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@article{nokey,

title = {Provable Advantage in Quantum Phase Learning via Quantum Kernel Alphatron},

author = {Yusen Wu and Bujiao Wu and Jingbo Wang and Xiao Yuan},

year  = {2023},

date = {2023-01-01},

journal = {arXiv:2111.07553},

abstract = {Can we use a quantum computer to speed up classical machine learning in solving problems of practical significance? Here, we study this open question focusing on the quantum phase learning problem, an important task in many-body quantum physics. We prove that, under widely believed complexity theory assumptions, quantum phase learning problem cannot be efficiently solved by machine learning algorithms using classical resources and classical data. Whereas using quantum data, we theoretically prove the universality of quantum kernel Alphatron in efficiently predicting quantum phases, indicating quantum advantages in this learning problem. We numerically benchmark the algorithm for a variety of problems, including recognizing symmetry-protected topological phases and symmetry-broken phases. Our results highlight the capability of quantum machine learning in efficient prediction of quantum phases.},

keywords = {},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{nokey,

title = {Quantum optimisation via maximally amplified states},

author = {Tavis Bennett and Jingbo Wang},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {arXiv:2111.00796},

keywords = {},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{nokey,

title = {Quantum Optimisation for Continuous Multivariable Functions by a Structured Search},

author = {Edric Matwiejew and Jason Pye and Jingbo B. Wang},

year  = {2023},

date = {2023-01-01},

journal = {arXiv:2210.06227},

abstract = {Solving optimisation problems is a promising near-term application of quantum computers. Quantum variational algorithms leverage quantum superposition and entanglement to optimise over exponentially large solution spaces using an alternating sequence of classically tunable unitaries. However, prior work has primarily addressed discrete optimisation problems. In addition, these algorithms have been designed generally under the assumption of an unstructured solution space, which constrains their speedup to the theoretical limits for the unstructured Grover's quantum search algorithm. In this paper, we show that quantum variational algorithms can efficiently optimise continuous multivariable functions by exploiting general structural properties of a discretised continuous solution space with a convergence that exceeds the limits of an unstructured quantum search. We introduce the Quantum Multivariable Optimisation Algorithm (QMOA) and demonstrate its advantage over pre-existing methods, particularly when optimising high-dimensional and oscillatory functions.},

keywords = {},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{nokey,

title = {Orbital Expansion Variational Quantum Eigensolver },

author = {Yusen Wu and Zigeng Huang and Jinzhao Sun and Xiao Yuan and Jingbo Wang and Dingshun Lv},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {https://arxiv.org/abs/2210.06897},

keywords = {},

pubstate = {forthcoming},

tppubtype = {article}

}

@article{PhysRevLett.128.050501,

title = {Deterministic Search on Star Graphs via Quantum Walks},

author = {Dengke Qu and Samuel Marsh and Kunkun Wang and Lei Xiao and Jingbo Wang and Peng Xue},

url = {https://link.aps.org/doi/10.1103/PhysRevLett.128.050501},

doi = {10.1103/PhysRevLett.128.050501},

year  = {2022},

date = {2022-08-01},

urldate = {2022-08-01},

journal = {Physical Review Letters (IF 9.161)},

volume = {128},

issue = {5},

pages = {050501},

publisher = {American Physical Society},

abstract = {We propose a novel algorithm for quantum spatial search on a star graph using interleaved continuous-time quantum walks and marking oracle queries. Initializing the system in the star’s central vertex, we determine the optimal quantum walk times to reach full overlap with the marked state using ⌈(π/4)√N−(1/2)⌉ oracle queries, matching the well-known lower bound of Grover’s search. We implement the deterministic search in a database of size seven on photonic quantum hardware, and demonstrate the effective scaling of the approach up to size 115. This is the first experimental demonstration of quantum walk-based search on the highly noise-resistant star graph, which provides new evidence for the applications of quantum walk in quantum algorithms and quantum information processing.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Estimating Gibbs partition function with quantum Clifford sampling},

author = { Yusen Wu and Jingbo Wang},

year  = {2022},

date = {2022-07-01},

urldate = {2022-01-01},

journal = {Quantum Science and Technology (IF 5.994)  },

volume = {7},

pages = {025006},

abstract = {The partition function is an essential quantity in statistical mechanics, and its accurate computation is a key component of any statistical analysis of quantum systems and phenomena. However, for interacting many-body quantum systems, its calculation generally involves summing over an exponential number of terms and can thus quickly grow to be intractable. Accurately and efficiently estimating the partition function of its corresponding system Hamiltonian then becomes the key in solving quantum many-body problems. In this paper we develop a hybrid quantum-classical algorithm to estimate the partition function, utilizing a novel Clifford sampling technique. Note that previous works on the estimation of partition functions require $mathcal{O}(1/epsilonsqrt{Delta})$-depth quantum circuits, where $Delta$ is the minimum spectral gap of stochastic matrices and $epsilon$ is the multiplicative error.  Our algorithm requires only a shallow $mathcal{O}(1)$-depth quantum circuit, repeated $mathcal{O}(n/epsilon^2)$ times, to provide a comparable $epsilon$ approximation. Shallow-depth quantum circuits are considered vitally important for currently available NISQ (Noisy Intermediate-Scale Quantum) devices.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {QuOp_MPI: A framework for parallel simulation of quantum variational algorithms},

author = {Edric Matwiejew and Jingbo Wang },

year  = {2022},

date = {2022-06-08},

urldate = {2022-06-08},

journal = {Journal of Computational Science (IF 3.817)},

volume = {62},

pages = {101711},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Quantum Walk-Based Vehicle Routing Optimisation},

author = {T. Bennett and E. Matwiejew and S. Marsh and J. B. Wang},

url = {https://www.frontiersin.org/articles/10.3389/fphy.2021.730856/full?utm_source=dlvr.it&utm_medium=twitter},

doi = {10.3389/fphy.2021.730856},

year  = {2021},

date = {2021-12-20},

urldate = {2021-12-20},

journal = {Frontiers in Physics (IF 3.563)},

volume = {9},

pages = {730856},

abstract = {This paper demonstrates the applicability of the Quantum Walk-based Optimisation Algorithm (QWOA) to the Capacitated Vehicle Routing Problem (CVRP). Efficient algorithms are developed for the indexing and un-indexing of the solution space and for implementing the required alternating phase-walk unitaries, which are the core components of QWOA. Results of numerical simulation demonstrate that the QWOA is capable of producing convergence to near-optimal solutions for a randomly generated eight location CVRP. Preparation of the amplified quantum state in this example problem is demonstrated to produce higher-quality solutions than expected from a classical random sampling of equivalent computational effort.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Marsh_2021,

title = {A framework for optimal quantum spatial search using alternating phase-walks},

author = {Samuel Marsh and Jingbo Wang},

url = {https://iopscience.iop.org/article/10.1088/2058-9565/ac26af},

doi = {10.1088/2058-9565/ac26af},

year  = {2021},

date = {2021-09-28},

urldate = {2021-09-28},

journal = {Quantum Science and Technology (IF 5.994)},

volume = {6},

number = {4},

pages = {045029},

publisher = {IOP Publishing},

abstract = {We present a novel methodological framework for quantum spatial search, generalising the Childs & Goldstone () algorithm via alternating applications of marked-vertex phase shifts and continuous-time quantum walks. We determine closed form expressions for the optimal walk time and phase shift parameters for periodic graphs. These parameters are chosen to rotate the system about subsets of the graph Laplacian eigenstates, amplifying the probability of measuring the marked vertex. The state evolution is asymptotically optimal for any class of periodic graphs having a fixed number of unique eigenvalues. We demonstrate the effectiveness of the algorithm by applying it to obtain search on a variety of graphs. One important class is the n × n 3 rook graph, which has N = n 4 vertices. On this class of graphs the algorithm performs suboptimally, achieving only overlap after time . Using the new alternating phase-walk framework, we show that overlap is obtained in phase-walk iterations.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}