Exploring quantum many-body systems from the viewpoints of quantum computing and spectroscopy

Understanding quantum many-body behaviours such as exotic phases and spectroscopic properties in quantum materials and molecular systems is a long-standing problem of both fundamental and practical interest in quantum physics. This understanding provides insights into the true underlying physics of quantum many-body systems, aids in the prediction of the microscopic and macroscopic properties of those systems, and also advances the rational design and synthesis of novel materials. However, our ability to understand quantum many-body behaviours has hitherto been limited, due to the excessive demands imposed on classical computing by the inherent complexity of describing and analysing those behaviours. While the advent of quantum computing has opened up new possibilities for examining these questions, the current generation of quantum technology does not yet present a feasible, standalone way to solve the above problem. However, a fusion of classical and quantum approaches could arguably provide a viable way of exploring interesting quantum phenomena. The central objective of this thesis is to achieve such a synthesis in practice, and to establish a corresponding framework for the study of quantum many-body systems. One area of particular interest is the intersection between quantum computing and spectroscopy, specifically in terms of the latter's potential to greatly assist in the investigation of quantum many-body phenomena. Quantum many-body problems in general can be divided into two classes, static and dynamic problems, which correspond to the estimation of eigenstate properties (such as eigenenergies and order parameters in different phases), and dynamical properties (such as response to an external field). In Part II, I present a number of approaches to solve these static and dynamic problems. I initially establish a quantum computing framework based on hybrid quantum-classical tensor networks, which incorporate the inherent advantages of classical tensor networks and quantum computing to represent the quantum system. I then demonstrate how eigenstate properties can be estimated by a randomised linear-combination-of-unitary method, termed algorithmic cooling, with at most one ancillary qubit; this achieves a logarithmic circuit complexity with respect to precision in eigenstate property estimation, and reaches the Heisenberg limit in eigenenergy estimation. Turning to dynamic problems, I present an adaptive product formula approach to construct a low-depth quantum circuit for simulating quantum dynamics. I further show how to enable large-scale dynamics simulation based on hybrid tensor networks, followed by a perturbative approach to simulating quantum many-body dynamics. In Part III, I first demonstrate how spectroscopic features of quantum systems can be probed. Equipped with the framework and methods established and developed in this thesis, I study quantum many-body phenomena, and excitation spectra in interacting bosons, fermions, and quantum spins through numerics and experiments. In Part IV, the quantum resources required for the application of quantum computing to realistic problems in the near future are assessed, together with the challenges that accompany such application. This encompasses a discussion of the estimated resources needed for estimating eigenstate properties of spins, fermions and molecules, in respect of both noisy quantum processors and fault-tolerant quantum computers. I then address some of the inherent challenges of using near-term noisy quantum devices, such as encountering unavoidable quantum process errors and statistical errors, by applying error mitigation, and efficient grouping measurement schemes proposed in this thesis. Finally, I conclude with a few remarks on the development of quantum computing in solving quantum many-body systems, and I pose outlooks for further research in this field

Variational quantum simulation of general processes

Variational quantum algorithms have been proposed to solve static and dynamic problems of closed many-body quantum systems. Here we investigate variational quantum simulation of three general types of tasks---generalised time evolution with a non-Hermitian Hamiltonian, linear algebra problems, and open quantum system dynamics. The algorithm for generalised time evolution provides a unified framework for variational quantum simulation. In particular, we show its application in solving linear systems of equations and matrix-vector multiplications by converting these algebraic problems into generalised time evolution. Meanwhile, assuming a tensor product structure of the matrices, we also propose another variational approach for these two tasks by combining variational real and imaginary time evolution. Finally, we introduce variational quantum simulation for open system dynamics. We variationally implement the stochastic Schr\"odinger equation, which consists of dissipative evolution and stochastic jump processes. We numerically test the algorithm with a six-qubit 2D transverse field Ising model under dissipation.Comment: 18 page

Simple and high-precision Hamiltonian simulation by compensating Trotter error with linear combination of unitary operations

Trotter and linear-combination-of-unitary (LCU) are two popular Hamiltonian simulation methods. We propose Hamiltonian simulation algorithms using LCU to compensate Trotter error, which enjoy both of their advantages. By adding few gates after the Kth-order Trotter, we realize a better time scaling than 2Kth-order Trotter. Our first algorithm exponentially improves the accuracy scaling of the Kth-order Trotter formula. In the second algorithm, we consider the detailed structure of Hamiltonians and construct LCU for Trotter errors with commutator scaling. Consequently, for lattice Hamiltonians, the algorithm enjoys almost linear system-size dependence and quadratically improves the accuracy of the Kth-order Trotter.Comment: 74 pages, 15 figures. Comments are welcom

Robust and Efficient Hamiltonian Learning

With the fast development of quantum technology, the sizes of both digital and analog quantum systems increase drastically. In order to have better control and understanding of the quantum hardware, an important task is to characterize the interaction, i.e., to learn the Hamiltonian, which determines both static and dynamic properties of the system. Conventional Hamiltonian learning methods either require costly process tomography or adopt impractical assumptions, such as prior information on the Hamiltonian structure and the ground or thermal states of the system. In this work, we present a robust and efficient Hamiltonian learning method that circumvents these limitations based only on mild assumptions. The proposed method can efficiently learn any Hamiltonian that is sparse on the Pauli basis using only short-time dynamics and local operations without any information on the Hamiltonian or preparing any eigenstates or thermal states. The method has a scalable complexity and a vanishing failure probability regarding the qubit number. Meanwhile, it performs robustly given the presence of state preparation and measurement errors and resiliently against a certain amount of circuit and shot noise. We numerically test the scaling and the estimation accuracy of the method for transverse field Ising Hamiltonian with random interaction strengths and molecular Hamiltonians, both with varying sizes and manually added noise. All these results verify the robustness and efficacy of the method, paving the way for a systematic understanding of the dynamics of large quantum systems.Comment: 41 pages, 6 figures, Open source implementation available at https://github.com/zyHan2077/HamiltonianLearnin

Low-depth Hamiltonian Simulation by Adaptive Product Formula

Various Hamiltonian simulation algorithms have been proposed to efficiently study the dynamics of quantum systems using a universal quantum computer. However, existing algorithms generally approximate the entire time evolution operators, which may need a deep quantum circuit that are beyond the capability of near-term noisy quantum devices. Here, focusing on the time evolution of a fixed input quantum state, we propose an adaptive approach to construct a low-depth time evolution circuit. By introducing a measurable quantifier that describes the simulation error, we use an adaptive strategy to learn the shallow quantum circuit that minimizes the simulation error. We numerically test the adaptive method with the electronic Hamiltonians of $\mathrm{H_2O}$ and $\mathrm{H_4}$ molecules, and the transverse field ising model with random coefficients. Compared to the first-order Suzuki-Trotter product formula, our method can significantly reduce the circuit depth (specifically the number of two-qubit gates) by around two orders while maintaining the simulation accuracy. We show applications of the method in simulating many-body dynamics and solving energy spectra with the quantum Krylov algorithm. Our work sheds light on practical Hamiltonian simulation with noisy-intermediate-scale-quantum devices.Comment: 10 pages, 2 figure

Quantum Computing Quantum Monte Carlo

Quantum computing and quantum Monte Carlo (QMC) are respectively the state-of-the-art quantum and classical computing methods for understanding many-body quantum systems. Here, we propose a hybrid quantum-classical algorithm that integrates these two methods, inheriting their distinct features in efficient representation and manipulation of quantum states and overcoming their limitations. We first introduce non-stoquasticity indicators (NSIs) and their upper bounds, which measure the sign problem, the most notable limitation of QMC. We show that our algorithm could greatly mitigate the sign problem, which decreases NSIs with the assistance of quantum computing. Meanwhile, the use of quantum Monte Carlo also increases the expressivity of shallow quantum circuits, allowing more accurate computation that is conventionally achievable only with much deeper circuits. We numerically test and verify the method for the N$_2$ molecule (12 qubits) and the Hubbard model (16 qubits). Our work paves the way to solving practical problems with intermediate-scale and early-fault tolerant quantum computers, with potential applications in chemistry, condensed matter physics, materials, high energy physics, etc

Some variational recipes for quantum field theories

Rapid developments of quantum information technology show promising opportunities for simulating quantum field theory in near-term quantum devices. In this work, we formulate the theory of (time-dependent) variational quantum simulation of the 1+1 dimensional $\lambda \phi^4$ quantum field theory including encoding, state preparation, and time evolution, with several numerical simulation results. These algorithms could be understood as near-term variational analogs of the Jordan-Lee-Preskill algorithm, the basic algorithm for simulating quantum field theory using universal quantum devices. Besides, we highlight the advantages of encoding with harmonic oscillator basis based on the LSZ reduction formula and several computational efficiency such as when implementing a bosonic version of the unitary coupled cluster ansatz to prepare initial states. We also discuss how to circumvent the "spectral crowding" problem in the quantum field theory simulation and appraise our algorithm by both state and subspace fidelities.Comment: 28 pages, many figures. v2: modified style, add references, clear typos. v3: significant change, authors adde

Perturbative quantum simulation

Approximations based on perturbation theory are the basis for most of the quantitative predictions of quantum mechanics, whether in quantum field theory, many-body physics, chemistry or other domains. Quantum computing provides an alternative to the perturbation paradigm, but the tens of noisy qubits currently available in state-of-the-art quantum processors are of limited practical utility. In this article, we introduce perturbative quantum simulation, which combines the complementary strengths of the two approaches, enabling the solution of large practical quantum problems using noisy intermediate-scale quantum hardware. The use of a quantum processor eliminates the need to identify a solvable unperturbed Hamiltonian, while the introduction of perturbative coupling permits the quantum processor to simulate systems larger than the available number of physical qubits. After introducing the general perturbative simulation framework, we present an explicit example algorithm that mimics the Dyson series expansion. We then numerically benchmark the method for interacting bosons, fermions, and quantum spins in different topologies, and study different physical phenomena on systems of up to $48$ qubits, such as information propagation, charge-spin separation and magnetism. In addition, we use 5 physical qubits on the IBMQ cloud to experimentally simulate the $8$-qubit Ising model using our algorithm. The result verifies the noise robustness of our method and illustrates its potential for benchmarking large quantum processors with smaller ones.Comment: 35 pages, 12 figure