Quantum Gates Research Articles

Classification of Pneumonia via a Hybrid ZFNet-Quantum Neural Network Using a Chest X-ray Dataset.

Deep neural networks (DNNs) have made significant contributions to diagnosing pneumonia from chest X-ray imaging. However, certain aspects of diagnosis and planning can be further enhanced through the implementation of a quantum deep neural network (QDNN). Therefore, we introduced a technique that integrates neural networks with quantum algorithms named the ZFNet-quantum neural network for detecting pneumonia using 5863 X-ray scans with binary cases. The hybrid model efficiently pre-processes complex and high-dimensional data by extracting significant features from the ZFNet model. These significant features are given to the quantum circuit algorithm and further embedded into a quantum device. The parameterized quantum circuit algorithm using qubits, superposition theorem, and entanglement phenomena generates 4 features from 4098 features extracted from images via a deep transfer learning model. Moreover, to validate the outcome measures of the proposed technique, we used various PennyLane quantum devices to detect pneumonia and normal control images. By using the Adam optimizer, which exploits an adaptive learning rate that is fixed to 10-6 and six layers of a quantum circuit composed of quantum gates, the proposed model achieves an accuracy of 96.5%, corresponding to 25 epochs. The integrated ZFNet-quantum learning network outperforms the deep transfer learning network in terms of testing accuracy, as the accuracy gained by the convolutional neural network (CNN) is 94%. Therefore, we use a hybrid classical-quantum model to detect pneumonia in which a variational quantum algorithm enhances the outcomes of a ZFNet transfer learning method. This approach is an efficient and automated method for detecting pneumonia and could significantly enhance outcome measures related to the speed and accuracy of the network in the clinical and healthcare sectors.

Error‐Heralded and Deterministic Interconversion Between W State and Greenberger–Horne–Zeilinger State with Faithful Quantum Gates in Decoherence‐Free‐Subspace

Error‐Heralded and Deterministic Interconversion Between W State and Greenberger–Horne–Zeilinger State with Faithful Quantum Gates in Decoherence‐Free‐Subspace

Implementing arbitrary multimode continuous-variable quantum gates with fixed non-Gaussian states and adaptive linear optics

Non-Gaussian quantum gates are essential components for optical quantum information processing. However, the efficient implementation of practically important multimode higher-order non-Gaussian gates has not been comprehensively studied. We propose a measurement-based method to directly implement general, multimode, and higher-order non-Gaussian gates using only fixed non-Gaussian ancillary states and adaptive linear optics. Compared to existing methods, our method allows for a more resource-efficient and experimentally feasible implementation of multimode gates that are important for various applications in optical quantum technology, such as the two-mode cubic quantum nondemolition gate or the three-mode continuous-variable Toffoli gate, and their higher-order extensions. Our results will expedite the progress toward fault-tolerant universal quantum computing with light. Published by the American Physical Society 2024

Extended quantum process tomography of logical operations on an encoded bosonic qubit

We demonstrate the use of coherent-state quantum process tomography (csQPT) for a bosonic-mode superconducting circuit. We have enhanced our methodology over previous implementations of csQPT by leveraging Kraus operators and constrained gradient descent to learn the underlying process. We show the results of our method by characterizing a logical quantum gate implemented using displacement and selective number-dependent arbitrary phase operations on an encoded qubit. Our use of csQPT allows for the reconstruction of Kraus operators for the larger Hilbert space rather than being limited to the logical subspace. This approach enables us to more accurately identify and quantify the various error mechanisms that can lead to gate infidelity, including those occurring outside of the computational subspace. We showcase the potential of our approach by demonstrating the ability to quantify leakage outside of the computational subspace, a key factor for developing more robust and reliable quantum gates in high-dimensional systems. Published by the American Physical Society 2024

Privacy-preserving keyword query quantum scheme for outsourced data in cloud environments

Cloud computing, as a popular technology in recent years, has greatly facilitated the development of data outsourcing services. However, when users access sensitive data stored in the cloud, ensuring the security of data remains a pressing challenge. In this paper, we present a privacy-preserving keyword query scheme for outsourced data in cloud environment. Furthermore, to implement this scheme, we propose a series of quantum basic protocols with single qubits. The proposed basic protocols do not require the execution of quantum gate operations, and the necessary measurements are only Bell measurements based on measurement-device-independence. Therefore, it is practical and feasible under current technology. Moreover, compared with classical schemes, our scheme has higher security (i.e., quantum security). Finally, we conduct simulation experiments in IBM Qiskit to verify the correctness and feasibility of the critical parts of the scheme.

Investigating and improving student understanding of the basics of quantum computing

Quantum information science and engineering (QISE) is a rapidly developing field that leverages the skills of experts from many disciplines to utilize the potential of quantum systems in a variety of applications. It requires talent from a wide variety of traditional fields, including physics, engineering, chemistry, and computer science, to name a few. To prepare students for such opportunities, it is important to give them a strong foundation in the basics of QISE, in which quantum computing plays a central role. In this study, we discuss the development, validation, and evaluation of a Quantum Interactive Learning Tutorial, on the basics and applications of quantum computing. These include an overview of key quantum mechanical concepts relevant to quantum computation (including ways a quantum computer is different from a classical computer), properties of single- and multiqubit systems, and the basics of single-qubit quantum gates. The tutorial uses guided inquiry-based teaching-learning sequences. Its development and validation involved conducting cognitive task analysis from both expert and student perspectives and using common student difficulties as a guide. For example, before engaging with the tutorial, after traditional lecture-based instruction, one reasoning primitive that was common in student responses is that a major difference between an N-bit classical and N-qubit quantum computer is that various things associated with a number N for a classical computer should be replaced with the number 2N for a quantum computer (e.g., 2N qubits must be initialized and 2N bits of information are obtained as the output of the computation on the quantum computer). This type of reasoning primitive also led many students to incorrectly think that there are only N distinctly different states available when computation takes place on a classical computer. Research suggests that this type of reasoning primitive has its origins in students learning that quantum computers can provide exponential advantage for certain problems, e.g., Shor’s algorithm for factoring products of large prime numbers, and that the quantum state during the computation can be in a superposition of 2N linearly independent states. The inquiry-based learning sequences in the tutorial provide scaffolding support to help students develop a functional understanding. The final version of the validated tutorial was implemented in two distinct courses offered by the physics department with slightly different student populations and broader course goals. Students’ understanding was evaluated after traditional lecture-based instruction on the requisite concepts and again after engaging with the tutorial. We analyze and discuss their improvement in performance on concepts covered in the tutorial. Published by the American Physical Society 2024

In situ characterization of qubit-drive phase distortions

Reducing errors in quantum gates is critical to the development of quantum computers. To do so, any distortions in the control signals should be identified; however, conventional tools are not always applicable when part of the system is under high vacuum, cryogenic, or microscopic. Here, we demonstrate a method to detect and compensate for amplitude-dependent phase changes, using the qubit itself as a probe. The technique is implemented using a microwave-driven trapped-ion qubit, where correcting phase distortions leads to a threefold improvement in the error of single-qubit gates implemented with pulses of different amplitudes, to attain state-of-the-art performance benchmarked at 1.6(4)×10−6 error per Clifford gate. Published by the American Physical Society 2024

Individually Addressed Quantum Gate Interactions Using Dynamical Decoupling

A leading approach to implementing small-scale quantum computers has been to use laser beams, focused to micron spot sizes, to address and entangle trapped ions in a linear crystal. Here we propose a method to implement individually addressed entangling gate interactions, but driven by microwave fields, with a spatial resolution of a few microns, corresponding to 10−5 microwave wavelengths. We experimentally demonstrate the ability to suppress the effect of the state-dependent force using a single ion, and find the required interaction introduces 3.7(4)×10−4 error per emulated gate in a single-qubit benchmarking sequence. We model the scheme for a 17-qubit ion crystal, and find that any pair of ions should be addressable with an average crosstalk error of approximately 10−5. Published by the American Physical Society 2024

Quantum‐Enhanced K‐Nearest Neighbors for Text Classification: A Hybrid Approach with Unified Circuit and Reduced Quantum Gates

Quantum‐Enhanced K‐Nearest Neighbors for Text Classification: A Hybrid Approach with Unified Circuit and Reduced Quantum Gates

Improved Entanglement‐Based High‐Dimensional Optical Quantum Computation with Linear Optics

AbstractQuantum gates are the essential block for quantum computers. High‐dimensional quantum gates exhibit remarkable advantages over their 2D counterparts for some quantum information processing tasks. Here, a family of entanglement‐based optical controlled‐SWAP gates on is presented. With the hybrid encoding, the control qubits and target qudits are encoded in photonic polarization and spatial degrees of freedom, respectively. The circuit is constructed using only () linear optics, beating an earlier result of 14 linear optics with . The circuit depth five is much lower than an earlier result of 11 with . Besides, the fidelity of the presented circuit can reach 99.4%, and it is higher than the previous counterpart with . The scheme is constructed in a deterministic way without any borrowed ancillary photons or measurement‐induced nonlinearities. Moreover, the approach allows .

Resource analysis of quantum algorithms for coarse-grained protein folding models

Protein folding processes are a vital aspect of molecular biology that is hard to simulate with conventional computers. Quantum algorithms have been proven superior for certain problems and may help tackle this complex life science challenge. We analyze the resource requirements for simulating simplified yet computationally challenging protein folding models on a quantum computer, assessing the feasibility of these existing approaches in the current and near-future technological landscape. We calculate the minimum number of qubits, interactions, and two-qubit gates necessary to build a heuristic quantum algorithm with the specific information of a folding problem. Particularly, we focus on the resources needed to build quantum operations based on the Hamiltonian linked to the protein folding models for a given amino acid count. Such operations are a fundamental component of these quantum algorithms, guiding the evolution of the quantum state for efficient computations. Specifically, we study coarse-grained folding models on the lattice and the fixed backbone side-chain conformation model and assess their compatibility with the constraints of existing quantum hardware given different bit encodings. We conclude that the number of qubits required falls within current technological capabilities. However, the limiting factor is the high number of interactions in the Hamiltonian, resulting in a quantum gate count unavailable today. Published by the American Physical Society 2024

Operating semiconductor quantum processors with hopping spins.

Qubits that can be efficiently controlled are essential for the development of scalable quantum hardware. Although resonant control is used to execute high-fidelity quantum gates, the scalability is challenged by the integration of high-frequency oscillating signals, qubit cross-talk, and heating. Here, we show that by engineering the hopping of spins between quantum dots with a site-dependent spin quantization axis, quantum control can be established with discrete signals. We demonstrate hopping-based quantum logic and obtain single-qubit gate fidelities of 99.97%, coherent shuttling fidelities of 99.992% per hop, and a two-qubit gate fidelity of 99.3%, corresponding to error rates that have been predicted to allow for quantum error correction. We also show that hopping spins constitute a tuning method by statistically mapping the coherence of a 10-quantum dot system. Our results show that dense quantum dot arrays with sparse occupation could be developed for efficient and high-connectivity qubit registers.

Q-Map: quantum circuit implementation of boolean functions

Quantum computing has gained attention in recent years due to the significant progress in quantum computing technology. Today many companies like IBM, Google and Microsoft have developed quantum computers and simulators for research and commercial use. The development of quantum techniques and algorithms is essential to exploit the full power of quantum computers. In this paper we propose a simple visual technique (we call Q-Map) for quantum realization of classical Boolean logic circuits. The proposed method utilizes concepts from Boolean algebra to produce a quantum circuit with minimal number of quantum gates.

Quantum gate control of polar molecules with machine learning.

We propose a scheme for achieving basic quantum gates using ultracold polar molecules in pendular states. The qubits are encoded in the YbF molecules trapped in an electric field with a certain gradient and coupled by the dipole-dipole interaction. The time-dependent control sequences consisting of multiple pulses are considered to interact with the pendular qubits. To achieve high-fidelity quantum gates, we map the control problem for the coupled molecular system into a Markov decision process and deal with it using the techniques of deep reinforcement learning (DRL). By training the agents over multiple episodes, the optimal control pulse sequences for the two-qubit gates of NOT, controlled NOT, and Hadamard are discovered with high fidelities. Moreover, the population dynamics of YbF molecules driven by the discovered gate sequences are analyzed in detail. Furthermore, by combining the optimal gate sequences, we successfully simulate the quantum circuit for entanglement. Our findings could offer new insights into efficiently controlling molecular systems for practical molecule-based quantum computing using DRL.

A deterministic qudit-dependent high-dimensional photonic quantum gate with weak cross-kerr nonlinearity

High-dimensional quantum systems expand the Hilbert space, enabling the processing of more intricate information and the execution of wider quantum operations. The development of high-dimensional quantum systems relies significantly on the implementation of high-dimensional gates. In the paper, we propose a deterministic 4 × 4-dimensional controlled-not (CNOT) gate for a three-photon system, where encoding on the polarization-spatial state of a single photon acts as a 4-dimensional control qudit, meanwhile the polarized state of the remaining two photons serves as a 4-dimensional target qudit. The implementation of the gate leverages weak cross-kerr nonlinearities and X-homodyne detectors, which impose lower requirements on the interaction strength and demonstrate robustness against photon loss. In accordance with the measurement of the coherent state, the relevant classical feed-forward operations are employed to make high-dimensional CNOT gate effective. Additionally, the proposed scheme demonstrates a high successful probability as a result of the utilization of mature measurement methods and straightforward optical elements. Further, the fidelity of the CNOT gate is robust against photon loss and is approximate to 1 with the existing technology. Consequently, our deterministic protocol presents a promising approach for the practical realization of high-dimensional quantum computation for photon systems.

Quanto: optimizing quantum circuits with automatic generation of circuit identities

Existing quantum compilers focus on mapping a logical quantum circuit to a quantum device and its native quantum gates. Only simple circuit identities are used to optimize the quantum circuit during the compilation process. This approach misses more complex circuit identities, which could be used to optimize the quantum circuit further. We propose Quanto, the first quantum optimizer that automatically generates circuit identities. Quanto takes as input a gate set and generates provably correct circuit identities for the gate set. Quanto’s automatic generation of circuit identities includes single-qubit and two-qubit gates, which leads to a new database of circuit identities, some of which are novel to the best of our knowledge. In addition to the generation of new circuit identities, Quanto’s optimizer applies such circuit identities to quantum circuits and finds optimized quantum circuits that have not been discovered by other quantum compilers, including IBM Qiskit and Cambridge Quantum Computing Tket. Quanto’s database of circuit identities could be applied to improve existing quantum compilers and Quanto can be used to generate identity databases for new gate sets.

A meta-trained generator for quantum architecture search

Variational Quantum Algorithms (VQAs) have made great success in the Noisy Intermediate-Scale Quantum (NISQ) era due to their relative resilience to noise and high flexibility relative to quantum resources. Quantum Architecture Search (QAS) aims to enhance the performance of VQAs by refining the structure of the adopted Parameterized Quantum Circuit (PQC). QAS is garnering increased attention owing to its automation, reduced reliance on expert experience, and its ability to achieve better performance while requiring fewer quantum gates than manually designed circuits. However, existing QAS algorithms optimize the structure from scratch for each VQA without using any prior experience, rendering the process inefficient and time-consuming. Moreover, determining the number of quantum gates, a crucial hyper-parameter in these algorithms is a challenging and time-consuming task. To mitigate these challenges, we accelerate the QAS algorithm via a meta-trained generator. The proposed algorithm directly generates high-performance circuits for a new VQA by utilizing a meta-trained Variational AutoEncoder (VAE). The number of quantum gates required in the designed circuit is automatically determined based on meta-knowledge learned from a variety of training tasks. Furthermore, we have developed a meta-predictor to filter out circuits with suboptimal performance, thereby accelerating the algorithm. Simulation results on variational quantum compiling and Quantum Approximation Optimization Algorithm (QAOA) demonstrate the superior performance of our method over a state-of-the-art algorithm, namely Differentiable Quantum Architecture Search (DQAS).

Adaptive Trotterization for Time-Dependent Hamiltonian Quantum Dynamics Using Piecewise Conservation Laws.

Digital quantum simulation relies on Trotterization to discretize time evolution into elementary quantum gates. On current quantum processors with notable gate imperfections, there is a critical trade-off between improved accuracy for finer time steps, and increased error rate on account of the larger circuit depth. We present an adaptive Trotterization algorithm to cope with time dependent Hamiltonians, where we propose a concept of piecewise "conserved" quantities to estimate errors in the time evolution between two (nearby) points in time; these allow us to bound the errors accumulated over the full simulation period. They reduce to standard conservation laws in the case of time independent Hamiltonians, for which we first developed an adaptive Trotterization scheme [H. Zhao et al., Making Trotterization adaptive and energy-self-correcting for NISQ devices and beyond, PRX Quantum 4, 030319 (2023).2691-339910.1103/PRXQuantum.4.030319]. We validate the algorithm for a time dependent quantum spin chain, demonstrating that it can outperform the conventional Trotter algorithm with a fixed step size at a controlled error.

Fast entangling quantum gates with almost-resonant modulated driving

Recently, the method of using a special category of synthetic analytical pulses under off-resonant conditions has improved the experimental performance of two- and multi-qubit gates in the cold atom qubit platform. In order to explore more possibilities and wider ranges of options, we design and analyze the method of almost-resonant modulated driving (ARMD) in constructing fast-speed and high-fidelity quantum logic gates. Whilst the modulation forms the key concept of high-fidelity Rydberg blockade gates so far, the on-off resonance condition can lead to nontrivial nuances in the styles of dynamics, and the ARMD gate protocols have its different characteristic mechanisms in quantum physics compared with the off-resonant gate processes. In particular, ARMD gates usually have abrupt phase changes and at certain points during the time evolution. From a more fundamental point of view, both the resonant and off-resonant methods all together belong to the unitary operation family of fast modulated driving with respect to precisely characterized inter-qubit interactions, which usually allows the quantum logic gate to conclude within one continuous pulse. We anticipate the ARMD gates to work well with the Rydberg dipole-dipole interaction and will serve as a helpful tool in the future studies of the cold atom qubit platform.

Robustness of dynamic quantum control: Differential sensitivity bounds

Dynamic control via optimized, piecewise-constant pulses is a common paradigm for open-loop control to implement quantum gates. While numerous methods exist for the synthesis of such controls, there are many open questions regarding the robustness of the resulting control schemes in the presence of model uncertainty; unlike in classical control, there are generally no analytical guarantees on the control performance with respect to inexact modeling of the system. In this paper, a new robustness measure based on the differential sensitivity of the gate fidelity error to parametric (structured) uncertainties is introduced, and bounds on the differential sensitivity to parametric uncertainties are used to establish performance guarantees for optimal controllers for a variety of quantum gate types, system sizes, and control implementations. Specifically, it is shown how a maximum allowable perturbation over a set of Hamiltonian uncertainties that guarantees a given fidelity error can be reliably computed. This measure of robustness is inversely proportional to the upper bound on the differential sensitivity of the fidelity error evaluated under nominal operating conditions. Finally, the results show that the nominal fidelity error and differential sensitivity upper bound are positively correlated across a wide range of problems and control implementations, suggesting that in the high-fidelity control regime, rather than there being a trade-off between fidelity and robustness, higher nominal gate fidelities are positively correlated with increased robustness of the controls in the presence of parametric uncertainties.