arXiv subjects

Zhaokai Li

Publications and source records attributed to Zhaokai Li.

At least 19 recordsLinked to original sources

arXiv2026

Quantum neural network equipped with backpropagation on a qudit processor

Quantum neural networks (QNNs), one of the fundamental algorithms in quantum machine learning, have been widely used in classification and identification tasks. However, the capabilities of QNNs are constrained by their size, which is determined by the dimension of the Hilbert space of the underlying quantum processor. Multi-level quantum digits (qudits) offer access to a higher-dimensional Hilbert space compared to two-level qubits, enabling the construction of more expressive QNNs. In this work, we report an experimental demonstration of qudit-based QNN using a trapped $\rm ^{40}Ca^+$ ion. We train the QNN using a hybrid quantum-classical implementation of backpropagation and achieve an experimental classification accuracy of $95.7\%$ on a test image set. This demonstration highlights the potential of qudit-based processors to QNN architectures and provides a framework for implementing qudit-based QNNs across various quantum devices.

quant-ph↗

arXiv2026

A New Branching Bisimulation for Probabilistic Processes

We introduce a new branching bisimulation for probabilistic processes, which induces a more refined equivalence relation than any known equivalence that abstracts from unobservable actions, with a rooted version that is a congruence for a language of probabilistic process with the usual static as well as dynamic constructs including recursion.

cs.LO↗

arXiv2026

High-Accuracy Temporal Prediction via Experimental Quantum Reservoir Computing in Correlated Spins

Physical reservoir computing provides a powerful machine learning paradigm that exploits nonlinear physical dynamics for efficient information processing. By incorporating quantum effects, quantum reservoir computing offers superior potential for machine learning applications, as quantum dynamics are exponentially costly to simulate classically. Here, we present a novel quantum reservoir computing approach based on correlated quantum spin systems, exploiting natural quantum many-body interactions to generate reservoir dynamics, thereby circumventing the practical challenges of deep quantum circuits. Our experimental implementation supports nontrivial quantum entanglement and exhibits sufficient dynamical complexity for high-performance machine learning. We achieve state-of-the-art performance in experiments on standard time-series benchmarks, reducing prediction error by 1 to 2 orders of magnitude compared to previous quantum reservoir experiments. In long-term weather forecasting, our 9-spin quantum reservoir delivers greater prediction accuracy than classical reservoirs with thousands of nodes. This represents the first experimental demonstration of quantum machine learning outperforming large-scale classical models on real-world tasks.

quant-ph↗

arXiv2025

Phase field simulation of dendrite growth in solid-state lithium batteries based on mechanical-thermo-electrochemical coupling

Solid-state lithium batteries possess numerous advantages, such as high energy density, excellent cycle stability, superior mechanical strength, non-flammability, enhanced safety, and extended service life. These characteristics make them highly suitable for applications in aerospace, new energy vehicles, and portable electronic devices. However, the growth of lithium dendrite at the electrode/electrolyte interface remains a critical challenge, limiting both performance and safety. The growth of lithium dendrites in the electrolyte not only reduces the Coulombic efficiency of the battery but also poses a risk of puncturing the electrolyte, leading to internal short circuits between the anode and cathode. This study is to solve the problem of lithium dendrite growth in solid-state lithium batteries by employing phase-field theory for numerical simulations. A phase-field model is developed by coupling the mechanical stress field, thermal field, and electrochemical field, to investigate the morphology and evolution of lithium dendrites under the condition of different ambient temperatures, external pressures, and their combined effects. The results indicate that higher temperature and greater external pressure significantly suppress lithium dendrite growth, leading to fewer side branches, smoother surfaces, and more uniform electrochemical deposition. Increased external pressure inhibits longitudinal dendrite growth, resulting in a compressed morphology with higher compactness, but at the cost of increased mechanical instability. The combined effect of temperature and pressure exhibits a pronounced inhibitory influence on dendrite growth, with stress concentrating at the dendrite roots. This stress distribution promotes lateral growth, facilitating the formation of flatter and denser lithium deposits.

cond-mat.mtrl-sci↗

arXiv2025

Variational Quantum Metrology with Loschmidt Echo

By utilizing quantum mechanical effects, such as superposition and entanglement, quantum metrology promises higher precision than the classical strategies. It is, however, practically challenging to realize the quantum advantages. This is mainly due to the difficulties in engineering non-classical probe state and performing nontrivial measurement in practise, particularly with a large number of particles. Here we propose a scalable scheme with a symmetrical variational quantum circuit which, same as the Loschmidt echo, consists of a forward and a backward evolution. We show that in this scheme the quantum Fisher information, which quantifies the precision limit, can be efficiently obtained from a measurement signal of the Loschmidt echo. We experimentally implement the scheme on an ensemble of 10-spin quantum processor and successfully achieves a precision near the theoretical limit which outperforms the standard quantum limit with 12.4 dB. The scheme can be efficiently implemented on various noisy intermediate-scale quantum devices which provides a promising routine to demonstrate quantum advantages.

quant-ph↗

arXiv2024

Quantum Anomaly Detection with a Spin Processor in Diamond

In the processing of quantum computation, analyzing and learning the pattern of the quantum data are essential for many tasks. Quantum machine learning algorithms can not only deal with the quantum states generated in the preceding quantum procedures, but also the quantum registers encoding classical problems. In this work, we experimentally demonstrate the anomaly detection of quantum states encoding audio samples with a three-qubit quantum processor consisting of solid-state spins in diamond. By training the quantum machine with a few normal samples, the quantum machine can detect the anomaly samples with a minimum error rate of 15.4%. These results show the power of quantum anomaly detection in dealing with machine learning tasks and the potential to detect abnormal output of quantum devices.

quant-ph↗

arXiv2022

Optimal and robust experiment design for quantum state tomography of star-topology register

While quantum state tomography plays a vital role in the verification and benchmarking of quantum systems, it is an intractable task if the controllability and measurement of quantum registers are constrained. In this paper, we study the quantum state tomography of star-topology registers, in which the individual addressability of peripheral spins is infeasible. Based on the star-symmetry, we decompose the Hilbert space to alleviate the complexity of tomography and design a compact strategy with minimum number of measurements. By optimizing the parameterized quantum circuit for information transfer, the robustness against measurement errors is also improved. Furthermore, we apply this method to a 10-spin star-topology register and demonstrate its ability to characterize large-scale systems. Our results can help future investigations of quantum systems with constrained ability of quantum control and measurement.

quant-ph↗

arXiv2022

Resonant Quantum Principal Component Analysis

Principal component analysis has been widely adopted to reduce the dimension of data while preserving the information. The quantum version of PCA (qPCA) can be used to analyze an unknown low-rank density matrix by rapidly revealing the principal components of it, i.e. the eigenvectors of the density matrix with largest eigenvalues. However, due to the substantial resource requirement, its experimental implementation remains challenging. Here, we develop a resonant analysis algorithm with the minimal resource for ancillary qubits, in which only one frequency scanning probe qubit is required to extract the principal components. In the experiment, we demonstrate the distillation of the first principal component of a 4$\times$4 density matrix, with the efficiency of 86.0% and fidelity of 0.90. This work shows the speed-up ability of quantum algorithm in dimension reduction of data and thus could be used as part of quantum artificial intelligence algorithms in the future.

quant-ph↗

arXiv2019

Experimental Cryptographic Verification for Near-Term Quantum Cloud Computing

Recently, there are more and more organizations offering quantum-cloud services, where any client can access a quantum computer remotely through the internet. In the near future, these cloud servers may claim to offer quantum computing power out of reach of classical devices. An important task is to make sure that there is a real quantum computer running, instead of a simulation by a classical device. Here we explore the applicability of a cryptographic verification scheme that avoids the need of implementing a full quantum algorithm or requiring the clients to communicate with quantum resources. In this scheme, the client encodes a secret string in a scrambled IQP (instantaneous quantum polynomial) circuit sent to the quantum cloud in the form of classical message, and verify the computation by checking the probability bias of a class of output strings generated by the server. We provided a theoretical extension and implemented the scheme on a 5-qubit NMR quantum processor in the laboratory and a 5-qubit and 16-qubit processors of the IBM quantum cloud. We found that the experimental results of the NMR processor can be verified by the scheme with about $2.5\%$ error, after noise compensation by standard techniques. However, the fidelity of the IBM quantum cloud is currently too low to pass the test (about $42\%$ error). This verification scheme shall become practical when servers claim to offer quantum-computing resources that can achieve quantum supremacy.

quant-ph↗

arXiv2019

Quantum Simulation of Resonant Transitions for Solving the Eigen-problem of an Effective Water Hamiltonian

It is difficult to calculate the energy levels and eigenstates of a large physical system on a classical computer because of the exponentially growing size of the Hilbert space. In this work, we experimentally demonstrate a quantum algorithm which could solve this problem via simulated resonant transitions. Using a four-qubit quantum simulator in which two qubits are used as ancillas for control and measurement, we obtain the energy spectrum of a 2-qubit low-energy effective Hamiltonian of the water molecule. The simulated transitions allow the state of the quantum simulator to transform and access large regions of the Hilbert space, including states that have no overlap with the initial state. Furthermore, we make use of this algorithm to efficiently prepare specific eigenstates on the simulator according to the measured eigenenergies.

quant-ph↗

arXiv2018

Experimental Preparation of Topologically Ordered States via Adiabatic Evolution

Topological orders are a class of exotic states of matter characterized by patterns of long-range entanglement. Certain topologically ordered systems are proposed as potential realization of fault-tolerant quantum computation. Topological orders can arise in two-dimensional spin-lattice models. In this paper, we engineer a time-dependent Hamiltonian to prepare a topologically ordered state through adiabatic evolution. The other sectors in the degenerate ground-state space of the model are obtained by applying nontrivial operations corresponding to closed string operators. Each sector is highly entangled, as shown from the completely reconstructed density matrices. This paves the way towards exploring the properties of topological orders and the application of topological orders in topological quantum memory.

quant-ph↗

arXiv2018

Quantum speed-up in solving the maximal clique problem

The maximal clique problem, to find the maximally sized clique in a given graph, is classically an NP-complete computational problem, which has potential applications ranging from electrical engineering, computational chemistry, bioinformatics to social networks. Here we develop a quantum algorithm to solve the maximal clique problem for any graph $G$ with $n$ vertices with quadratic speed-up over its classical counterparts, where the time and spatial complexities are reduced to, respectively, $O(\sqrt{2^{n}})$ and $O(n^{2})$. With respect to oracle-related quantum algorithms for the NP-complete problems, we identify our algorithm to be optimal. To justify the feasibility of the proposed quantum algorithm, we have successfully solved an exemplified clique problem for a graph $G$ with two vertices and one edge by carrying out a nuclear magnetic resonance experiment involving four qubits.

quant-ph↗

arXiv2017

Uncertainty conservation relations: theory and experiment

As a very fundamental principle in quantum physics, uncertainty principle has been studied intensively via various uncertainty inequalities. Based on the information measure introduced by Brukner and Zeilinger in [Phys. Rev. Lett. 83, 3354 (1999)], we derive an uncertainty conservation relation in the presence of quantum memory. We show that the sum of measurement uncertainties over a complete set of mutually unbiased bases on a subsystem is equal to a total uncertainty determined by the initial bipartite state. Hence for a given bipartite system the uncertainty is conserved and the uncertainty relation is given by an equality. When the total uncertainty vanishes, all the uncertainties related to every mutually unbiased base measurement are zero, which is substantially different from the uncertainty relation given by Berta et. al. [Nat. Phys. 6, 659 (2010)] where an uncertainty inequality is presented. By directly measuring the corresponding quantum mechanical observables on a 5-qubit spin system, we experimentally verify the uncertainty conservation relation in the presence of quantum memory without quantum state tomography.

quant-ph↗

arXiv2017

Experimentally Probing Topological Order and Its Breakdown via Modular Matrices

The modern conception of phases of matter has undergone tremendous developments since the first observation of topologically ordered states in fractional quantum Hall systems in the 1980s. In this paper, we explore the question: How much detail of the physics of topological orders can in principle be observed using state of the art technologies? We find that using surprisingly little data, namely the toric code Hamiltonian in the presence of generic disorders and detuning from its exactly solvable point, the modular matrices -- characterizing anyonic statistics that are some of the most fundamental finger prints of topological orders -- can be reconstructed with very good accuracy solely by experimental means. This is a first experimental realization of these fundamental signatures of a topological order, a test of their robustness against perturbations, and a proof of principle -- that current technologies have attained the precision to identify phases of matter and, as such, probe an extended region of phase space around the soluble point before its breakdown. Given the special role of anyonic statistics in quantum computation, our work promises myriad applications both in probing and realistically harnessing these exotic phases of matter.

quant-ph↗

arXiv2017

High-fidelity adiabatic quantum computation using the intrinsic Hamiltonian of a spin system: Application to the experimental factorization of 291311

In previous implementations of adiabatic quantum algorithms using spin systems, the average Hamiltonian method with Trotter's formula was conventionally adopted to generate an effective instantaneous Hamiltonian that simulates an adiabatic passage. However, this approach had issues with the precision of the effective Hamiltonian and with the adiabaticity of the evolution. In order to address these, we here propose and experimentally demonstrate a novel scheme for adiabatic quantum computation by using the intrinsic Hamiltonian of a realistic spin system to represent the problem Hamiltonian while adiabatically driving the system by an extrinsic Hamiltonian directly induced by electromagnetic pulses. In comparison to the conventional method, we observed two advantages of our approach: improved ease of implementation and higher fidelity. As a showcase example of our approach, we experimentally factor 291311, which is larger than any other quantum factorization known.

quant-ph↗

arXiv2016

Scalable quantum computation based on quantum actuated nuclear-spin decoherence-free qubits

We propose a novel architecture for scalable quantum computation based on quantum actuated decoherence-free (DF) qubits. Each qubit is encoded by the DF subspace of a nuclear spin pair and has long coherence time. A nitrogen-vacancy center in diamond is chosen as the quantum actuator to realize initialization, readout and universal control of DF qubits with fidelities higher than 99%. It reduces the challenge of classical interfaces from controlling and observing complex quantum systems down to a simple quantum actuator. Our scheme also provides a novel way to handle complex quantum systems.

quant-ph↗

arXiv2016

Experimental Adiabatic Quantum Factorization under Ambient Conditions Based on a Solid-State Single Spin System

The adiabatic quantum computation is a universal and robust method of quantum computing. In this architecture, the problem can be solved by adiabatically evolving the quantum processor from the ground state of a simple initial Hamiltonian to that of a final one, which encodes the solution of the problem. By far, there is no experimental realization of adiabatic quantum computation on a single solid spin system under ambient conditions, which has been proved to be a compatible candidate for scalable quantum computation. In this letter, we report on the first experimental realization of an adiabatic quantum algorithm on a single solid spin system under ambient conditions. All elements of adiabatic quantum computation, including initial state preparation, adiabatic evolution, and final state readout, are realized experimentally. As an example, we factored 35 into its prime factors 5 and 7 on our adiabatic quantum processor.

quant-ph↗

arXiv2016

Experimental test of Heisenberg's measurement uncertainty relation based on statistical distances

Incompatible observables can be approximated by compatible observables in joint measurement or measured sequentially, with constrained accuracy as implied by Heisenberg's original formulation of the uncertainty principle. Recently, Busch, Lahti, and Werner proposed inaccuracy trade-off relations based on statistical distances between probability distributions of measurement outcomes [Phys. Rev. Lett. 111, 160405 (2013); Phys. Rev. A 89, 012129 (2014)]. Here we reform their theoretical framework, derive an improved relation for qubit measurement, and perform an experimental test on a spin system. The relation reveals that the worst-case inaccuracy is tightly bounded from below by the incompatibility of target observables, and is verified by the experiment employing joint measurement in which two compatible but typically non-commutative observables on one qubit are measured simultaneously.

quant-ph↗