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arXiv:2403.10619 · Physical Review A

Simulating quantum field theories on continuous-variable quantum computers

Steven Adam Abel, Michael Spannowsky, Simon Williams

Published Jul 8, 2024• 21 citations
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Abstract

We delve into the use of photonic quantum computing to simulate quantum mechanics and extend its application towards quantum field theory. We develop and prove a method that leverages this form of continuous-variable quantum computing (CVQC) to reproduce the time evolution of quantum-mechanical states under arbitrary Hamiltonians, and we demonstrate the method's remarkable efficacy with various potentials. Our method centers on constructing an , a specially prepared quantum state that induces the desired time evolution on the target state. This is achieved by introducing a non-Gaussian operation using a measurement-based quantum computing approach, enhanced by machine learning. Furthermore, we propose a framework in which these methods can be extended to encode field theories in CVQC without discretizing the field values, thus preserving the continuous nature of the fields. This opens new avenues for quantum computing applications in quantum field theory. Published by the American Physical Society 2024

DOI
10.1103/physreva.110.012607

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We delve into the use of photonic quantum computing to simulate quantum mechanics and extend its application towards quantum field theory. We develop and prove a method that leverages this form of continuous-variable quantum computing (CVQC) to reproduce the time evolution of quantum-mechanical states under arbitrary Hamiltonians, and we demonstrate the method's remarkable efficacy with various potentials. Our method centers on constructing an , a specially prepared quantum state that induces the desired time evolution on the target state. This is achieved by introducing a non-Gaussian operation using a measurement-based quantum computing approach, enhanced by machine learning. Furthermore, we propose a framework in which these methods can be extended to encode field theories in CVQC without discretizing the field values, thus preserving the continuous nature of the fields. This opens new avenues for quantum computing applications in quantum field theory. Published by the American Physical Society 2024
This work introduces an approach to quantum simulation by leveraging continuous-variable systems within a photonic hardware-inspired framework. The primary focus is on simulating static properties of the ground state of Hamiltonians associated with infinite-dimensional systems, such as those arising in quantum field theory. We present a continuous-variable variational quantum eigensolver compatible with state-of-the-art photonic technology. The framework we introduce allows us to compare discrete and continuous variable systems without introducing a truncation of the Hilbert space, opening the possibility to investigate the scenarios where one of the two formalisms performs better. We apply it to the study of static properties of the Bose-Hubbard model and demonstrate its effectiveness and practicality, highlighting the potential of continuous-variable quantum simulations in addressing complex problems in quantum physics. Published by the American Physical Society 2024
We propose a framework for simulating the real-time dynamics of quantum field theories using continuous-variable quantum computing (CVQC). Focusing on ( 1 + 1 )-dimensional φ 4 scalar field theory, the approach employs the Hamiltonian formalism to map the theory onto a spatial lattice, with fields represented as quantum harmonic oscillators. Using measurement-based quantum computing, we implement non-Gaussian operations for CQVC platforms. The study introduces methods for preparing initial states with specific momenta and simulating their evolution under the φ 4 Hamiltonian. Key quantum objects, such as two-point correlation functions, validate the framework against analytical solutions. Scattering simulations further illustrate how mass and coupling strength influence field dynamics and energy redistribution. Thus, we demonstrate CVQC's scalability for larger lattice systems and its potential for simulating more complex field theories.
We formulate a continuous-variable quantum computing (CVQC) algorithm to study Berry's phase on photonic quantum computers. We demonstrate that CVQC allows the simulation of charged particles with orbital angular momentum under the influence of an adiabatically changing B⃗\vec{B}B field. Although formulated entirely in the CVQC setting, our construction uses only passive linear-optical operations (beam splitters and phase shifts), which act identically in single-photon photonic architectures. This enables experimental realisation on the Quandella Ascella platform, where we observe the Berry's phase phenomenon with interferometric measurement. We also generalise the framework to more rapid non-adiabatic evolution. By concatenating Aharonov-Anandan cycles for opposing magnetic fields we demonstrate that one can engineer a circuit in which dynamical phases and leading non-geometric errors cancel by symmetry, leaving the intrinsically robust geometric phase contribution.
Quantum computing offers a paradigm shift in efficiently simulating quantum field theories (QFTs). In this review, we outline two new techniques for the simulation of QFTs on quantum devices. The first technique employs Hamiltonian Truncation (HT) for nonperturbative real-time QFT simulations on Noisy Intermediate-Scale Quantum (NISQ) devices. As a use case, we apply the HT approach to the Schwinger model. For the observables studied, HT avoids complex state preparation, reducing circuit depth and making the algorithm well suited for NISQ devices. Validated on the ibm_brisbane quantum computer with results showing good agreement with numerical simulations, HT presents an efficient alternative to lattice models. The second approach introduces a continuous-variable quantum computing (CVQC) framework for simulating the real-time dynamics of QFTs, where quantum field values are encoded directly into photonic qumodes. By avoiding the overhead of field digitization, CVQC enables scalable simulations using significantly fewer quantum resources than traditional qubit-based approaches. Classical emulation of the qumode lattice successfully reproduces scattering processes in [Formula: see text]-dimensional [Formula: see text] theory. Together, these methods demonstrate viable paths toward near-term quantum simulations of interacting field theories, offering complementary strategies to address challenges in nonperturbative studies in high-energy physics.
Abstract Continuous-variable quantum computing (CVQC) uses quantum states with continuous degrees of freedom, such as the quadratures of bosonic modes, to encode information, promising efficient solutions to complex problems. Trapped-ion systems provide a robust platform with long coherence times and precise qubit control, enabling the manipulation of quantum information through its motional and electronic degrees of freedom. In this theoretical work, we quantitatively analyze the fidelity of a Kerr operation in a single trapped-ion system, calibrating the interaction time using a motional-state Rabi frequency correction. Through numerical simulations, we validate Gaussian and non-Gaussian CVQC operations, achieving fidelities exceeding 99% in all cases, including non-Gaussian operations. Furthermore, we demonstrate two key applications: first, the implementation of a continuous-variable quantum neural network for regression problems, which exhibits low error rates; and second, its application to state preparation tasks, achieving a fidelity of 90% for the given example. These results open new opportunities to execute quantum computing algorithms on continuous variables with trapped ions, such as quantum machine learning algorithms.
Continuous-variables (CV) quantum optics is a natural formalism for neural networks (NNs) due to its ability to reproduce the information processing of such trainable interconnected systems. In quantum optics, Gaussian operators induce affine mappings on the quadratures of optical modes while non-Gaussian resources (the challenging piece for physical implementation) originate the nonlinear effects, unlocking quantum analogs of an artificial neuron. This work presents a novel experimentally-feasible framework for continuous-variable quantum optical neural networks (QONNs) developed with available photonic components: coherent states as input encoding, a general Gaussian transformation followed by multi-mode photon subtractions as the processing layer, and homodyne detection as outputs readout. The closed-form expressions of such architecture are derived demonstrating the family of adaptive activations and the quantum-optical neurons that emerge from the amount of photon-subtracted modes, proving that the proposed design satisfies the Universal Approximation Theorem within a single layer. To classically simulate the QONN training, the high-performance QuaNNTO library has been developed based on Wick-Isserlis expansion and Bogoliubov transformations, allowing multi-layer exact expectation values of non-Gaussian states without truncating the infinite-dimensional Hilbert space. Experiments on supervised learning and state-preparation tasks show balanced-resource efficiency with strong expressivity and generalization capabilities, illustrating the potential of the architecture for scalable photonic quantum machine learning and for quantum applications such as complex non-Gaussian gate synthesis.