Kwek, Leong Chuan

Simulating the dynamics of many-body quantum systems is believed to be one of the first fields that quantum computers can show a quantum advantage over classical computers. Noisy intermediate-scale quantum (NISQ) algorithms aim at effectively using the currently available quantum hardware. For quantum simulation, various types of NISQ algorithms have been proposed with individual advantages as well as challenges. In this work, we propose a new algorithm, truncated Taylor quantum simulator (TQS), that shares the advantages of existing algorithms and alleviates some of the shortcomings. Our algorithm does not have any classical-quantum feedback loop and bypasses the barren plateau problem by construction. The classical part in our hybrid quantum-classical algorithm corresponds to a quadratically constrained quadratic program (QCQP) with a single quadratic equality constraint, which admits a semidefinite relaxation. The QCQP based classical optimization was recently introduced as the classical step in quantum assisted eigensolver (QAE), a NISQ algorithm for the Hamiltonian ground state problem. Thus, our work provides a conceptual unification between the NISQ algorithms for the Hamiltonian ground state problem and the Hamiltonian simulation. We recover differential equation-based NISQ algorithms for Hamiltonian simulation such as quantum assisted simulator (QAS) and variational quantum simulator (VQS) as particular cases of our algorithm. We test our algorithm on some toy examples on current cloud quantum computers. We also provide a systematic approach to improve the accuracy of our algorithm.

We study the ground state of a bosonic ring ladder under a gauge flux in the vortex phase, corresponding to the case where the single-particle dispersion relation has two degenerate minima. By combining exact diagonalization and an approximate fermionization approach we show that the ground state of the system evolves from a fragmented state of two single-particle states at weak interparticle interactions to a fragmented state of two Fermi seas at large interactions. Fragmentation is inferred from the study of the eigenvalues of the reduced single-particle density matrix as well as from the calculation of the fidelity of the states. We characterize these nonclassical states by the momentum distribution, the chiral currents, and the current-current correlations.

A universal fault-tolerant quantum computer that can efficiently solve problems such as integer factorization and unstructured database search requires millions of qubits with low error rates and long coherence times. While the experimental advancement toward realizing such devices will potentially take decades of research, noisy intermediate-scale quantum (NISQ) computers already exist. These computers are composed of hundreds of noisy qubits, i.e., qubits that are not error corrected, and therefore perform imperfect operations within a limited coherence time. In the search for achieving quantum advantage with these devices, algorithms have been proposed for applications in various disciplines spanning physics, machine learning, quantum chemistry, and combinatorial optimization. The overarching goal of such algorithms is to leverage the limited available resources to perform classically challenging tasks. In this review, a thorough summary of NISQ computational paradigms and algorithms is provided. The key structure of these algorithms and their limitations and advantages are discussed. A comprehensive overview of various benchmarking and software tools useful for programming and testing NISQ devices is additionally provided.

Simulating quantum dynamics is expected to be performed more easily on a quantum computer than on a classical computer. However, the currently available quantum devices lack the capability to implement fault-tolerant quantum algorithms for quantum simulation. Hybrid classical quantum algorithms such as the variational quantum algorithms have been proposed to effectively use current term quantum devices. One promising approach to quantum simulation in the noisy intermediate-scale quantum (NISQ) era is the diagonalisation based approach, with some of the promising examples being the subspace variational quantum simulator (SVQS), variational fast forwarding (VFF), fixed-state variational fast forwarding (fs-VFF), and the variational Hamiltonian diagonalisation (VHD) algorithms. However, these algorithms require a feedback loop between the classical and quantum computers, which can be a crucial bottleneck in practical application. Here, we present the classical quantum fast forwarding (CQFF) as an alternative diagonalisation based algorithm for quantum simulation. CQFF shares some similarities with SVQS, VFF, fs-VFF and VHD but removes the need for a classical-quantum feedback loop and controlled multi-qubit unitaries. The CQFF algorithm does not suffer from the barren plateau problem and the accuracy can be systematically increased. Furthermore, if the Hamiltonian to be simulated is expressed as a linear combination of tensored-Pauli matrices, the CQFF algorithm reduces to the task of sampling some many-body quantum state in a set of Pauli-rotated bases, which is easy to do in the NISQ era. We run the CQFF algorithm on existing quantum processors and demonstrate the promise of the CQFF algorithm for current-term quantum hardware. We compare CQFF with Trotterization for a XY spin chain model Hamiltonian and find that the CQFF algorithm can simulate the dynamics more than 105 times longer than Trotterization on current-term quantum hardware. This provides a 104 times improvement over the previous record.

Quantum autoencoders serve as efficient means for quantum data compression. Here, we propose and demonstrate their use to reduce resource costs for quantum teleportation of subspaces in high-dimensional systems. We use a quantum autoencoder in a compress-teleport-decompress manner and report the first demonstration with qutrits using an integrated photonic platform for future scalability. The key strategy is to compress the dimensionality of input states by erasing redundant information and recover the initial states after chip-to-chip teleportation. Unsupervised machine learning is applied to train the on-chip autoencoder, enabling the compression and teleportation of any state from a high-dimensional subspace. Unknown states are decompressed at a high fidelity (~0.971), obtaining a total teleportation fidelity of ~0.894. Subspace encodings hold great potential as they support enhanced noise robustness and increased coherence. Laying the groundwork for machine learning techniques in quantum systems, our scheme opens previously unidentified paths toward high-dimensional quantum computing and networking.

A Bose-Einstein condensate confined in ring shaped lattices interrupted by a weak link and pierced by an effective magnetic flux defines the atomic counterpart of the superconducting quantum interference device: the atomtronic quantum interference device (AQUID). In this paper, we report on the detection of current states in the system through a self-heterodyne protocol. Following the original proposal of the NIST and Paris groups, the ring-condensate many-body wave function interferes with a reference condensate expanding from the center of the ring. We focus on the rf AQUID which realizes effective qubit dynamics. Both the Bose-Hubbard and Gross-Pitaevskii dynamics are studied. For the Bose-Hubbard dynamics, we demonstrate that the self-heterodyne protocol can be applied, but higher-order correlations in the evolution of the interfering condensates are measured to readout of the current states of the system. We study how states with macroscopic quantum coherence can be told apart analyzing the noise in the time of flight of the ring condensate.

We study a multifunctional device for cold atoms consisting of a three-terminal ring circuit pierced by a synthetic magnetic flux, where the ring can be continuous or discretized. The flux controls the atomic current through the ring via the Aharonov-Bohm effect. Our device shows a flux-induced transition of reflections from an Andreev-like negative density to positive density. Further, the flux can direct the atomic current into specific output ports, realizing a flexible nonreciprocal switch to connect multiple atomic systems or sense rotations. By changing the flux linearly in time, we convert constant matter wave currents into an ac modulated current. This effect can be used to realize an atomic frequency generator and study fundamental problems related to the Aharonov-Bohm effect. We experimentally demonstrate Bose-Einstein condensation into the light-shaped optical potential of the three-terminal ring. Our work opens up the possibility of atomtronic devices for practical applications in quantum technologies.

We propose a scheme to perform stroboscopic Hamiltonian engineering in the low frequency regime using a quantum system with one-dimensional nearest-neighbor coupling that are commonly available in the NISQ era. Computational problems are encoded in the effective Hamiltonian of the quantum systems under the effect of external driving. Our approach is nonperturbative and it does not rely on high-frequency expansions, which are a common tool in Floquet engineering. In our paper, the effective Hamiltonian that we want to engineer is fully tailored through designing the periodic driving. We illustrate how this quantum computation proceeds with two examples, an instance from the 3-SAT problem and the LiH molecule quantum chemistry simulation. In the case of the 3-SAT Hamiltonian, we show that by starting from the ground state of the trivial Hamiltonian, the quantum systems go through an adiabatic process in the stroboscopic picture towards the target Hamiltonian of the problem.

Reducing noise in quantum systems is a significant challenge in advancing quantum technologies. We propose and demonstrate a noise reduction scheme utilizing a quantum autoencoder, which offers rigorous performance guarantees. The quantum autoencoder is trained to compress noisy quantum states into a latent subspace and eliminate noise through projective measurements. We identify various noise models in which the noiseless state can be perfectly reconstructed, even at high noise levels. We apply the autoencoder to cool thermal states to the ground state and reduce the cost of magic state distillation by several orders of magnitude. Our autoencoder can be implemented using only unitary transformations without the need for ancillas, making it immediately compatible with state-of-the-art quantum technologies. We experimentally validate our noise reduction methods in a photonic integrated circuit. Our results have direct applications in enhancing the robustness of quantum technologies against noise.