Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

A 1-Qubit Quantum Neural Network for NOT, AND, OR, NAND, NOR, XOR, and XNOR

Mirza Nur Hidayat, Muhammad Faruq Nuruddinsyah

Published Nov 5, 2025• 0 citations
Publisher

Abstract

In this paper, we implement a single-qubit quantum neural network to realise all Boolean logic functions. The circuit consists of four quantum rotation gates: two at the beginning as a quantum feature map of the dense angle encoding type, and the last two gates as the ansatz. The optimal parameters in the ansatz are determined using an iterative method, yielding expectation values that match the desired values and enabling Boolean output predictions. Our results demonstrate that the single-qubit parametric circuit, constructed with optimal parameter values obtained from iterative optimisation, is capable of implementing all Boolean logic functions.

DOI
10.36227/techrxiv.176231694.45262109/v1

Related papers

A Single-Gate, Single-Qubit Quantum Neural Network for All Boolean Logic Functions and the Iris Dataset

Mirza Nur Hidayat, Muhammad Faruq Nuruddinsyah

In this study, we demonstrate a single-qubit quantum neural network employing one rotation gate to implement all Boolean logic functions and to classify the Iris dataset. This rotation gate serves simultaneously as both a quantum feature map and an ansatz. The ansatz parameters are optimised using gradient descent, with the mean squared error as the loss function. Results confirm that the circuit successfully implements all Boolean logic functions and classifies the Iris dataset.

•Nov 13, 2025•0 citations

A 1-Gate, 1-Qubit Quantum Neural Network

Nur Hidayat, Mirza, Faruq Nuruddinsyah, Muhammad

In this study, we construct a single-qubit quantum neural network using only one rotation gate to implement all Boolean logic functions and to classify the Iris dataset. This rotation gate serves simultaneously as both a quantum feature map and an ansatz. The ansatz parameters are optimised using gradient descent, with the mean squared error as the loss function. Results confirm that the circuit successfully implements all Boolean logic functions and classifies the Iris dataset.

•Nov 4, 2025•0 citations

Quantum Neural Network (QNN) based Realization of XOR gate Using Systematic Quantum Circuit Based Approach

Vishal Srivastava, Gopal

Quantum computing offers transformative potential for information processing, promising solutions to complex problems beyond the reach of classical computers. Central to this field is the development of quantum logic gates, the foundational elements of quantum circuits. This work investigates a systematic method for implementing a quantum XOR gate, a critical logic operation, using quantum neural network architectures. Quantum phenomena like superposition and entanglement offer possibilities for substantial progress in areas such as cryptography, simulations, and optimization. Initial findings using QNNs for a classical XOR classification problem show promising accuracy above 90%. Realizing the full potential of quantum computing requires continued advancements in areas like fault-tolerant qubit engineering, high-fidelity gate operations, and scalable circuit design..

International Conference on Internet of Everything, Microwave Engineering, Communication and Networks (IEMECON)•Oct 24, 2024•1 citation

Measurement circuit ansatz: Naimark versus quantum neural-network measurements

Sung Won Yun, Thi Ha Kyaw, Joonwoo Bae

In this work, we present constructions of quantum circuits to implement general measurements on quantum hardware. Firstly, we investigate a quantum circuit ansatz by following the Naimark extension with a universal set of gates, such as controlled-NOT and single-qubit gates; we call it a {\it Naimark measurement}. We present a circuit ansatz framed by the Naimark extension, leaving single-qubit gates with parameters, and apply a classical optimizer to determine their parameters to approximate a desired quantum measurement. Secondly, we relax the Naimark measurement with quantum neural-network (QNN) circuits, employing parameterized quantum circuits. We present hybrid Naimark-QNN measurements by incorporating QNN circuits into Naimark measurements. Thirdly, we also consider fully QNN measurements with shallow parameterized circuits. Then, we compare the constructed measurement circuits, Naimark, hybrid Naimark-QNN, and fully QNN measurements, for strategies of state discrimination, such as minimum-error and maximum-confidence measurements. We demonstrate that QNN circuits can efficiently and effectively achieve near-optimal quantum measurements with fewer training iterations.

quant-pharXiv:2606.07376•Quantum Science and Technology•Jun 8, 2026•0 citations

Power and Limitations of Single-Qubit Native Quantum Neural Networks

Zhan Yu, Hongshun Yao, Mujin Li +1 more

Quantum neural networks (QNNs) have emerged as a leading strategy to establish applications in machine learning, chemistry, and optimization. While the applications of QNN have been widely investigated, its theoretical foundation remains less understood. In this paper, we formulate a theoretical framework for the expressive ability of data re-uploading quantum neural networks that consist of interleaved encoding circuit blocks and trainable circuit blocks. First, we prove that single-qubit quantum neural networks can approximate any univariate function by mapping the model to a partial Fourier series. We in particular establish the exact correlations between the parameters of the trainable gates and the Fourier coefficients, resolving an open problem on the universal approximation property of QNN. Second, we discuss the limitations of single-qubit native QNNs on approximating multivariate functions by analyzing the frequency spectrum and the flexibility of Fourier coefficients. We further demonstrate the expressivity and limitations of single-qubit native QNNs via numerical experiments. We believe these results would improve our understanding of QNNs and provide a helpful guideline for designing powerful QNNs for machine learning tasks.

arXiv:2205.07848•neural information processing systems•Jan 1, 2022•20 citations

The Proposal of a Fully Quantum Neural Network and Fidelity-Driven Training Using Directional Gradients for Multi-Class Classification

Dawid Ewald

In this work, we present a training method for a Fully Quantum Neural Network (FQNN) based entirely on quantum circuits. The model processes data exclusively through quantum operations, without incorporating classical neural network layers. In the proposed architecture, the roles of classical neurons and weights are assumed, respectively, by qubits and parameterized quantum gates: input features are encoded into quantum states of qubits, while the network weights correspond to the rotation angles of quantum gates that govern the system’s state evolution. The optimization of gate parameters is performed using directional gradient estimation, where gradients are numerically approximated via finite differences, eliminating the need for analytic derivation. The training objective is defined as the quantum-state fidelity, which measures the similarity between the network’s output state and a reference state representing the correct class. Experiments were conducted using the Qiskit AerSimulator, which allows for the accurate simulation of quantum circuits on a classical computer. The proposed approach was applied to the classification of the Iris dataset. The experimental results demonstrate that the FQNN is capable of effectively learning to distinguish between classes based on input features, achieving stable test accuracy across runs. These findings confirm the feasibility of constructing fully quantum classifiers without relying on hybrid quantum—classical architectures. The FQNN architecture consists of multiple quantum layers, each incorporating parameterized rotation operations and entanglement between qubits. The number of layers is determined by the ratio of quantum parameters (weights) to the number of input features. Each layer functions analogously to a hidden layer in a classical neural network, transforming the quantum-state space into a richer feature representation through controlled quantum operations. As a result, the network is capable of dynamically modeling dependencies among input features without the use of classical activation functions.

Electronics•May 28, 2025•3 citations
In this paper, we implement a single-qubit quantum neural network to realise all Boolean logic functions. The circuit consists of four quantum rotation gates: two at the beginning as a quantum feature map of the dense angle encoding type, and the last two gates as the ansatz. The optimal parameters in the ansatz are determined using an iterative method, yielding expectation values that match the desired values and enabling Boolean output predictions. Our results demonstrate that the single-qubit parametric circuit, constructed with optimal parameter values obtained from iterative optimisation, is capable of implementing all Boolean logic functions.
Quantum computing offers transformative potential for information processing, promising solutions to complex problems beyond the reach of classical computers. Central to this field is the development of quantum logic gates, the foundational elements of quantum circuits. This work investigates a systematic method for implementing a quantum XOR gate, a critical logic operation, using quantum neural network architectures. Quantum phenomena like superposition and entanglement offer possibilities for substantial progress in areas such as cryptography, simulations, and optimization. Initial findings using QNNs for a classical XOR classification problem show promising accuracy above 90%. Realizing the full potential of quantum computing requires continued advancements in areas like fault-tolerant qubit engineering, high-fidelity gate operations, and scalable circuit design..
In this work, we present constructions of quantum circuits to implement general measurements on quantum hardware. Firstly, we investigate a quantum circuit ansatz by following the Naimark extension with a universal set of gates, such as controlled-NOT and single-qubit gates; we call it a {\it Naimark measurement}. We present a circuit ansatz framed by the Naimark extension, leaving single-qubit gates with parameters, and apply a classical optimizer to determine their parameters to approximate a desired quantum measurement. Secondly, we relax the Naimark measurement with quantum neural-network (QNN) circuits, employing parameterized quantum circuits. We present hybrid Naimark-QNN measurements by incorporating QNN circuits into Naimark measurements. Thirdly, we also consider fully QNN measurements with shallow parameterized circuits. Then, we compare the constructed measurement circuits, Naimark, hybrid Naimark-QNN, and fully QNN measurements, for strategies of state discrimination, such as minimum-error and maximum-confidence measurements. We demonstrate that QNN circuits can efficiently and effectively achieve near-optimal quantum measurements with fewer training iterations.
Quantum neural networks (QNNs) have emerged as a leading strategy to establish applications in machine learning, chemistry, and optimization. While the applications of QNN have been widely investigated, its theoretical foundation remains less understood. In this paper, we formulate a theoretical framework for the expressive ability of data re-uploading quantum neural networks that consist of interleaved encoding circuit blocks and trainable circuit blocks. First, we prove that single-qubit quantum neural networks can approximate any univariate function by mapping the model to a partial Fourier series. We in particular establish the exact correlations between the parameters of the trainable gates and the Fourier coefficients, resolving an open problem on the universal approximation property of QNN. Second, we discuss the limitations of single-qubit native QNNs on approximating multivariate functions by analyzing the frequency spectrum and the flexibility of Fourier coefficients. We further demonstrate the expressivity and limitations of single-qubit native QNNs via numerical experiments. We believe these results would improve our understanding of QNNs and provide a helpful guideline for designing powerful QNNs for machine learning tasks.
In this work, we present a training method for a Fully Quantum Neural Network (FQNN) based entirely on quantum circuits. The model processes data exclusively through quantum operations, without incorporating classical neural network layers. In the proposed architecture, the roles of classical neurons and weights are assumed, respectively, by qubits and parameterized quantum gates: input features are encoded into quantum states of qubits, while the network weights correspond to the rotation angles of quantum gates that govern the system’s state evolution. The optimization of gate parameters is performed using directional gradient estimation, where gradients are numerically approximated via finite differences, eliminating the need for analytic derivation. The training objective is defined as the quantum-state fidelity, which measures the similarity between the network’s output state and a reference state representing the correct class. Experiments were conducted using the Qiskit AerSimulator, which allows for the accurate simulation of quantum circuits on a classical computer. The proposed approach was applied to the classification of the Iris dataset. The experimental results demonstrate that the FQNN is capable of effectively learning to distinguish between classes based on input features, achieving stable test accuracy across runs. These findings confirm the feasibility of constructing fully quantum classifiers without relying on hybrid quantum—classical architectures. The FQNN architecture consists of multiple quantum layers, each incorporating parameterized rotation operations and entanglement between qubits. The number of layers is determined by the ratio of quantum parameters (weights) to the number of input features. Each layer functions analogously to a hidden layer in a classical neural network, transforming the quantum-state space into a richer feature representation through controlled quantum operations. As a result, the network is capable of dynamically modeling dependencies among input features without the use of classical activation functions.