoch to epoch. In this paper, we compare the basic implementations of TensorFlow Quantum's Quantum Convolutional Neural Network and a hybrid quantum-classical network using angle encoding, with a third network of our design that utilizes amplitude encoding for enriched state preparation. Our results show there is a direct benefit in performing amplitude encoding before training a TensorFlow Quantum hybrid quantum-classical model. In the best case scenario, amplitude encoding made classifying the samples 8.9% more accurate.

DOI
10.1109/access.2020.3040798

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We investigate two fundamental data encoding strategies for gate-based quantum computers: amplitude encoding and angle encoding. In amplitude encoding, a normalized -dimensional classical vector is mapped into the probability amplitudes of a -qubit quantum state, whereas angle encoding uses one or more single-qubit rotation gates per feature to encode data as angles.[1, 2] We construct a toy binary classification dataset with four real-valued features and compare these encoding schemes using a variational quantum classifier (VQC) approach. Our results indicate that angle encoding often yields slightly higher classification accuracy (e.g. 82% vs. 75% in one experiment) at the cost of requiring more qubits, while amplitude encoding compresses features into fewer qubits (2 vs. 4) but demands deeper state-preparation circuits. These findings align with previous studies that report higher accuracy for rotation encodings (e.g. vs. in MNIST experiments [3]) and note that the encoding choice acts as a hyperparameter affecting VQC performance. [4]

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Learning methodologies on quantum devices have shown that there are advantages in utilizing quantum properties. A requirement for using quantum computing in machine learning techniques is the data representation as quantum states. In Quantum Machine Learning, quantum state preparation is paramount to attain a functional pipeline in a model. One state preparation method, amplitude encoding, allows a dataset to be mapped or encoded more robustly and enhances the learning of quantum models. Albeit more densely represented, a dataset which has been prepared by amplitude encoding provides a more learnable input to a model. The two main advantages from using amplitude encoding are an increase in classification accuracy and reduced variability of learning epoch to epoch. In this paper, we compare the basic implementations of TensorFlow Quantum's Quantum Convolutional Neural Network and a hybrid quantum-classical network using angle encoding, with a third network of our design that utilizes amplitude encoding for enriched state preparation. Our results show there is a direct benefit in performing amplitude encoding before training a TensorFlow Quantum hybrid quantum-classical model. In the best case scenario, amplitude encoding made classifying the samples 8.9% more accurate.
Quantum computing performs calculations by using physical phenomena and quantum mechanics principles to solve problems. This form of computation theoretically has been shown to provide speed ups to some problems of modern-day processing. With much anticipation the utilization of quantum phenomena in the field of Machine Learning has become apparent. The work here develops models from two software frameworks: TensorFlow Quantum (TFQ) and PennyLane for machine learning purposes. Both developed models utilize an information encoding technique amplitude encoding for preparation of states in a quantum learning model. This thesis explores both the capacity for amplitude encoding to provide enriched state preparation in learning methods and a deep analysis of data properties that provide insights into training data using a Variational Quantum Classifier (VQC). The advent of these new methods begs the question of how to best use these tools, we aim to give some overview explanation for the applicable state of quantum machine learning given actual device constraints. The results show there is a clear advantage for using amplitude encoding over other methods as we show using a hybrid quantum-classical neural network in TFQ. Additionally, there are several steps of preprocessing that can lead to more feature rich data when utilizing a VQC, in essence the no free lunch theorem holds true for quantum learning methods as it does in classical techniques. Information albeit encoded in a quantum form does not change the steps of preparing data but involves new ways to comprehend and appreciate these novel methods.
A central challenge in quantum machine learning is the state preparation bottleneck that describes the prohibitive computational cost of loading high-dimensional classical data into a quantum state. Although amplitude encoding can represent 2n2^n2n-dimensional data using only nnn qubits in principle, preparing arbitrary states remains computationally expensive, typically requiring variational optimization of a parameterized quantum circuit for each individual data instance. In this work, we propose a method that avoids iterative optimization by training a classical neural network to map input data directly to the continuous parameters of a fixed quantum circuit. We demonstrate the generation of quantum image states with high fidelity on data not seen during training. Since all optimization is performed once during training, the resulting model encodes new inputs in a single inference step, providing a scalable pathway for data loading in near-term quantum algorithms. We validate our method on the MNIST and Fashion-MNIST datasets, achieving fidelities up to 0.992 on unseen images and reducing the per-data-instance runtime by more than 5000-fold.
Quantum convolutional neural networks (QCNNs) offer a promising architecture for near-term quantum machine learning by combining hierarchical feature extraction with modest parameter growth. However, any QCNN operating on classical data must rely on an encoding scheme to embed inputs into quantum states, and this choice can dominate both performance and resource requirements. This work presents an implementation-level comparison of three representative encodings -- Angle, Amplitude, and a Hybrid phase/angle scheme -- for QCNNs under depolarizing noise. We develop a fully differentiable PyTorch--Qiskit pipeline with a custom autograd bridge, batched parameter-shift gradients, and shot scheduling, and use it to train QCNNs on downsampled binary variants of MNIST and Fashion-MNIST at 4×44\times 44×4 and 8×88\times 88×8 resolutions. Our experiments reveal regime-dependent trade-offs. On aggressively downsampled 4×44\times 44×4 inputs, Angle encoding attains higher accuracy and remains comparatively robust as noise increases, while the Hybrid encoder trails and exhibits non-monotonic trends. At 8×88\times 88×8, the Hybrid scheme can overtake Angle under moderate noise, suggesting that mixed phase/angle encoders benefit from additional feature bandwidth. Amplitude-encoded QCNNs are sparsely represented in the downsampled grids but achieve strong performance in lightweight and full-resolution configurations, where training dynamics closely resemble classical convergence. Taken together, these results provide practical guidance for choosing QCNN encoders under joint constraints of resolution, noise strength, and simulation budget.
ABSTRACT Recent advancements in Quantum Computing and Machine Learning have increased attention to Quantum Machine Learning (QML), which aims to develop machine learning models by exploiting the quantum computing paradigm. One of the widely used models in this area is the Variational Quantum Circuit (VQC), a hybrid model where the quantum circuit handles data inference while classical optimization adjusts the parameters of the circuit. The quantum circuit consists of an encoding layer, which loads data into the circuit, and a template circuit, known as the ansatz, responsible for processing the data. This work involves performing an analysis by considering both Amplitude‐ and Angle‐encoding models, and examining how the type of rotational gate applied affects the classification performance of the model. This comparison is carried out by training the different models on two datasets, Wine and Diabetes, and evaluating their performance. The study demonstrates that, under identical model topologies, the difference in accuracy between the best and worst models ranges from 10% to 30%, with differences reaching up to 41%. Moreover, the results highlight how the choice of rotational gates used in encoding can significantly impact the model's classification performance. The findings confirm that the embedding represents a hyperparameter for VQC models.
Quantum machine learning is one of the most exciting new avenues in the world of artificial intelligence, especially because of the enormous computational power of quantum computers and the promise of the development of near error-free quantum computers in the not-so-distant future. For quantum algorithms to be used in real-life applications, quantum computers must be able to work with classical data. One of the key steps in quantum algorithms dealing with classical data is the encoding of classical data points to quantum states, which can then be processed by quantum gates. It is known that the type of encoding technique that works best for a particular network is dependent on the dataset being used. In this paper, a new parallel structure is proposed utilizing two encoding techniques, namely amplitude encoding and angle encoding, for effective classical data classification via quantum neural network. The paper further proposes a maximally expressible and entangled ansatz used to design a simple Quantum Convolutional Neural Network (QCNN) with only 32 parameters, that is used in the latter stages of the network and is kept the same across all encoding instances so that a comparison between the different encoding methods is possible. Extensive experimentation is carried out on two publicly available image datasets, namely MNIST and Fashion MNIST. The results show that the proposed method achieves better results than any of the encoding techniques deployed alone for binary classification.
We investigate two fundamental data encoding strategies for gate-based quantum computers: amplitude encoding and angle encoding. In amplitude encoding, a normalized -dimensional classical vector is mapped into the probability amplitudes of a -qubit quantum state, whereas angle encoding uses one or more single-qubit rotation gates per feature to encode data as angles.[1, 2] We construct a toy binary classification dataset with four real-valued features and compare these encoding schemes using a variational quantum classifier (VQC) approach. Our results indicate that angle encoding often yields slightly higher classification accuracy (e.g. 82% vs. 75% in one experiment) at the cost of requiring more qubits, while amplitude encoding compresses features into fewer qubits (2 vs. 4) but demands deeper state-preparation circuits. These findings align with previous studies that report higher accuracy for rotation encodings (e.g. vs. in MNIST experiments [3]) and note that the encoding choice acts as a hyperparameter affecting VQC performance. [4]