HQS Qorrelator App

Quantum Correlation Analysis App

HQStage Module

Quantum Computing

Enter the HQS Qorrelator App, a user-friendly solution designed to calculate correlation functions using the power of quantum computers. Correlation functions are a central component of spectroscopy, and the HQS Qorrelator App makes it easier than ever to work with them.

The app is particularly suited to obtaining NMR correlation functions, one of the most key methods in spectroscopy. This feature is especially beneficial in the chemical and pharmaceutical industries, where NMR spectroscopy is frequently employed.

Spectroscopy, an incredibly powerful tool, stands as a cornerstone in the field of chemistry. It facilitates the precise identification and characterization of chemical compounds, thereby playing a pivotal role in areas such as drug discovery and environmental analysis.

Indeed, it is arguably the most crucial instrument at our disposal for probing the microscopic world of quantum phenomena. However, interpreting spectroscopic data can pose a significant challenge.

This is where theoretical considerations come into play, aiding in the calculation of spectra. Given the quantum nature of spectroscopy, these calculations can be quite complex for conventional computers to handle.

The NMR spectrum of non-exchangeable hydrogen of cis-3-chloroacrylic acid simulated on NISQ devices. The solid orange curve depicts the results on IBM Perth, the blue dash-dotted curve is the IonQ Aria ones, and the solid black curve shows the results with exact diagonalization.

(a) The spectrum of 1,2,4-Trichlorobenzene (C6 H3 Cl3) through experimental NMR spectroscopy. (b) The comparison of the simulation with exact diagonalization, shown as the solid black curve, with noise-free simulation depicted as the dashed blue curve.

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Features

  • Create a quantum program to calculate correlation functions in the time domain for an NMR-type problem, considering a total-spin-conserving Hamiltonian and an infinite-temperature environment.

  • Add spin-resolved correlation functions to the NMR calculation for a more in-depth analysis of the results.

  • Choose the direction of the B-field used for the NMR calculation, with automatic conversion of input Hamiltonians.

  • Easy to run on many different quantum computers using open-source qoqo backends, without the need for long Python post-processing routines.

  • Obtain the noisy algorithm model for the time evolution used in the correlation function calculation. This can be used, for instance, to determine how well the effective environmental noise fits the assumption of an infinite-temperature environment that is used for NMR calculations.

Benefits

  • Convenient end-to-end implementation of the correlation functions for NMR spectroscopy

  • User-friendly general calculation of correlation functions

  • Easy-to-use function to calculate the peak width of the calculated spectrum using noise information from the quantum computer. This allows for a unique estimate of the resolution of the result on noisy intermediate-scale quantum computers.

Use Case

  • Resource Analysis: Determining the required resources (qubits, computational time) to calculate NMR or other spectra based on a specific Spin-Hamiltonian.

  • Feasibility Assessment: Evaluating the feasibility of calculating NMR or other spectra on a particular quantum computer.

  • Noise Examination: Analyzing the impact of noise on the calculation of NMR or other spectra on a quantum computer.

  • Experimentation: Ability to conduct test runs to calculate NMR or other spectra on a quantum computer to optimize algorithms and parameters.

Theoretical Background

Empfohlen
The impact of noise on the simulation of NMR spectroscopy on NISQ devices
The impact of noise on the simulation of NMR spectroscopy on NISQ devices

Authors: Andisheh Khedri, Pascal Stadler, Kirsten Bark, Matteo Lodi, Rolando Reiner, Nicolas Vogt, Michael Marthaler, Juha Leppäkangas

We present the simulation of nuclear magnetic resonance (NMR) spectroscopy of small organic molecules with two promising quantum computing platforms, namely IBM's quantum processors based on superconducting qubits and IonQ's Aria trapped ion quantum computer addressed via Amazon Bracket. We analyze the impact of noise on the obtained NMR spectra, and we formulate an effective decoherence rate that quantifies the threshold noise that our proposed algorithm can tolerate. Furthermore we showcase how our noise analysis allows us to improve the spectra. Our investigations pave the way to better employ such application-driven quantum tasks on current noisy quantum devices.

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