Extended parameter shift rules with minimal derivative variance for parameterized quantum circuits
arXiv:2508.08802 · doi:10.1103/f57b-q28w
Abstract
Parameter shift rules (PSRs) are useful methods for computing arbitrary-order derivatives of the cost function in parameterized quantum circuits. The basic idea of PSRs is to evaluate the cost function at different parameter shifts, then use specific coefficients to combine them linearly to obtain the exact derivatives. In this work, we propose an extended parameter shift rule (EPSR) which generalizes a broad range of existing PSRs and has the following two advantages. First, EPSR offers an infinite number of possible parameter shifts, allowing the selection of the optimal parameter shifts to minimize the final derivative variance and thereby obtaining the more accurate derivative estimates with limited quantum resources. Second, EPSR extends the scope of the PSRs in the sense that EPSR can handle arbitrary Hermitian operator in gate in the parameterized quantum circuits, while existing PSRs are valid only for simple Hermitian generators such as simple Pauli words. Additionally, we show that the widely used ``general PSR'', introduced by Wierichs et al. (2022), is a special case of our EPSR, and we prove that it yields globally optimal shifts for minimizing the derivative variance under the weighted-shot scheme. Finally, through numerical simulations, we demonstrate the effectiveness of EPSR and show that the usage of the optimal parameter shifts indeed leads to more accurate derivative estimates.
33 pages, 6 figures
References in corpus (34)
- Quantum Machine Learning
- A variational eigenvalue solver on a quantum processor
- Variational Quantum Algorithms
- Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
- Supervised learning with quantum enhanced feature spaces
- Quantum machine learning in feature Hilbert spaces
- Quantum Circuit Learning
- Parameterized quantum circuits as machine learning models
- Evaluating analytic gradients on quantum hardware
- An adaptive variational algorithm for exact molecular simulations on a quantum computer
- Circuit-centric quantum classifiers
- The effect of data encoding on the expressive power of variational quantum machine learning models
- Challenges and Opportunities in Quantum Machine Learning
- Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implementation on Near-Term Devices
- Quantum optimization using variational algorithms on near-term quantum devices
- Data re-uploading for a universal quantum classifier
- Variational ansatz-based quantum simulation of imaginary time evolution
- Quantum Natural Gradient
- Theory of variational quantum simulation
- Continuous-variable quantum neural networks
- A Review on Quantum Approximate Optimization Algorithm and its Variants
- Hybrid Quantum-Classical Approach to Quantum Optimal Control
- Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
- Variational quantum simulation of general processes
- Exploring entanglement and optimization within the Hamiltonian Variational Ansatz
- Stochastic gradient descent for hybrid quantum-classical optimization
- Structure optimization for parameterized quantum circuits
- Sequential minimal optimization for quantum-classical hybrid algorithms
- Estimating the gradient and higher-order derivatives on quantum hardware
- Fourier expansion in variational quantum algorithms
- Schrödinger-Heisenberg Variational Quantum Algorithms
- Variational approach to photonic quantum circuits via the parameter shift rule
- Random coordinate descent: a simple alternative for optimizing parameterized quantum circuits
- Fast gradient-free optimization of excitations in variational quantum eigensolvers