pith. sign in

arxiv: 1905.13311 · v1 · pith:EUTRVEW4new · submitted 2019-05-30 · 🪐 quant-ph

Gradients of parameterized quantum gates using the parameter-shift rule and gate decomposition

classification 🪐 quant-ph
keywords gatesparameter-shiftquantumruleapproachgradientsancillaapplying
0
0 comments X
read the original abstract

The parameter-shift rule is an approach to measuring gradients of quantum circuits with respect to their parameters, which does not require ancilla qubits or controlled operations. Here, I discuss applying this approach to a wider range of parameterize quantum gates by decomposing gates into a product of standard gates, each of which is parameter-shift rule differentiable.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Graph-Conditioned Meta-Optimizer for QAOA Parameter Generation on Multiple Problem Classes

    quant-ph 2026-04 unverdicted novelty 7.0

    A graph-conditioned meta-optimizer learns QAOA parameter trajectories from one problem class and transfers them to others, yielding better initializations than standard methods in an empirical study of 64 settings.

  2. Scaling Quantum Optimization for Unit Commitment via Pauli Correlation Encoding

    quant-ph 2026-05 unverdicted novelty 6.0

    Hybrid quantum-classical optimization for unit commitment uses Pauli-Correlation Encoding to solve multi-period schedules with up to 312 binary variables while satisfying load, ramping, and reserve constraints.

  3. Don't Get Your Kroneckers in a Twist: Gaussian Processes on High-Dimensional Incomplete Grids

    cs.LG 2026-05 unverdicted novelty 6.0

    CUTS-GPR performs numerically exact Gaussian process regression with near-linear scaling in training points N and low-order polynomial scaling in dimensions D by exploiting additive kernels on incomplete grids.

  4. Quantum Optimization for Electromagnetics: Physics-Informed QAOA for Reconfigurable Intelligent Surfaces

    cs.CE 2026-05 unverdicted novelty 6.0

    Sparse distance-penalized Ising models are required for feasible QAOA execution on NISQ devices when optimizing RIS with mutual coupling, at the cost of reduced beamforming precision compared to dense models.

  5. A unified framework for efficient quantum simulation of nonlinear spectroscopy

    quant-ph 2026-04 unverdicted novelty 6.0

    A unified quantum framework computes n-th order nonlinear spectroscopies on near-term devices by reformulating multi-time responses as weighted sums of finite-amplitude expectation values via a generalized parameter s...

  6. Variational Probe and Measurement Optimization for Structured Phase Estimation

    quant-ph 2025-07 unverdicted novelty 6.0

    Variational optimization of shallow probes and decoders for structured phase estimation in small qubit arrays approaches entanglement-enhanced precision bounds for both uniform and weighted encodings.

  7. A review of quantum machine learning and quantum-inspired applied methods to computational fluid dynamics

    quant-ph 2025-10 unverdicted novelty 2.0

    A survey of variational quantum algorithms, quantum neural networks, and tensor networks for addressing scalability challenges in computational fluid dynamics.