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Photonics for Quantum (PfQ)

Simulating subatomic physics on a quantum frequency processor

Pavel Lougovski

Published Aug 27, 2021• 0 citations
Publisher

Abstract

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.

DOI
10.1117/12.2610001

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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.
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.
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.
Quantum computers have the potential to deliver speed-ups for solving certain important problems that are intractable for classical counterparts, making them a promising avenue for advancing modern computation. However, many quantum algorithms require deep quantum circuits, which are challenging to implement on current noisy devices. To address this limitation, variational quantum algorithms (VQAs) have been actively developed, enabling practical quantum computing in the noisy intermediate-scale quantum (NISQ) era. Among them, the variational quantum eigensolver (VQE) stands out as a leading approach for solving problems in quantum chemistry, many-body physics, and even integer factorization. The VQE algorithm can be implemented on various quantum hardware platforms, including photonic systems, quantum dots, trapped ions, neutral atoms, and superconducting circuits. In particular, photonic platforms offer several advantages: they operate at room temperature, exhibit low decoherence, and support multiple degrees of freedom, making them suitable for scalable, high-dimensional quantum computation. Here we present methodologies for realizing VQE on photonic systems, highlighting their potential for practical quantum computing. We first provide a theoretical overview of the VQE framework, focusing on the procedure for variationally estimating ground state energies. We then explore how photonic systems can implement these processes, showing that a wide variety of problems can be addressed using either multiple qubit states or a single qudit state.
Abstract 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.