Abstract
Quantum computers have the potential to advance material design and drug discovery by performing costly electronic structure calculations. A critical aspect of this application requires optimizing the limited resources of the quantum hardware. Here, we experimentally demonstrate an end-to-end pipeline that focuses on minimizing quantum resources while maintaining accuracy. Using density matrix embedding theory as a problem decomposition technique, and an ion-trap quantum computer, we simulate a ring of 10 hydrogen atoms without freezing any electrons. The originally 20-qubit system is decomposed into 10 two-qubit problems, making it amenable to currently available hardware. Combining this decomposition with a qubit coupled cluster circuit ansatz, circuit optimization, and density matrix purification, we accurately reproduce the potential energy curve in agreement with the full configuration interaction energy in the minimal basis set. Our experimental results are an early demonstration of the potential for problem decomposition to accurately simulate large molecules on quantum hardware.
Highlights
Quantum computers have the potential to advance material design and drug discovery by performing costly electronic structure calculations
In contrast to simulating the entire system using H^, in Density matrix embedding theory (DMET), the system to be simulated is divided into small fragments
The experimentally determined energies nearly coincide with the full configuration interaction (full CI) energies and DMET-qubit coupled-cluster (QCC) reference values and agree within the margin of error
Summary
Quantum computers have the potential to advance material design and drug discovery by performing costly electronic structure calculations. Performing accurate electronic structure simulations on classical computers requires a great amount of computational resources They grow exponentially with the system size when employing the full configuration interaction (full CI) method, which calculates the exact solution of the electronic Schrödinger equation in a given basis set. To approach the simulation of larger molecules, problem decomposition techniques can be used to decompose a given molecular system into small subsystems, without sacrificing the accuracy of the electronic structure calculation for a wide class of chemical systems[15–18]. These techniques admit a more compact representation of a molecule, enabling the explicit inclusion of more electrons in calculating correlation energies. The amount of reduction in the computational cost and resulting accuracy is dependent on the problem decomposition algorithm and the system being studied, these techniques have the potential to substantially reduce the qubit count requirements in electronic structure simulations[19–27]
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