Quantum Circuit Parameters Learning with Gradient Descent Using Backpropagation
arXiv:1910.14266
Abstract
Quantum computing has the potential to outperform classical computers and is expected to play an active role in various fields. In quantum machine learning, a quantum computer has been found useful for enhanced feature representation and high dimensional state or function approximation. Quantum-Classical hybrid algorithms are proposed in recent years for this purpose under the Noisy-Intermediate Scale Quantum computer (NISQ) environment. Under this scheme, the role played by classical computer is the parameter tuning, parameter optimization, and parameter update for the quantum circuit. In this paper, we propose a gradient descent based backpropagation algorithm that can efficiently calculate the gradient in parameter optimization and update the parameter for quantum circuit learning, which outperforms the current parameter search algorithms in terms of computing speed while presents the same or even higher test accuracy.
11 pages, 9 figures
References in corpus (19)
- Quantum Computing in the NISQ era and beyond
- A variational eigenvalue solver on a quantum processor
- Supervised learning with quantum enhanced feature spaces
- The Physical Implementation of Quantum Computation
- A Quantum Approximate Optimization Algorithm
- Quantum machine learning in feature Hilbert spaces
- Characterizing Quantum Supremacy in Near-Term Devices
- An adaptive variational algorithm for exact molecular simulations on a quantum computer
- Quantum Computation of Electronic Transitions using a Variational Quantum Eigensolver
- 0.5 Petabyte Simulation of a 45-Qubit Quantum Circuit
- A flexible high-performance simulator for verifying and benchmarking quantum circuits implemented on real hardware
- 64-Qubit Quantum Circuit Simulation
- Leveraging Secondary Storage to Simulate Deep 54-qubit Sycamore Circuits
- qHiPSTER: The Quantum High Performance Software Testing Environment
- Classical Simulation of Intermediate-Size Quantum Circuits
- qTorch: The Quantum Tensor Contraction Handler
- Sublinear quantum algorithms for training linear and kernel-based classifiers
- Convolution filter embedded quantum gate autoencoder
- The sum-over-histories formulation of quantum computing