Variational quantum state discriminator for supervised machine learning
arXiv:2303.03588 · doi:10.1088/2058-9565/ad0a05
Abstract
Quantum state discrimination (QSD) is a fundamental task in quantum information processing with numerous applications. We present a variational quantum algorithm that performs the minimum-error QSD, called the variational quantum state discriminator (VQSD). The VQSD uses a parameterized quantum circuit that is trained by minimizing a cost function derived from the QSD, and finds the optimal positive-operator valued measure (POVM) for distinguishing target quantum states. The VQSD is capable of discriminating even unknown states, eliminating the need for expensive quantum state tomography. Our numerical simulations and comparisons with semidefinite programming demonstrate the effectiveness of the VQSD in finding optimal POVMs for minimum-error QSD of both pure and mixed states. In addition, the VQSD can be utilized as a supervised machine learning algorithm for multi-class classification. The area under the receiver operating characteristic curve obtained in numerical simulations with the Iris flower dataset ranges from 0.97 to 1 with an average of 0.985, demonstrating excellent performance of the VQSD classifier.
arXiv comment12 pages, 22 figures
References in corpus (47)
- Quantum Computing in the NISQ era and beyond
- Variational Quantum Algorithms
- Supervised learning with quantum enhanced feature spaces
- Noisy intermediate-scale quantum (NISQ) algorithms
- Quantum Coherence as a Resource
- Quantum Convolutional Neural Networks
- Quantum Resource Theories
- Quantum random access memory
- Efficient Learning for Deep Quantum Neural Networks
- Challenges and Opportunities in Quantum Machine Learning
- Synthesis of Quantum Logic Circuits
- Distance measures to compare real and ideal quantum processes
- Quantum generative adversarial learning
- Quantum generative adversarial networks
- Quantum autoencoders for efficient compression of quantum data
- Probabilistic cloning and identification of linearly independent quantum states
- Quantum steering: a review with focus on semidefinite programming
- Optimal Quantum Circuits for General Two-Qubit Gates
- Quantum State Discrimination
- Quantum Circuits for General Multiqubit Gates
- Quantum convolutional neural network for classical data classification
- Distinguishing separable and entangled states
- Robust data encodings for quantum classifiers
- Quantum state discrimination and its applications
- Minimal Universal Two-qubit Quantum Circuits
- A universal quantum circuit for two-qubit transformations with three CNOT gates
- Experimental Quantum Generative Adversarial Networks for Image Generation
- Quantum Circuits for Isometries
- A divide-and-conquer algorithm for quantum state preparation
- Trainability of Dissipative Perceptron-Based Quantum Neural Networks
- Quantum circuits with uniformly controlled one-qubit gates
- Approximation by Quantum Circuits
- Quantum autoencoders to denoise quantum data
- Minimum-error discrimination between three mirror-symmetric states
- Universal discriminative quantum neural networks
- Binary search trees for generalized measurement
- Analysis and synthesis of feature map for kernel-based quantum classifier
- A Rudimentary Quantum Compiler
- Circuit-based quantum random access memory for classical data with continuous amplitudes
- Configurable sublinear circuits for quantum state preparation
- Estimating distinguishability measures on quantum computers
- Quantum Circuits for Quantum Channels
- Unsupervised classification of quantum data
- Quantum State Discrimination Using Noisy Quantum Neural Networks
- Implementation of a general single-qubit positive operator-valued measure on a circuit-based quantum computer
- Compact quantum kernel-based binary classifier
- Quantum State Discrimination for Supervised Classification