Quantum States Research Articles

Variable hyperparameterized Gaussian kernel using displaced squeezed vacuum state

There are machine learning schemes for realizing different types of kernels by quantum states of light. It is particularly interesting to realize a Gaussian kernel due to its wider applicability. A multimode coherent state can generate the Gaussian kernel with a constant value of hyperparameter. However, this constancy has limited the application of the Gaussian kernel when it is applied to complex learning problems. We improve it by introducing a variable hyperparameterized Gaussian kernel with a multimode-displaced squeezed vacuum state. The learning capacity of this kernel is tested with the support vector machines over some synthesized data sets, as well as public benchmark data sets. We establish that the proposed variable hyperparameterized Gaussian kernel offers better accuracy over the constant Gaussian kernel.

Quantum state preparation of topological chiral spin liquids via Floquet engineering

Quantum state preparation of topological chiral spin liquids via Floquet engineering

Isotope interface engineering for thermal transport suppression in cryogenic graphene

The development of emerging technologies, such as quantum computing and semiconductor electronics, emphasizes the growing significance of thermal management at cryogenic temperatures. Herein, by designing isotope interfaces based on the Golomb ruler, we achieved effective suppression of the phonon thermal transport of cryogenic graphene. The pronounced disordering of the Golomb ruler sequence results in the stronger suppression of thermal transport compared to other sequences with the same isotope doping ratio. We demonstrated that the Golomb ruler-based isotope interfaces have strong scattering and confinement effects on phonon transport via extensive molecular dynamics simulations combined with wave packet analysis, with a proper correction for the missing quantum statistics. This work provides a new stream for the design of thermal transport suppression under cryogenic conditions and is expected to expand to other fields.

Matrix product state approximations to quantum states of low energy variance

We show how to efficiently simulate pure quantum states in one dimensional systems that have both finite energy density and vanishingly small energy fluctuations. We do so by studying the performance of a tensor network algorithm that produces matrix product states whose energy variance decreases as the bond dimension increases. Our results imply that variances as small as ∝1/log⁡N can be achieved with polynomial bond dimension. With this, we prove that there exist states with a very narrow support in the bulk of the spectrum that still have moderate entanglement entropy, in contrast with typical eigenstates that display a volume law. Our main technical tool is the Berry-Esseen theorem for spin systems, a strengthening of the central limit theorem for the energy distribution of product states. We also give a simpler proof of that theorem, together with slight improvements in the error scaling, which should be of independent interest.

Versatile Fidelity Estimation with Confidence.

As quantum devices become more complex and the requirements on these devices become more demanding, it is crucial to be able to verify the performance of such devices in a scalable and reliable fashion. A cornerstone task in this challenge is quantifying how close an experimentally prepared quantum state is to the desired one. Here we present a method to construct an estimator for the quantum state fidelity that is compatible with any measurement protocol. Our method provides a confidence interval on this estimator that is guaranteed to be nearly minimax optimal for the specified measurement protocol. For a well-chosen measurement scheme, our method is competitive in the number of measurement outcomes required for estimation. We demonstrate our method using simulations and experimental data from a trapped-ion quantum computer and compare the results to state-of-the-art techniques. Our method can be easily extended to estimate the expectation value of any observable, such as entanglement witnesses.

Quantum Tensor-Product Decomposition from Choi-State Tomography

The Schmidt decomposition is the go-to tool for measuring bipartite entanglement of pure quantum states. Similarly, it is possible to study the entangling features of a quantum operation using its operator-Schmidt or tensor-product decomposition. While quantum technological implementations of the former are thoroughly studied, entangling properties on the operator level are harder to extract in the quantum computational framework because of the exponential nature of sample complexity. Here, we present an algorithm for unbalanced partitions into a small subsystem and a large one (the environment) to compute the tensor-product decomposition of a unitary the effect of which on the small subsystem is captured in classical memory, while the effect on the environment is accessible as a quantum resource. This quantum algorithm may be used to make predictions about operator nonlocality and effective open quantum dynamics on a subsystem, as well as for finding low-rank approximations and low-depth compilations of quantum circuit unitaries. We demonstrate the method and its applications on a time-evolution unitary of an isotropic Heisenberg model in two dimensions. Published by the American Physical Society 2024

General measurements with limited resources and their application to quantum unambiguous state discrimination

In this report, we present a framework for implementing an arbitrary n-outcome generalized quantum measurement (POVM) on an m-qubit register as a sequence of two-outcome measurements requiring only single ancillary qubit. Our procedure offers a particular construction for the two-outcome partial measurements which can be composed into a full implementation of the measurement on any gate architecture. This implementation in general requires classical feedback; we present specific cases when this is not the case. We apply this framework on the unambiguous state discrimination and analyze possible strategies. In the simplest case, it gives the same construction as is known, if we opt for performing conclusiveness measurement first. However, it also offers possibility of performing measurement for one of the state outcomes first, leaving conclusiveness measurement for later. This shows flexibility of presented framework and opens possibilities for further optimization. We present discussion also on biased qubit case as well as general case of unambiguous quantum state discrimination in higher dimension.

Fast preparation of Dicke state by shortcuts to adiabatic passages

Dicke state is one of the most useful multipartite quantum states. In this paper, by utilizing the method of designing the evolution operators to construct shortcuts to adiabatic passages (STAP), we have implemented the fast preparation of Dicke states |D3(2)〉 and |D4(2)〉 with one step. Specifically, with the assistance of quantum Zeno dynamics, the original Hamiltonian of the system is simplified into an effective Hamiltonian. Under the dynamic evolution of this effective Hamiltonian, the pulse control is realized by selecting two appropriate auxiliary parameters. This not only greatly shortens the evolution time but also optimizes the operation of the quantum system. In addition, we have discussed the effects of decoherence, parameter fluctuations and noise on fidelity. The results indicate that the proposed protocol maintains high fidelity. Finally, the fast preparation of Dicke state |Dn(2)〉 can be achieved based on the same method.

Shallow Shadows: Expectation Estimation Using Low-Depth Random Clifford Circuits.

We provide practical and powerful schemes for learning properties of a quantum state using a small number of measurements. Specifically, we present a randomized measurement scheme modulated by the depth of a random quantum circuit in one spatial dimension. This scheme interpolates between two known classical shadows schemes based on random Pauli measurements and random Clifford measurements. We focus on the regime where depth scales logarithmically in the system size and provide evidence that this retains the desirable sample complexity properties of both extremal schemes while also being experimentally feasible. We present methods for two key tasks; estimating expectation values of certain observables from generated classical shadows and, computing upper bounds on the depth-modulated shadow norm, thus providing rigorous guarantees on the accuracy of the output estimates. We achieve our findings by bringing together tools from shadow estimation, random circuits, and tensor networks.

Fast Thermodynamic Study on a Silicon Nanotransistor at Cryogenic Temperatures.

Cryogenic temperatures are crucial for the operation of semiconductor quantum electronic devices, yet the heating effects induced by microwave or laser signals used for quantum state manipulation can lead to significant temperature variations at the nanoscale. Therefore, probing the temperature of individual devices in working conditions and understanding the thermodynamics are paramount for designing and operating large-scale quantum computing systems. In this study, we demonstrate high-sensitivity fast thermometry in a silicon nanotransistor at cryogenic temperatures using RF reflectometry. Through this method, we explore the thermodynamic processes of the nanotransistor during and after a laser pulse and determine the dominant heat dissipation channels in the few-kelvin temperature range. These insights are important to understand thermal budgets in quantum circuits, with our techniques being compatible with microwave and laser radiation, offering a versatile approach for studying other quantum electronic devices in working conditions.

Quantum algorithm for solving the advection equation using Hamiltonian simulation

A quantum algorithm for solving the advection equation by embedding the discrete time-marching operator into Hamiltonian simulations is presented. One-dimensional advection can be simulated directly since the central finite-difference operator for first-order derivatives is anti-Hermitian. Here this is extended to industrially relevant multidimensional flows with realistic boundary conditions and arbitrary finite-difference stencils. A single copy of the initial quantum state is required and the circuit depth grows linearly with the required number of time steps, the sparsity of the time-marching operator, and the inverse of the allowable error. State-vector simulations of a scalar transported in a two-dimensional channel flow and lid-driven cavity configuration are presented as a proof of concept of the proposed approach. Published by the American Physical Society 2024

Entropic uncertainty principle for mixed states

The entropic uncertainty principle in the form proven by Maassen and Uffink yields a fundamental inequality that is prominently used in many places all over the field of quantum information theory. In this paper, we provide a family of versatile generalizations of this relation. Our proof methods build on a deep connection between entropic uncertainties and interpolation inequalities for the doubly stochastic map that links probability distributions in two measurement bases. In contrast to the original relation, our generalization also incorporates the von Neumann entropy of the underlying quantum state. These results can be directly used to bound the extractable randomness of a source-independent quantum random number generator in the presence of fully quantum attacks, to certify entanglement between trusted parties, or to bound the entanglement of a system with an untrusted environment. Published by the American Physical Society 2024

Avoiding lateral mode leakage in thin film lithium niobate waveguides for the generation of spectrally pure photons at telecom wavelengths

Photonic integrated optical components, notably straight waveguides, serve as pivotal elements for on-chip generation and manipulation of quantum states of light. In this work, we focus on optimizing waveguides based on lithium niobate on insulator (LNOI) to generate photon pairs at telecom wavelengths using spontaneous parametric down-conversion (SPDC). Specifically, we investigate lateral leakage for all possible SPDC processes involving type 0, type I, and type II phase matching conditions in an X-cut lithium niobate waveguide and provide a recipe to avoid leakage loss for the interacting photons. Furthermore, focusing on type II phase matching, we engineer the waveguide in the single-mode regime such that it also satisfies group index matching for generating spectrally pure single photons with high purity (99.33%). We also address fabrication imperfections of the optimized design and find that the spectral purity of the generated photons is robust to fabrication errors. This work offers guidance for the suitable selection of morphological parameters to obtain lossless, single-mode LNOI waveguides for building linear optical circuits and photon pair generation at telecom wavelengths using desired phase-matching conditions.

Performance optimization of continuous-variable quantum teleportation with generalized photon-varying non-Gaussian operations

Continuous variable quantum teleportation provides a path to the long-distance transmission of quantum states. Photon-varying non-Gaussian operations have been shown to improve the fidelity of quantum teleportation when integrated into the protocol. However, for a given type of non-Gaussian operation, the achievable fidelity varies with the parameters associated with the operation. Previous work only focused on particular settings of the parameters, over which an optimization was missing. The potential of such operations is not fully uncovered. Given a fixed non-Gaussian operation, the achievable fidelity also varies with input states. An operation that increases the fidelity for teleporting one class of states might do the contrary for other classes of states. A performance metric, upon which an operation is optimized, suitable for different input states, is also missing. In this work, we build a framework for photon-varying non-Gaussian operations for multimode states, upon which we propose a performance metric suitable for arbitrary teleportation input states. We then apply the new metric to evaluate different types of non-Gaussian operations. Starting from simple multiphoton photon subtraction and photon addition, we find that increasing the number of ancillary photons involved in the operation does not guarantee performance improvement. We then investigate a generalization of the operations mentioned above, finding that operations that approximate a particular form provide the best improvement. The results provided here will be valuable for real-world implementations of quantum teleportation networks and applications that harness the non-Gaussianity of quantum states. Published by the American Physical Society 2024

Simulation of an entanglement-based quantum key distribution protocol

Since the 80 s when it was first proposed, Quantum Key Distribution (QKD) elicited great interest in the field of cryptography as a unique procedure for key generation that could in principle guarantee unconditionally secure communication “by the laws of Physics”. In the last fifteen years commercial solutions have started appearing on the market, showing that practical implementations of the protocol were not only possible but also competitive in terms of security and achievable secret-key rate. In this work we describe a simulation of the historical QKD protocol E91 on the IBM Quantum platform, making use of the qubit formalism to represent the quantum states received by two communicating nodes. Having implemented also the post-processing steps for the error correction and the privacy amplification, this model can represent a simple stand-alone tool to study the performance not only of one-to-one communication but of more complex systems that rely on QKD for security, one above all QKD networks.

Efficient optimization of deep neural quantum states

Efficient optimization of deep neural quantum states

Modeling quantum optical phenomena using transition currents

Spontaneous light emission is central to a vast range of physical systems and is a founding pillar for the theory of light–matter interactions. In the presence of complex photonic media, the description of spontaneous light emission usually requires advanced theoretical quantum optics tools such as macroscopic quantum electrodynamics, involving quantized electromagnetic fields. Although rigorous and comprehensive, the complexity of such models can obscure the intuitive understanding of many quantum-optical phenomena. Here, we review a method for calculating spontaneous emission and other quantum-optical processes without making explicit use of quantized electromagnetic fields. Instead, we introduce the concept of transition currents, comprising charges in matter that undergo transitions between initial and final quantum states. We show how predictions that usually demand advanced methods in quantum electrodynamics or quantum optics can be reproduced by feeding these transition currents as sources to the classical Maxwell equations. One then obtains the relevant quantum observables from the resulting classical field amplitudes, without washing out quantum optical effects. We show that this procedure allows for a straightforward description of quantum phenomena, even when going beyond the dipole approximation and single emitters. As illustrative examples, we calculate emission patterns and Purcell-enhanced emission rates in both bound-electron and free-electron systems. For the latter, we derive cathodoluminescence emission and energy-loss probabilities of free electrons interacting with nanostructured samples. In addition, we calculate quantum-beat phenomena in bound-electron systems and wave function-dependent optical coherence in free-electron systems. Remarkably, the transition-current formalism captures more complex phenomena, such as many-body interference effects and super-radiance of both bound- and free-electron systems, second-order processes such as two-photon emission, and quantum recoil corrections to free-electron radiation. We review a variety of light–matter interactions in fields ranging from electron microscopy to nanophotonics and quantum optics, for which the transition-current theoretical formalism facilitates practical simulations and a deeper understanding of novel applications.

Pound–Drever–Hall feedforward: laser phase noise suppression beyond feedback

Pound–Drever–Hall (PDH) laser frequency stabilization is a powerful technique widely used for building narrow linewidth lasers. This technique is, however, ineffective in suppressing high-frequency (>100kHz) laser phase noise detrimental for many applications. Here, we introduce an effective method that can greatly enhance its high-frequency performance. The idea is to recycle the residual PDH signal of a laser locked to a cavity by feedforwarding it directly to the laser output field after a delay fiber. Using this straightforward method, we demonstrate a phase noise suppression capability about four orders of magnitude better than just using the usual PDH feedback for noise around a few MHz. We further find that this method exhibits noise suppression performance equivalent to cavity filtering. This method holds great promise for applications demanding highly stable lasers with diminished phase noise up to tens of MHz (e.g., precise and high-speed control of atomic and molecular quantum states).

Quadratic Speed‐ups in Quantum Kernelized Binary Classification

AbstractClassification is at the core of data‐driven prediction and decision‐making, representing a fundamental task in supervised machine learning. Recently, several quantum machine learning algorithms that use quantum kernels as a measure of similarities between data have emerged to perform binary classification on datasets encoded as quantum states. The potential advantages of quantum kernels arise from the ability of quantum computers to construct kernels that are more effective than their classical counterparts in capturing patterns in data or computing kernels more efficiently. However, existing quantum kernel‐based classification algorithms do not harness the capability of having data samples in quantum superposition for additional enhancements. This work demonstrates how such capability can be leveraged in quantum kernelized binary classifiers (QKCs) through Quantum Amplitude Estimation (QAE) for quadratic speed‐up. Additionally, new quantum circuits are proposed for the QKCs in which the number of qubits is reduced by one, and the circuit depth is reduced linearly with respect to the number of sample data. The quadratic speed‐up over previous methods is verified through numerical simulations on the Iris dataset.

Transformer-Based Neural-Network Quantum State Method for Electronic Band Structures of Real Solids.

Recent advancements in neural networks have led to significant progress in addressing many-body electron correlations in small molecules and various physical models. In this work, we propose QiankunNet-Solid, which incorporates periodic boundary conditions into the neural network quantum state (NNQS) framework based on generative Transformer architecture along with a batched autoregressive sampling (BAS) method, enabling the effective ab initio calculation of real solid materials. The accuracy of this method is demonstrated in one-, two-, and three-dimensional periodic systems, with results comparable to those of full configuration interaction and coupled-cluster method, even in the strongly correlated regime. Furthermore, we compute the band structures and density of states for silicon crystal. The successful incorporation of periodic boundary conditions into the NNQS framework through QiankunNet-Solid opens up new possibilities for the accurate and efficient study of electronic structure properties in solid-state physics.