An Amplitude-Based Implementation of the Unit Step Function on a Quantum Computer
arXiv:2206.03053 · doi:10.1103/PhysRevA.107.022606
Abstract
Modelling non-linear activation functions on quantum computers is vital for quantum neurons employed in fully quantum neural networks, however, remains a challenging task. We introduce an amplitude-based implementation for approximating non-linearity in the form of the unit step function on a quantum computer. Our approach expands upon repeat-until-success protocols, suggesting a modification that requires a single measurement only. We describe two distinct circuit types which receive their input either directly from a classical computer, or as a quantum state when embedded in a more advanced quantum algorithm. All quantum circuits are theoretically evaluated using numerical simulation and executed on Noisy Intermediate-Scale Quantum hardware. We demonstrate that reliable experimental data with high precision can be obtained from our quantum circuits involving up to 8 qubits, and up to 25 CX-gate applications, enabled by state-of-the-art hardware-optimization techniques and measurement error mitigation.
arXiv comment11 pages, 9 figures, 1 table
References in corpus (27)
- Quantum Computing in the NISQ era and beyond
- Quantum Machine Learning
- Elementary gates for quantum computation
- Variational Quantum Algorithms
- The theory of variational hybrid quantum-classical algorithms
- The power of quantum neural networks
- Synthesis of Quantum Logic Circuits
- The quest for a Quantum Neural Network
- Quantum machine learning: a classical perspective
- Quantum Circuits for General Multiqubit Gates
- Layerwise learning for quantum neural networks
- Quantum Risk Analysis
- Quantum generalisation of feedforward neural networks
- Scalable mitigation of measurement errors on quantum computers
- An Artificial Neuron Implemented on an Actual Quantum Processor
- Quantum computational finance: Monte Carlo pricing of financial derivatives
- Option Pricing using Quantum Computers
- Efficient synthesis of universal Repeat-Until-Success circuits
- A Threshold for Quantum Advantage in Derivative Pricing
- Unitary quantum perceptron as efficient universal approximator
- Quantum implementation of an artificial feed-forward neural network
- Efficient State Preparation for Quantum Amplitude Estimation
- Nonlinear Quantum Neuron: A Fundamental Building Block for Quantum Neural Networks
- Floating Point Representations in Quantum Circuit Synthesis
- Repeat-Until-Success circuits with fixed-point oblivious amplitude amplification
- A Quantum Hopfield Associative Memory Implemented on an Actual Quantum Processor
- A Universal Quantum Circuit Design for Periodical Functions