This thesis looks at how we can leverage current and near future quantum computers to effectively simulate quantum field theories. The scope of this thesis covers how to simulate the (1+1)-D Transverse Ising model on digital quantum computers and the methods to handle multiple types of machine errors to effectively extract real-time dynamic information. We leverage modifications to the transverse Ising model to study the calculation of phase shifts from scattering a particle off of simple potentials and demonstrate that these calculations are feasible on current quantum hardware. In looking toward the future we demonstrate two different state preparation algorithms. One algorithm projected cooling we show can extract information about the second order phase transition in the Transverse Ising Model and has excellent scaling properties. The second algorithm involves leveraging classical field theory simulations to circumvent quantum state preparation methods on that should prove tractable on near term quantum computing hardware. These tools together highlight the promise of quantum computing to tackle interesting problems in high energy physics.
This thesis looks at how we can leverage current and near future quantum computers to effectively simulate quantum field theories. The scope of this thesis covers how to simulate the (1+1)-D Transverse Ising model on digital quantum computers and the methods to handle multiple types of machine errors to effectively extract real-time dynamic information. We leverage modifications to the transverse Ising model to study the calculation of phase shifts from scattering a particle off of simple potentials and demonstrate that these calculations are feasible on current quantum hardware. In looking toward the future we demonstrate two different state preparation algorithms. One algorithm projected cooling we show can extract information about the second order phase transition in the Transverse Ising Model and has excellent scaling properties. The second algorithm involves leveraging classical field theory simulations to circumvent quantum state preparation methods on that should prove tractable on near term quantum computing hardware. These tools together highlight the promise of quantum computing to tackle interesting problems in high energy physics.
Quantum mechanical problems are among the hardest to simulate and, in some cases, remain intractable even for the most powerful computers. Quantum computing has emerged as a new technological platform to address such challenges, with rapid advances in recent years. Yet, current quantum devices remain noisy and limited in scale. Hence, it is essential to identify classically hard problems of practical interest and tractable with existing quantum devices. Among potential applications, the real-time simulation of quantum systems is one of the most promising to deliver an early, practical quantum advantage. This doctoral thesis is therefore centered around simulating quantum dynamics on quantum devices. We first present an overview of the most relevant quantum algorithms for quantum dynamics, highlighting respective advantages and limitations. Further, we identify relevant problems within quantum dynamics that could benefit from quantum simulation in the near future. Second, we propose a method for benchmarking hardware and error mitigation algorithms that is based on well-understood theoretical results and suffers from no scaling issues. The resulting quality metric is intuitive and transferable to other applications. We successfully implement the scheme on up to 133 qubits, demonstrating coherent evolution up to a two-qubit gate depth of 28, featuring 1396 two-qubit gates, before noise becomes prevalent. Third, we propose a novel variant of probabilistic error amplification to implement open quantum dynamics, relying on characterizing and altering hardware noise to mimic the system-environment interaction under study. Lastly, we present two studies on state preparation and phase classification: first, a hybrid algorithm to prepare ground states of electron-phonon systems; second, a quantum machine learning-based approach to distinguish phases in previously prepared quantum states.
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.
Quantum computing offers a new, orthogonal direction for investigating fundamental physics, extending beyond classical numerical methods and conventional observables. Realizing this potential requires directly confronting the noise limiting currently available quantum computers. Progress rests on advancing algorithms, interpreting their results, and managing their errors together. This thesis presents several advancements in the use of quantum simulation and quantum information to probe fundamental physics.
The first is in the use of quantum computers to simulate collisions in quantum field theories. Central to these simulations are new wavepacket preparation, time evolution, and error mitigation techniques, which allow for simulations with some of the largest effective circuit volumes to date. These methods enable the first quantum simulation providing numerical evidence for inelastic particle production, a key process in fundamental physics.
The second advancement centers on the role quantum-information-theoretic quantities play in physical processes. Beyond mere correlations with the physics of the process, entanglement and magic are shown to probe the interactions present in scattering and hadronization dynamics.
A precision study requires a complete quantification of algorithmic and hardware uncertainties, an outstanding goal as quantum simulations mature. The third advancement in this thesis addresses error management. A framework minimizing the effect of algorithmic errors in analog quantum simulations is presented. In a step toward fault tolerance, error detection in encoded quantum simulations is shown to improve estimation of local observables relative to unencoded runs.
Together, the developments in this thesis mark practical progress toward fault-tolerant quantum simulations of fundamental physics capable of scientific discovery.
This research paper provides a comprehensive review of quantum simulation methods for lattice gauge theories during the current era of noisy intermediate-scale quantum computing. It focuses on the fundamental framework used to understand strong interactions in high-energy physics and explores how quantum devices can overcome the limitations of traditional computational methods, such as the sign problem found in classical simulations. The manuscript is built around three primary pillars that are critical for near-term progress. The first pillar involves encoding strategies, describing the movement away from simple mapping techniques toward more advanced, compact methods that significantly decrease the number of required quantum bits by focusing only on physically relevant states and exploiting local gauge constraints. The second pillar analyzes specific variational quantum algorithms that use flexible circuit structures to find low-energy states, comparing different approaches based on their circuit depth and suitability for current hardware. The third pillar evaluates various error mitigation techniques used to extract accurate information from imperfect quantum hardware, such as methods that amplify and then remove noise or those that use physical symmetries to detect and discard errors. Additionally, the paper compares different hardware technologies—including those based on superconducting circuits, trapped atoms, and ions—to weigh their respective benefits, such as gate speed and connectivity, against their drawbacks. It also introduces emerging hybrid approaches that combine quantum circuits with artificial neural networks to improve simulation power and state representation. Ultimately, the review identifies pathways toward achieving practical advantages in the field and discusses the challenges that remain for scaling these systems to larger sizes.
Many important physical and mathematical problems are difficult to solve on classical computers, resulting in difficulty in finding new advanced materials, simulating quantum field theories and solving large combinatorial optimisation tasks. Quantum computers have shown great promise in being able to solve these (and many other) problems faster than their classical counterparts, and therefore should be thoroughly investigated as a significant tool for enabling the acceleration of scientific progress. This work develops methods to tackle several problems across the spectrum of quantum algorithms, providing methods suitable for the near-term hardware, to the future where large fault-tolerant quantum computers exist. Therefore, the works in this thesis range from efficiently making use of limited quantum hardware to taking advantage of hardware benefits (e.g. excess qubits) as quantum computation advances. Covered topics include: an alternative paradigm for the optimisation of variational circuits and quantum neural networks; techniques for improving energy estimation of quantum states through the collection and processing of large amounts of randomised measurement data; the use of entanglement in guiding the automatic discovery of quantum circuits; investigating the role of symmetries in adiabatic state preparation of molecules and the large scale parallelisation of a common fault-tolerant subroutine, potentially producing large runtime speed-ups for the simulation of chemistry on quantum computers. Although there are many challenges remaining from both hardware and algorithms perspectives before the large-scale utility of quantum computers can be demonstrated, the contributions of this work make steps towards the efficient utilisation of quantum hardware that will be available in the coming years.
Lattice field theory, along with its algorithmic and hardware ecosystems, has been at the forefront of computational particle and nuclear physics. It continues to deliver impressive results on the hadronic spectrum, structure, decays, and reactions. Yet, this vigorous campaign has fallen short in addressing a range of problems involving dense matter and general dynamical phenomena. The reason is that such problems require an exponential scaling of computing time and space in system size. Quantum simulation, enabled by quantum-computing algorithms and hardware technology, promises a way forward by offering several polynomially efficient algorithms compared with their inefficient classical counterparts. Lattice gauge theorists have engaged in a multi-pronged program to leverage such new possibilities, and have steadily advanced the state of theory, algorithm, and hardware implementations and co-design. In this talk, I motivate the quantum-computational lattice-field-theory program; introduce the questions such a program is expected to address and the strategies it involves; report on recent progress; and end with a note on challenges and opportunities ahead.