Quantum algorithmic differentiation

Abstract

In this work we present an algorithm to perform algorithmic differentiation in the context of quantum computing. We present two versions of the algorithm, one which is fully quantum and one which employees a classical step (hybrid approach). Since the implementation of elementary functions is already possible on quantum computers, the scheme that we propose can be easily applied. Moreover, since some steps (such as the CNOT operator) can (or will be) faster on a quantum computer than on a classical one, our procedure may ultimately demonstrate that quantum algorithmic differentiation has an advantage relative to its classical counterpart.

arXiv comment15 pages, 5 figures

References in corpus (13)

• Quantum Machine Learning
• Evaluating analytic gradients on quantum hardware
• Quantum algorithms for supervised and unsupervised machine learning
• Quantum Computation by Adiabatic Evolution
• Quantum computational finance: Monte Carlo pricing of financial derivatives
• Efficient Networks for Quantum Factoring
• Fast quantum algorithm for numerical gradient estimation
• Calculating energy derivatives for quantum chemistry on a quantum computer
• Towards Pricing Financial Derivatives with an IBM Quantum Computer
• Advances in quantum machine learning
• Quantum Algorithms for Scientific Computing and Approximate Optimization
• Hybrid Quantum/Classical Derivative Theory: Analytical Gradients and Excited-State Dynamics for the Multistate Contracted Variational Quantum Eigensolver
• Adjoints and Automatic (Algorithmic) Differentiation in Computational Finance