/angle-sequence

angle-sequence

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Angle Sequence: Finding Angles for Quantum Signal Processing

Introduction

Quantum signal processing is a framework for quantum algorithms including Hamiltonian simulation, quantum linear system solving, amplitude amplification, etc.

Quantum signal processing performs spectral transformation of any unitary $U$, given access to an ancilla qubit, a controlled version of $U$ and single-qubit rotations on the ancilla qubit. It first truncates an arbitrary spectral transformation function into a Laurent polynomial, then finds a way to decompose the Laurent polynomial into a sequence of products of controlled-$U$ and single qubit rotations on the ancilla. Such routines achieve optimal gate complexity for many of the quantum algorithmic tasks mentioned above.

Our software package provides a lightweight solution for classically solving for the single-qubit rotation angles given the Laurent polynomial, a task called angle sequence finding or angle finding. Our package only depends on numpy and scipy and works under machine precision. Please see below for a chart giving the performance of our algorithm for the task of Hamiltonian simulation:

Please see the arXiv manuscript for more details.

Code Structure and Usage

  • angle_sequence.py is the main module of the algorithm.
  • LPoly.py defines two classes LPoly and LAlg, representing Laurent polynomials and Low algebra elements respectively.
  • completion.py describes the completion algorithm: Given a Laurent polynomial element $F(\tilde{w})$, find its counterpart $G(\tilde{w})$ such that $F(\tilde{w})+G(\tilde{w})*iX$ is a unitary element.
  • decomposition.py describes the halving algorithm: Given a unitary parity Low algebra element $V(\tilde{w})$, decompose it as a unique product of degree-0 rotations $\exp{i\theta X}$ and degree-1 monomials $w$.
  • ham_sim.py shows an example of how the angle sequence for Hamiltonian simulation can be found.

To find the angle sequence corresponding to a real Laurent polynomial $A(\tilde{w}) = \sum_{i=-n}^n a_i\tilde{w}^i$, simply run:

from angle_sequence import angle_sequence
ang_seq = angle_sequence([a_{-n}, a_{-n+2}, ..., a_n])
print(ang_seq)

Publication