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Utility-scale quantum computational chemistry

Davide Castaldo, Markus Reiher

Abstract

Chemistry and materials science are widely regarded as potential killer application fields for quantum hardware. While the dream of unlocking unprecedented simulation capabilities remains compelling, quantum algorithm development must adapt to the evolving constraints of the emerging quantum hardware in order to accomplish any advantage for the computational chemistry practice. At the same time, the continuous advancement of classical wavefunction-theory methods narrows the window for a broad quantum advantage. Here, we explore potential benefits of quantum computation from the broader perspective of utility-scale applications. We argue that quantum algorithms need not only enable accurate calculations for a few challenging, that is strongly correlated, molecular structures, that might be hard to describe with traditional methods. Instead, they must also support the practical integration of quantum-accelerated computations into high-throughput pipelines for routine calculations on arbitrary molecules, ultimately delivering a tangible value to society.

Utility-scale quantum computational chemistry

Abstract

Chemistry and materials science are widely regarded as potential killer application fields for quantum hardware. While the dream of unlocking unprecedented simulation capabilities remains compelling, quantum algorithm development must adapt to the evolving constraints of the emerging quantum hardware in order to accomplish any advantage for the computational chemistry practice. At the same time, the continuous advancement of classical wavefunction-theory methods narrows the window for a broad quantum advantage. Here, we explore potential benefits of quantum computation from the broader perspective of utility-scale applications. We argue that quantum algorithms need not only enable accurate calculations for a few challenging, that is strongly correlated, molecular structures, that might be hard to describe with traditional methods. Instead, they must also support the practical integration of quantum-accelerated computations into high-throughput pipelines for routine calculations on arbitrary molecules, ultimately delivering a tangible value to society.
Paper Structure (6 sections, 1 equation, 1 figure)

This paper contains 6 sections, 1 equation, 1 figure.

Table of Contents

  1. Introduction
  2. The quantum computing stack
  3. Different regimes of compilation
  4. Target Applications in Molecular and Materials Science
  5. Conclusions
  6. Author information

Figures (1)

  • Figure 1: Quantum circuit fidelity $F$ modeled according to the scalability framework proposed in Ref. katabarwa2024early. Fidelity decreases exponentially with the number of gates, following Eq. (\ref{['fidelity']}). The gate error probability $p_{\text{gate}}$ decreases with the number of physical qubits $Q$ and is given by $p_{\text{gate}} = p_0 Q^{1/s}$, where $s$ and $p_0$ are the hardware-dependent scalability factor and average error rate, respectively. Here we set $s = 4$ and $p_0 = 0.0001$.