mat2qubit: A lightweight pythonic package for qubit encodings of vibrational, bosonic, graph coloring, routing, scheduling, and general matrix problems

Abstract

Preparing problems for execution on quantum computers can require many compilation steps. Automated compilation software is useful not only for easier and faster problem execution, but also for facilitating the comparison between different algorithmic choices. Here we describe mat2qubit, a Python package for encoding several classes of classical and quantum problems into qubit representations. It is intended for use especially on Hamiltonians and functions defined over variables (e.g. particles) with cardinality larger than 2. More specifically, mat2qubit may be used to compile bosonic, phononic/vibrational, and spin-s problems, as well as classical problems such as graph coloring, routing, scheduling, and classical linear algebra more generally. In order to facilitate numerical analyses and ease of programmability, a built-in computer algebra system (CAS) allows for fully symbolic preparation and manipulation of problems (with symbolic operators, symbolic coefficients, and symbolic particle labels) before the final compilation into qubits is performed. We expect this code to be useful in the preparation and analysis of various classes of physics, chemistry, materials, and optimization problems for execution on digital quantum computers.

Improved Resource‐Tunable Near‐Term Quantum Algorithms for Transition Probabilities, with Applications in Physics and Variational Quantum Linear Algebra

“…We consider simple models from three application areas: spin chains, vibronic transitions in molecules, and linear systems solving. We prepared and executed the numerics using primarily Scipy, [55] mat2qubit, [56,57] and OpenFermion. [58] In Sec-tion 4.1 we summarize the applications and numerical methods, and in Section 4.2 we present and discuss the results.…”

Improved resource-tunable near-term quantum algorithms for transition probabilities, with applications in physics and variational quantum linear algebra

“…We consider simple models from three application areas: spin chains, vibronic transitions in molecules, and linear systems solving. We prepared and executed the numerics using primarily Scipy [52], mat2qubit [53], and OpenFermion [54]. In Section IV A we summarize the applications and numerical methods and in Section IV B we present and discuss the results.…”