Quantum Computing Revolutionizes Understanding of Complex Molecular Systems, Study Shows

Theoretical descriptions of excited states of molecular systems are vital for various scientific domains, including chemistry, physics, materials science, and biology. They aid in understanding processes like energy transfer, photocatalic hydrogen production, and nanoparticle carrier dynamics. However, capturing the complex correlation effects of these systems requires a hierarchical infrastructure of approximations, leading to increased overhead in classical computing methods. The emergence of quantum computing methods, specifically the Quantum Phase Estimator (QPE), could change this. The QPE can identify complex states that traditional classical computing and approximate methods cannot, potentially revolutionizing experiments in light source facilities.

What is the Importance of Theoretical Descriptions of Excited States of Molecular Systems?

Theoretical descriptions of excited states of molecular systems in high-energy regimes are crucial for supporting and driving many experimental efforts at light source facilities. These descriptions are important in advancing various scientific domains such as chemistry, physics, materials science, and biology. Advanced theoretical modeling tools can facilitate the understanding of various processes including energy transfer through photochemical processes, photocatalytic hydrogen production, and carrier dynamics in nanoparticles and materials.

These tools are also needed to realize proton-coupled transfer in redox reactions and enable water oxidation, photoactivation processes in proteins, bioluminescence of living organisms, and ultrafast protective mechanisms in DNA. Predictive modeling tools also play a crucial role in supporting advanced light sources that contribute significantly to the advancement of X-ray spectroscopies including X-ray absorption, X-ray emission, resonant inelastic X-ray scattering, X-ray magnetic circular dichroism, and X-ray photoelectron which have greatly improved our understanding of the structure and properties of matter.

However, capturing the complicated correlation effects of these molecular systems requires formalisms that provide a hierarchical infrastructure of approximations. These approximations lead to an increased overhead in classical computing methods and therefore decisions regarding the ranking of approximations and the quality of results must be made on purely numerical grounds.

How Can Quantum Computing Change the Situation?

The emergence of quantum computing methods has the potential to change this situation. In this study, the researchers demonstrate the efficiency of Quantum Phase Estimator (QPE) in identifying core-level states relevant to x-ray photoelectron spectroscopy. They compare and validate the QPE predictions with exact diagonalization and real-time equation of motion coupled cluster formulations, which are some of the most accurate methods for states dominated by collective correlation effects.

The Quantum phase estimation algorithm allows one to estimate the eigenvalue corresponding to an eigenvector of a general many-body Hamiltonian operator. From the QPE algorithm, the distribution of energies for the ground and excited states is determined by the Hamiltonian and a trial many-body wavefunction represented as a combination of Slater determinants. The error in each QPE energy estimate is inversely proportional to the number of applications of the time evolution operator specified either through the number of ancillary qubits used in QPE or through the targeted bits of precision in the robust phase estimation variant that uses only one ancillary qubit.

What is the Practicality of Algorithms that Leverage Quantum or Classical Computational Resources?

In this study, the researchers investigate the practicality of algorithms that leverage quantum or classical computational resources to describe high-energy excited states of ionized molecules in the context of X-ray photoelectron spectra (XPS) experiments. Specifically, they examine the Quantum Phase Estimation (QPE) algorithm for quantum computing. To assess its accuracy, they compare with classical computing results obtained through exact diagonalization or equivalently full configuration interaction (FCI) methods and with systematic approximations based on recently developed the real-time equation of motion coupled cluster (RTEOMCC) method.

Several quantum algorithms have been developed for the evaluation of Green’s functions, which can be used in the calculation of ionization potential energies or as a solver for different embedding approaches. In this study, the researchers discuss a direct quantum computing approach to evaluate the spectral function. The spectral function can be obtained as a byproduct of statistically averaged QPE simulations.

How Can Quantum Phase Estimation Techniques Identify Complex States?

The utilization of QPE techniques presents a unique prospect to identify complex states that cannot be readily obtained through traditional classical computing and approximate methods. The QPE method can identify energies of states that have nonzero overlap with the trial wave function. Furthermore, the design of variational quantum eigensolver (VQE) simulations requires a priori knowledge of many-body effects needed to describe the state of interest.

In contrast to the VQE, which can only be used for energy estimates of a single targeted state subject to the convergence of the iterative procedures, the QPE method can identify energies of states that have nonzero overlap with the trial wave function. This makes the QPE techniques a unique prospect to identify complex states that cannot be readily obtained through traditional classical computing and approximate methods.

What is the Future of Quantum Computing in Evaluating Spectral Functions?

The future of quantum computing in evaluating spectral functions looks promising. If QPE simulations are performed for all spin-orbitals of the electron system using trial states and QPE is used to evaluate the ground state energy of the electron system, then one can reproduce in an approximate way the diagonal elements of the Green’s function and corresponding spectral function.

This approach simplifies the process and makes it more efficient. However, it’s important to note that this is an approximation and the actual ground state can be more complex. Despite this, the use of quantum computing in this field presents a unique opportunity to advance our understanding of complex molecular systems and could potentially revolutionize the way we conduct experiments in light source facilities.