Implementing QSVT on Hardware

Implementing QSVT on Hardware

This PennyLane how-to demonstrates implementing the quantum singular value transformation (QSVT) in a hardware-compatible way, taking QSVT application research from simulators toward real devices. The goal is to apply a polynomial transformation to a Hamiltonian, and the guide breaks the task into its two ingredients: the projection angles that define the polynomial and a block encoding of the Hamiltonian. It shows how to compute the required angles for an example odd polynomial using PennyLane's built-in poly_to_angles function, and how to convert angle conventions from quantum signal processing to QSVT with transform_angles, noting that external tools such as pyqsp could supply the angles as well. For the block encoding, it adopts the linear-combination-of-unitaries approach via the PrepSelPrep operation, encoding a Hamiltonian expressed as a weighted sum of Pauli words, and then assembles the QSVT template from PrepSelPrep together with projector-controlled phase operations. The resulting circuit is checked against the directly computed polynomial of the Hamiltonian matrix. By emphasizing hardware-friendly primitives and explicit control-wire bookkeeping, the demo bridges the gap between the theory of QSVT and its execution on quantum processors, a valuable resource for practitioners moving into the intermediate-scale quantum era.

Run it

pip install -r requirements.txt
python demo.py

Source and license

Imported from demonstrations_v2/tutorial_qsvt_hardware/demo.py in PennyLaneAI/demos at c52c0abeb5122218aa96b38eea848864cce7323f, under the Apache License 2.0. Original authors: Xanadu and the PennyLane community. The upstream LICENSE is included alongside this example.