Publications

Convex optimization for non-equilibrium steady states on a hybrid quantum processor

Published in Physical Review Letters 130, 240601, 2023

Finding the transient and steady state properties of open quantum systems is a central problem in various fields of quantum technologies. Here, we present a quantum-assisted algorithm to determine the steady states of open system dynamics. By reformulating the problem of finding the fixed point of Lindblad dynamics as a feasibility semi-definite program, we bypass several well known issues with variational quantum approaches to solving for steady states. We demonstrate that our hybrid approach allows us to estimate the steady states of higher dimensional open quantum systems and discuss how our method can find multiple steady states for systems with symmetries.

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Atomtronic multi-terminal Aharonov-Bohm interferometer

Published in Physical Review A 107, L051303, 2023

Manipulating the flow of cold atoms promises practical devices for quantum technologies. Here, we study a three-terminal atomic ring circuit pierced by a synthetic magnetic flux as a powerful multi-functional device. The flux controls the transport through the ring via the Aharonov-Bohm effect. The device can measure the flux in-situ via a flux-induced transition of the reflections from negative Andreev-like to positive. By exciting the system we break an emergent symmetry of the flux. This allows us to direct the atomic current into specific output ports via the flux, realizing a flexible non-reciprocal switch to connect multiple atomic systems together. Our system allows us to study the time-dependent Aharonov-Bohm effect in a controllable manner. While the Aharonov-Bohm effect for time-dependent magnetic fields and electrons is an open problem, for cold atoms we observe a clear periodic modulation of the current with a time-dependent synthetic flux. This allows us to realize an atomic frequency generator as well as a converter between direct and alternating current. Our results can be directly implemented in experiments to create novel quantum technological devices.

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Molecular Property Prediction with Photonic Chip‐Based Machine Learning

Published in Laser and Photonics Reviews, 2022

Machine learning methods have revolutionized the discovery process of new molecules and materials. However, the intensive training process of neural networks for molecules with ever increasing complexity has resulted in exponential growth in computation cost, leading to long simulation time and high energy consumption. Photonic chip technology offers an alternative platform for implementing neural network with faster data processing and lower energy usage compared to digital computers. Here, we demonstrate the capability of photonic neural networks in predicting the quantum mechanical properties of molecules. Additionally, we show that multiple properties can be learned simultaneously in a photonic chip via a multi-task regression learning algorithm, which we believe is the first of its kind, as most previous works focus on implementing a network for the task of classification. Photonics technology are also naturally capable of implementing complex-valued neural networks at no additional hardware cost and we show that such neural networks outperform conventional real-valued networks for molecular property prediction. Our work opens the avenue for harnessing photonic technology for large-scale machine learning applications in molecular sciences such as drug discovery and materials design.

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NISQ computing: where are we and where do we go?

Published in AAPPS Bulletin, 2022

In this short review article, we aim to provide physicists not working within the quantum computing community a hopefully easy-to-read introduction to the state of the art in the field, with minimal mathematics involved. In particular, we focus on what is termed the Noisy Intermediate Scale Quantum era of quantum computing. We describe how this is increasingly seen to be a distinct phase in the development of quantum computers, heralding an era where we have quantum computers that are capable of doing certain quantum computations in a limited fashion, and subject to certain constraints and noise. We further discuss the prominent algorithms that are believed to hold the most potential for this era, and also describe the competing physical platforms on which to build a quantum computer that have seen the most success so far. We then talk about the applications that are most feasible in the near-term, and finish off with a short discussion on the state of the field. We hope that as non-experts read this article, it will give context to the recent developments in quantum computers that have garnered much popular press, and help the community understand how to place such developments in the timeline of quantum computing.

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NISQ algorithm for Hamiltonian simulation via truncated taylor series

Published in SciPost Physics 12 (4), 122, 2022

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 (TTQS), 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.

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Simulating Energy Transfer in Molecular Systems with Digital Quantum Computers

Published in Journal of Chemical Theory and Computation 18 (3), 1347-1358, 2022

Quantum computers have the potential to simulate chemical systems beyond the capability of classical computers. Recent developments in hybrid quantum-classical approaches enable the determinations of the ground or low energy states of molecular systems. Here, we extend near-term quantum simulations of chemistry to time-dependent processes by simulating energy transfer in organic semi-conducting molecules. We adopt an ab-initio exciton model built from the atomistic simulation of an N-molecule system. The exciton Hamiltonian is efficiently encoded in log2(N) qubits, and the dynamics is evolved using a time-dependent variational quantum algorithm. Our numerical examples demonstrate the feasibility of this approach, and simulations on IBM Q devices capture the qualitative behaviors of exciton dynamics but with considerable errors. We propose an error mitigation technique by combining experimental results from the variational and Trotter algorithms, and obtain significantly improved quantum dynamics. Our approach opens up new opportunities for modeling energy transfer in technologically relevant systems with quantum computers.

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Quantum assisted simulation of time dependent Hamiltonians

Published in Preprint on Arxiv, 2021

Quantum computers are expected to help us to achieve accurate simulation of the dynamics of many-body quantum systems. However, the limitations of current NISQ devices prevents us from realising this goal today. Recently an algorithm for performing quantum simulations called quantum assisted simulator has been proposed that promises realization on current experimental devices. In this work, we extend the quantum assisted simulator to simulate the dynamics of a class of time-dependent Hamiltonians. We show that the quantum assisted simulator is easier to implement as well as can realize multi-qubit interactions and challenging driving protocols that are difficult with other existing methods. We demonstrate this for a time-dependent Hamiltonian on the IBM Quantum Experience cloud quantum computer by showing superior performance of the quantum assisted simulator compared to Trotterization and variational quantum simulation. Our results indicate that quantum assisted simulator is a promising algorithm for current term quantum hardware.

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Fourth-order leapfrog algorithms for numerical time evolution of classical and quantum systems

Published in Preprint on Arxiv, 2020

Chau et al. [New J. Phys. 20, 073003 (2018)] presented a new and straight-forward derivation of a fourth-order approximation $U_7$ of the time-evolution operator and hinted at its potential value as a symplectic integrator. $U_7$ is based on the Suzuki-Trotter split-operator method and leads to an algorithm for numerical time propagation that is superior to established methods. We benchmark the performance of and other algorithms, including a Runge-Kutta method and another recently developed Suzuki-Trotter-based scheme, that are exact up to fourth order in the evolution parameter, against various classical and quantum systems. We find $U_7$ to deliver any given target accuracy with the lowest computational cost, across all systems and algorithms tested here. This study is accompanied by open-source numerical software that we hope will prove valuable in the classroom.

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Jonathan Lau Wei Zhong

Publications