The QUISA (Quantum Information, Simulation and Algorithms) Research Centre, hosted at The University of Western Australia, aims to foster collaboration and entrepreneurship, bringing together academic staff, research students, government and industrial partners to develop innovative quantum solutions to tackle otherwise intractable problems and complex phenomena.

@article{nokey,

title = {Experimental implementation of quantum-walk-based portfolio optimisation},

author = {Dengke Qu and Edric Matwiejew and Kunkun Wang and Jingbo Wang and Peng Xue},

doi = {10.1088/2058-9565/ad27e9},

year  = {2024},

date = {2024-02-23},

urldate = {2024-02-01},

journal = {Quantum Science and Technology (IF 6.568)  },

volume = {9},

pages = {025014},

abstract = {The application of quantum algorithms has attracted much attention as it holds the promise of solving practical problems that are intractable to classical algorithms. One such application is the recent development of a quantum-walk-based optimization algorithm approach to portfolio optimization under the modern portfolio theory framework. In this paper, we demonstrate an experimental realization of the alternating phase-shift and continuous-time quantum walk unitaries that underpin this quantum algorithm using optical networks and single photons. The experimental analysis confirms that the probability of states corresponding to high-quality solutions is efficiently amplified by increasing the number of phase-shift and quantum walk iterations. This work provides strong evidence for practical applications of quantum-walk-based algorithms such as financial portfolio optimization.},

keywords = {},

pubstate = {published},

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 = {2023-09-27},

urldate = {2023-01-01},

journal = {Quantum Science and Technology (Impact Factor 6.568)},

volume = {8},

pages = {045030},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Quantum Phase Recognition via Quantum Kernel Methods},

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

year  = {2023},

date = {2023-03-01},

urldate = {2023-03-01},

journal = {Quantum (Impact Factor 6.777)},

volume = {7},

pages = {981},

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 = {published},

tppubtype = {article}

}

@article{nokey,

title = {The first Workshop on Quantum in Consumer Technology at IEEE Quantum Week},

author = {Sotelo, Rafael and Wang, Jingbo and Nakamura, Yuichi and Farouk, Ahmed and Arjona, Rosario and Andraca, Salvador Venegas and James, Alex and Venegas-Gomez, Araceli and Gonzalez, Bill},

doi = {10.1109/MCE.2023.3243380},

year  = {2023},

date = {2023-02-01},

urldate = {2023-02-01},

journal = {IEEE Consumer Electronics Magazine (Impact Factor 4.135)},

volume = {2162},

pages = {2256},

keywords = {},

pubstate = {published},

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},

doi = {10.1088/2058-9565/ace6cc},

year  = {2023},

date = {2023-01-10},

urldate = {2023-01-10},

journal = {Quantum Science and Technology (Impact Factor 6.568) - In Press (formally accepted)  },

volume = {8},

pages = {045013},

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 = {published},

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{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 = {2022-07-01},

journal = {Quantum Science and Technology (IF 6.568)  },

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}

}