qopt

Welcome to qopt. In this documentation you will find everything you need to know about qopt to simulate qubits and apply optimal control techniques. You can find the source code and install instructions on https://github.com/qutech/qopt.

Abstract

Realistic modelling of qubit systems including noise and constraints imposed by control hardware is required for performance prediction and control optimization of quantum processors. We introduce qopt, a software framework for simulating qubit dynamics and robust quantum optimal control considering common experimental situations. To this end, we model open and closed qubit systems with a focus on the simulation of realistic noise characteristics and experimental constraints. Specifically, the influence of noise can be calculated using Monte Carlo methods, effective master equations or with the filter function formalism, enabling the investigation and mitigation of auto-correlated noise. In addition, limitations of control electronics including finite bandwidth effects can be considered. The calculation of gradients based on analytic results is implemented to facilitate the efficient optimization of control pulses. The software is published under an open source license, well-tested and features a detailed documentation.

Summary

This python package is designed to facilitate the simulation of state-of-the-art quantum bits (qubits) including operation limitations, where an emphasis is put on the description of realistic experimental setups. For this purpose, an extensive set of noise simulation tools is included and complemented by methods to describe the limitations posed by the electronics steering the quantum operations.

The simulation interfaces to optimization algorithms to be used in optimal quantum control, the field of study which optimizes the accuracy of quantum operations by intelligent steering methods.

The functionalities can be coarsely divided into simulation and optimization of quantum operations. Various cost functions can be evaluated on the simulated evolution of the qubits such as an error rate, a gate or state fidelity or a leakage rate. Since gradient-based optimization algorithms perform extremely well in minimization problems, we implemented the derivatives of the cost functions by the optimization parameters based on analytical calculations.

Simulation

The evolution of a closed quantum system is described by Schroedinger’s equation, such that the dynamics are determined by the Hamiltonian of the system. Solving Schroedinger’s equation yields a description of the temporal evolution of the quantum system.

The Hamiltonian is the sum of effects which can be controlled, those who can not be controlled (the drift) and effects which cannot be even predicted (the noise.)

Noise

The realistic simulation of noise is one of qopt’s key features. The various methods are therefore mentioned in more detail, and in a brief overview is given stating the advantages and requirements of each method.

Monte Carlo Simulations

The most forward way to simulate noise is to draw samples from the noise distribution and repeat the simulation for each of those noise realizations. Any cost function is then averaged over the repetitions. The sampling is based on pseudo random number generators. Monte Carlo simulations are universally applicable but computationally expensive for high frequency noise.

Lindblad Master Equation

In order to include dissipation effects in the simulation, the qubit and its environment must be described as open quantum system, described by a master equation in Lindblad form. The solution of the master equation is in the general case not unitary unlike the propagators calculated from Schroedinger’s equation, such that it can also describe the loss of energy or information into the environment. This approach is numerically efficient but only applicable to systems subjected to markovian noise.

Filter Functions

The filter function formalism is a mathematical approach which allows the estimation of fidelities in the presence of universal classical noise. It is numerically very efficient for low numbers of qubits and widely applicable. This package interfaces to the open source filter function package (https://github.com/qutech/filter_functions) written by Tobias Hangleiter.

Leakage

Leakage occurs when the qubit leaves the computational space spanned by the computational states. To take this kind of error into account, the Hilbert space must be expanded as vector space sum by the leakage levels. The simulation is then performed on the larger Hilbert space and needs to be truncated to the computational states for evaluation. The Leakage rate or transition rate into the leakage states can be used to quantify the error rate caused to leakage.

Pulse Parametrization

In many practical applications the optimization parameters do not appear directly as factors in the Hamiltonian. The control fields are modified by taking limitations on the control electronics and the physical qubit model into account.

Transfer Functions

To model realistic control electronics the package includes transfer functions mapping the ideal pulse to the actual provided voltages. This can include for example exponential saturation to consider finite voltage rise times in pulse generators, Gaussian smoothing of pulses to mimic bandwidth limitations on arbitrary waveform generators, linear transformations or even the measured response of an arbitrary waveform generator to a set of input voltages.

Amplitude Functions

A differentiable functional relation between the optimization parameters and the control amplitudes can be expressed in the amplitude functions. This can for example be the exchange energy as function of the voltage detuning in a double quantum dot implemented in semiconductor spin qubits.

Optimization

To leverage a given noisy quantum computer to its full potential, optimal control techniques can be applied to mitigate the detrimental effects of noise. The package allows the use of different optimization algorithms by a strong modularity in the implementation.

Analytical Derivatives

Gradient based optimization algorithms such as GRAPE have proven to be versatile and reliable for the application in pulse optimization. For the efficient calculation of gradients, the package implements analytical derivatives for the solution of the Schroedinger equation, the master equation in Lindblad form and all calculations used to estimate fidelities.

Documentation

The documentation is structured in the three parts ‘Features’, ‘Example Applications’ and the ‘API Documentation’.

Features

The first part introduces the qopt functionalities step by step. Refer to this chapter for an introduction to the simulation package.

Example Applications

The ‘Example Applications’ combines an educational introduction to physical phenomena with simulation techniques and theoretical background information. They demonstrate how the package is used and treat FAQs on the way. They can also serve as blueprints for applications.

API Documentation

You can find the full API Documentation in the last section. Each class is implemented in a single module and each module is a subsection in the auto-generated API documentation. These subsections start with a general description of the purpose of the respective class. If you want to gain a quick overview of the class structure, I advise you to read through these descriptions. During the implementation of a simulation using qopt, you can frequently jump to the classes and functions your are using to look up the signatures.

Indices and tables