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arXiv:1810.03959 · Physical Review A

Simulations of subatomic many-body physics on a quantum frequency processor

Hsuan‐Hao Lu, Natalie Klco, Joseph M. Lukens, Titus Morris, Aaina Bansal, A. Ekström, G. Hagen, T. Papenbrock, Andrew M. Weiner, Martin J. Savage A, Pavel Lougovski

Published Jul 15, 2019• 126 citations
arXivHTMLPDF

Abstract

Simulating complex many-body quantum phenomena is a major scientific impetus behind the development of quantum computing, and a range of technologies are being explored to address such systems. We present the results of the largest photonics-based simulation to date, applied in the context of subatomic physics. Using an all-optical quantum frequency processor, the ground-state energies of light nuclei including the triton ($^{3}\mathrm{H}$), $^{3}\mathrm{He}$, and the alpha particle ($^{4}\mathrm{He}$) are computed. Complementing these calculations and utilizing a 68-dimensional Hilbert space, our photonic simulator is used to perform subnucleon calculations of the two- and three-body forces between heavy mesons in the Schwinger model. This work is a first step in simulating subatomic many-body physics on quantum frequency processors---augmenting classical computations that bridge scales from quarks to nuclei.

DOI
10.1103/physreva.100.012320

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Simulating complex many-body quantum phenomena is a major scientific impetus behind the development of quantum computing, and a range of technologies are being explored to address such systems. We present the results of the largest photonics-based simulation to date, applied in the context of subatomic physics. Using an all-optical quantum frequency processor, the ground-state energies of light nuclei including the triton (3H^{3}\mathrm{H}3H), 3He^{3}\mathrm{He}3He, and the alpha particle (4He^{4}\mathrm{He}4He) are computed. Complementing these calculations and utilizing a 68-dimensional Hilbert space, our photonic simulator is used to perform subnucleon calculations of the two- and three-body forces between heavy mesons in the Schwinger model. This work is a first step in simulating subatomic many-body physics on quantum frequency processors---augmenting classical computations that bridge scales from quarks to nuclei.
Photonics is at the forefront of quantum computing and simulations. It enabled a host of pioneering demonstrations of the variational quantum eigensolver (VQE) algorithm, of molecular vibronic spectra and dynamics simulations, and of experimental Hamiltonian learning. It offers a versatile platform to process quantum information with low noise in a multitude of encodings, ranging from spatial or polarization degrees of freedom, to temporal modes. We discuss how frequency encoding, where qubits are represented by photons in narrow frequency bins, can be utilized for simulating complex many-body quantum phenomena in nuclear physics and quantum field theory. Using an all-optical quantum frequency processor, the ground-state energies of light nuclei including the triton, 3He, and the alpha particle are computed. Complementing these calculations and utilizing a 68-dimensional Hilbert space, our photonic simulator is used to perform sub-nucleon calculations of the two-body and three-body forces between heavy mesons in the Schwinger model. This work is a first step in simulating subatomic many-body physics on quantum frequency processors---augmenting classical computations that bridge scales from quarks to nuclei.
Quantum computing introduces a revolutionary approach to solving problems that are computationally beyond the reach of classical systems. Among its most promising applications is the simulation of many-body systems, where strong particle interactions lead to complex quantum behaviors. Traditional computational techniques often fail due to exponential resource requirements, but quantum simulators can efficiently model these interactions through entanglement and superposition. Recent advances in superconducting qubits, trapped ions, and photonic quantum devices have enabled small- scale experimental demonstrations of spin models, Hubbard systems, and correlated electron dynamics. These developments offer new insights into condensed matter physics and quantum phase transitions. As quantum technologies continue to evolve, they hold the potential to transform the study of materials, superconductivity, and other many-body phenomena with remarkable precision and scalability.
Quantum strong-interaction physics can yield collective phenomena over a vast range of energy scales and kinematics. We seek the emergence of quantum collective phenomena from underlying inter-particle interactions using advanced computational approaches with detailed theoretical analyses on both a low-energy (non-relativistic) and a high-energy (relativistic) scale. We select alpha-clustering in light nuclei for the non-relativistic phenomenon and a phase transition in quantum field theory for the relativistic phenomenon. Both problems are defined by microscopic Hamiltonians from the underlying theory and solved with advanced computational techniques. Detailed investigation of the results yields insights into remarkable emergent phenomena. This work proposes novel analytical and computational tools that lead to demonstrations on how non-perturbative quantum many-body theory in the Hamiltonian framework produces results that predict collective phenomena.
To understand the properties and interactions of materials, and determining the ground state energies is one of the important challenges in quantum chemistry, materials science, and quantum mechanics, where quantum computing can play an important role for studying the properties of materials. In this study, we have explored the quantum processor application to compute the He molecule ground state energy which utilizes the Variational Quantum Eigensolver (VQE) algorithm. In here, we have implemented VQE on a state-of-the-art quantum processor, optimizing a parameterized quantum circuit to minimize the energy expectation value of the He molecule's Hamiltonian on the four qubits processor. The obtained results of this work show a significant improvement in accuracy compared to classical computational methods, such as Hartree-Fock and density functional theory, which demonstrate the compute potential of quantum algorithms in quantum many-body problems. Thus, these results demonstrate the advantages of quantum computing in achieving high accuracy in simulations of molecular and material properties, and pave the way for future applications in more complex systems. This work highlights the potential of quantum processors in the fields of quantum chemistry, computational physics, and data science.