Quantum Fidelity Research Articles

Effective quantum volume, fidelity and computational cost of noisy quantum processing experiments

Today’s experimental noisy quantum processors can compete with and surpass all known algorithms on state-of-the-art supercomputers for the computational benchmark task of Random Circuit Sampling (Boixo et al., 2018, Arute et al., 2019, Wu et al., 2021, Zhu et al., 2022, Morvan et al., 2023). Additionally, a circuit-based quantum simulation of quantum information scrambling (Mi et al., 2021), which measures a local observable, has already outperformed standard full wave function simulation algorithms, e.g., exact Schrodinger evolution and Matrix Product States (MPS). However, this experiment has not yet surpassed tensor network contraction for computing the value of the observable. Based on those studies, we provide a unified framework that utilizes the underlying effective circuit volume to explain the tradeoff between the experimentally achievable signal-to-noise ratio for a specific observable, and the corresponding computational cost. We apply this framework to recent quantum processor experiments of Random Circuit Sampling (Morvan et al., 2023), quantum information scrambling (Mi et al., 2021), and a Floquet circuit unitary (Kim et al., 2023). This allows us to reproduce the results of Ref. (Kim et al., 2023) in less than one second per data point using one GPU.

Geometric relations in Classical and Quantum Information Theory using the Lambert-Tsallis Wq(x) function

This work presents relations using the Lambert-Tsallis function Wq(x) that provide geometric characteristics in classical and quantum information theory. Firstly, the Wq(x) function is used to estimate parameters in discrete and continuous distributions knowing the a priori probability value. A lower bound for the probability density derived from the branch point of Wq(x). Secondly, we will represent the direct relationship between the Fisher distance in the HF2 hyperbolic model of normal distributions as a function of the distance associated with the Kulback-Leibler divergence in the argument of the Lambert-Tsallis function. Subsequently, using the disentropy based on Rényi, a functional that uses Wq(x) as its kernel, can be used as a measure of purity in the qubit state space (Bloch sphere), as well as for calculating quantum disentanglement of pure states of two qubits. Finally, representations for quantum fidelity and quantum affinity are defined as a function of Wq(x) once the Fisher and Wigner-Yanase quantum information metrics were known, connecting the Lambert-Tsallis function to the quantum speed limit (QSL) theory.

Quantitative determination of the critical points of Mott metal–insulator transition in strongly correlated systems

Mottness is at the heart of the essential physics in a strongly correlated system as many novel quantum phenomena occur in the metallic phase near the Mott metal–insulator transition. We investigate the Mott transition in a Hubbard model by using the dynamical mean-field theory and introduce the local quantum state fidelity to depict the Mott metal–insulator transition. The local quantum state fidelity provides a convenient approach to determining the critical point of the Mott transition. Additionally, it presents a consistent description of the two distinct forms of the Mott transition points.

Resource-efficient simulation of noisy quantum circuits and application to network-enabled QRAM optimization

Giovannetti, Lloyd, and Maccone (2008) proposed a quantum random access memory (QRAM) architecture to retrieve arbitrary superpositions of N (quantum) memory cells via quantum switches and O(log (N)) address qubits. Toward physical QRAM implementations, Chen et al. (2021) recently showed that QRAM maps natively onto optically connected quantum networks with O(log (N)) overhead and built-in error detection. However, modeling QRAM on large networks has been stymied by exponentially rising classical compute requirements. Here, we address this bottleneck by: (1) introducing a resource-efficient method for simulating large-scale noisy entanglement, allowing us to evaluate hundreds and even thousands of qubits under various noise channels; and (2) analyzing Chen et al.’s network-based QRAM as an application at the scale of quantum data centers or near-term quantum internet; and (3) introducing a modified network-based QRAM architecture to improve quantum fidelity and access rate. We conclude that network-based QRAM could be built with existing or near-term technologies leveraging photonic integrated circuits and atomic or atom-like quantum memories.

The Necessary and Sufficient Conditions When Global and Local Fidelities Are Equal.

In the field of quantum information theory, the concept of quantum fidelity is employed to quantify the similarity between two quantum states. It has been observed that the fidelity between two states describing a bipartite quantum system A⊗B is always less than or equal to the quantum fidelity between the states in subsystem A alone. While this fidelity inequality is well understood, determining the conditions under which the inequality becomes an equality remains an open question. In this paper, we present the necessary and sufficient conditions for the equality of fidelities between a bipartite system A⊗B and subsystem A, considering pure quantum states. Moreover, we provide explicit representations of quantum states that satisfy the fidelity equality, based on our derived results.

Volumetric Benchmarking of Error Mitigation with Qermit

The detrimental effect of noise accumulates as quantum computers grow in size. In the case where devices are too small or noisy to perform error correction, error mitigation may be used. Error mitigation does not increase the fidelity of quantum states, but instead aims to reduce the approximation error in quantities of concern, such as expectation values of observables. However, it is as yet unclear which circuit types, and devices of which characteristics, benefit most from the use of error mitigation. Here we develop a methodology to assess the performance of quantum error mitigation techniques. Our benchmarks are volumetric in design, and are performed on different superconducting hardware devices. Extensive classical simulations are also used for comparison. We use these benchmarks to identify disconnects between the predicted and practical performance of error mitigation protocols, and to identify the situations in which their use is beneficial. To perform these experiments, and for the benefit of the wider community, we introduceQermit– an open source python package for quantum error mitigation. Qermit supports a wide range of error mitigation methods, is easily extensible and has a modular graph-based software design that facilitates composition of error mitigation protocols and subroutines.

The holographic map of an evaporating black hole

We construct a holographic map that takes the semi-classical state of an evaporating black hole and its Hawking radiation to a microscopic model that reflects the scrambling dynamics of the black hole. The microscopic model is given by a nested sequence of random unitaries, each one implementing a scrambling time step of the black hole evolution. Differently from other models, energy conservation and the thermal nature of the Hawking radiation are taken into account. We show that the QES formula follows for the entropy of multiple subsets of the radiation and black hole. We further show that a version of entanglement wedge reconstruction can be proved by computing suitable trace norms and quantum fidelities involving the action of a unitary on a subset of Hawking partners. If the Hawking partner is in an island, its unitary can be reconstructed by a unitary on the radiation. We also adopt a similar setup and analyse reconstruction of unitaries acting on an infalling system.

Numerical evidence against advantage with quantum fidelity kernels on classical data

Quantum machine learning techniques are commonly considered one of the most promising candidates for demonstrating practical quantum advantage. In particular, quantum kernel methods have been demonstrated to be able to learn certain classically intractable functions efficiently if the kernel is well aligned with the target function. In the more general case, quantum kernels are known to suffer from exponential ``flattening'' of the spectrum as the number of qubits grows, preventing generalization and necessitating the control of the inductive bias by hyperparameters. We show that the general-purpose hyperparameter-tuning techniques proposed to improve the generalization of quantum kernels lead to the kernel becoming well approximated by a classical kernel, removing the possibility of quantum advantage. We provide extensive numerical evidence for this phenomenon utilizing multiple previously studied quantum feature maps and both synthetic and real data. Our results show that unless novel techniques are developed to control the inductive bias of quantum kernels, they are unlikely to provide a quantum advantage on classical data that lacks special structure.

Subharmonic fidelity revival in a driven PXP model

The PXP model hosts a special set of nonergodic states, referred to as quantum many-body scars. One of the consequences of quantum scarring is the periodic revival of the wave function fidelity. It has been reported that quantum fidelity revival occurs in the PXP model for certain product states, and periodic driving of the chemical potential can enhance the magnitude of quantum revival and can even change the frequencies of revival showing the subharmonic response. Although the effect of the periodic driving in the PXP model has been studied in the limit of certain perturbative regimes, the general mechanism of such enhanced revival and frequency change has been barely studied. In this paper, we investigate how periodic driving in the PXP model can systematically control the fidelity revival. Particularly, focusing on a product state called the N\'eel state, we analyze the condition of driving to enhance the magnitude of revival or change the frequencies of revival. To clarify the reason for such control, we consider the similarities between the PXP model and the free spin-$1/2$ model in graph theoretical analysis and show that the quantum fidelity feature in the PXP model is well explained by the free spin-$1/2$ model. In addition, under a certain limit of the driving parameters, an analytic approach to explain the main features of the fidelity revival is also applied. Our results give insight into the scarring nature of the periodically driven PXP model and pave the way to understand the (sub)harmonic responses in these models and controls thereof.

Thermal entanglement and quantum coherence of a single electron in a double quantum dot with Rashba interaction

In this work, we study the thermal quantum coherence and fidelity in a semiconductor double quantum dot. The device consists of a single electron in a double quantum dot with Rashba spin-orbit coupling in the presence of an external magnetic field. In our scenario, the thermal entanglement of the single electron is driven by the charge and spin qubits, the latter controlled by Rashba coupling. Analytical expressions are obtained for thermal concurrence and correlated coherence using the density matrix formalism. The main goal of this work is to provide a good understanding of the effects of temperature and several parameters in quantum coherence. In addition, our findings show that we can use the Rashba coupling to tune in the thermal entanglement, quantum coherence, as well as, the thermal fidelity behavior of the system. Moreover, we focus on the role played by thermal entanglement and correlated coherence responsible for quantum correlations. We observe that the correlated coherence is more robust than the thermal entanglement in all cases, so quantum algorithms based only on correlated coherence may be stronger than those based on entanglement.

Proposal for quantum memory miniaturization in superconducting circuit QED

Superconducting circuit quantum electrodynamics is the main architecture for implementation of quantum processors. This work presents a proposal to miniaturize quantum memory devices by combining the quantum memory qubits encoded directly in the first two Fock states ${|0\ensuremath{\rangle},|1\ensuremath{\rangle}}$ and the bus channel in the same specially designed multimode cavity. One key feature of the resonant modes used for quantum memory is that the microwave current is localized, which facilitates the storage and retrieval of information in superconducting qubits while preventing crosstalk from other resonant modes. Frequency design and distinct coupling strengths in this kind of device are analyzed with electromagnetic simulations and numerical calculations. The quantum memory properties, e.g., storage speed, fidelity, and stability, are examined in four cases: (1) memory operation of single-qubit state, (2) disturbing effects, (3) coupling operation, and (4) entangled states storage. Decoherence time of the quantum processor can be extended to more than one order of magnitude longer than that without the memory. By integrating transmons with optimally designed coplanar waveguide multimode quantum memories, the present study could provide an optimistic perspective for the development of large-scale superconducting quantum processors.

Quantifying emotions through quantum computations

Quantum computations are extensively used to solve challenging problems and emotion detection is one such field that has lot of potential to be explored. Classical studies can identify emotions but fail to observe their intensities at such minute level. The proposed research primarily intends to quantify the intensities related with happy and sad emotions on a quantum computer examining subjects within Amsterdam Dynamic Facial Expression Set (ADFES) dataset. Leveraging quantum ability to compute quantum state fidelity established closeness of the encoded states. We used quantum variational classifier technique for this experiment performed on the quantum simulator available on the IBM Quantum Experience platform.

A Quantum Tanimoto Coefficient Fidelity for Entanglement Measurement

Fidelity plays an important role in quantum information processing, which provides a basic scale for comparing two quantum states. At present, one of the most commonly used fidelities is Uhlmann-Jozsa (U-J) fidelity. However, U-J fidelity needs to calculate the square root of the matrix, which is not trivial in the case of large or infinite density matrices. Moreover, U-J fidelity is a measure of overlap, which has limitations in some cases and cannot reflect the similarity between quantum states well. Therefore, a novel quantum fidelity measure called quantum Tanimoto coefficient (QTC) fidelity is proposed in this paper. Unlike other existing fidelities, QTC fidelity not only considers the overlap between quantum states, but also takes into account the separation between quantum states for the first time, which leads to a better performance of measure. Specifically, we discuss the properties of the proposed QTC fidelity. QTC fidelity is compared with some existing fidelities through specific examples, which reflects the effectiveness and advantages of QTC fidelity. In addition, based on the QTC fidelity, three discrimination coefficients d <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QTC</sup> , d <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QTC</sup> , and d <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QTC</sup> are defined to measure the difference between quantum states. It is proved that the discrimination coefficient d <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">QTC</sup> is a true metric. Finally, we apply the proposed QTC fidelity-based discrimination coefficients to measure the entanglement of quantum states to show their practicability.

Deep Reinforcement Learning for Preparation of Thermal and Prethermal Quantum States

We propose a method based on deep reinforcement learning that efficiently prepares a quantum many-body pure state in thermal or prethermal equilibrium. The main physical intuition underlying the method is that the information on the equilibrium states can be efficiently encoded/extracted by focusing on only a few local observables, relying on the typicality of equilibrium states. Instead of resorting to the expensive preparation protocol that adopts global features such as the quantum state fidelity, we show that the equilibrium states can be efficiently prepared only by learning the expectation values of local observables. We demonstrate our method by preparing two illustrative examples: Gibbs ensembles in non-integrable systems and generalized Gibbs ensembles in integrable systems. Pure states prepared solely from local observables are numerically shown to successfully encode the macroscopic properties of the equilibrium states. Furthermore, we find that the preparation errors, with respect to the system size, decay exponentially for Gibbs ensembles and polynomially for generalized Gibbs ensembles, which are in agreement with the finite-size fluctuation within thermodynamic ensembles. Our method paves a path toward studying the thermodynamic and statistical properties of quantum many-body systems in quantum hardware.

Optimal quantum control via genetic algorithms for quantum state engineering in driven-resonator mediated networks

We employ a machine learning-enabled approach to quantum state engineering based on evolutionary algorithms. In particular, we focus on superconducting platforms and consider a network of qubits—encoded in the states of artificial atoms with no direct coupling—interacting via a common single-mode driven microwave resonator. The qubit-resonator couplings are assumed to be in the resonant regime and tunable in time. A genetic algorithm is used in order to find the functional time-dependence of the couplings that optimise the fidelity between the evolved state and a variety of targets, including three-qubit GHZ and Dicke states and four-qubit graph states. We observe high quantum fidelities (above 0.96 in the worst case setting of a system of effective dimension 96), fast preparation times, and resilience to noise, despite the algorithm being trained in the ideal noise-free setting. These results show that the genetic algorithms represent an effective approach to control quantum systems of large dimensions.

Quantum Codes Do Not Increase Fidelity against Isotropic Errors

In this article, we study the ability of error-correcting quantum codes to increase the fidelity of quantum states throughout a quantum computation. We analyze arbitrary quantum codes that encode all qubits involved in the computation, and we study the evolution of n-qubit fidelity from the end of one application of the correcting circuit to the end of the next application. We assume that the correcting circuit does not introduce new errors, that it does not increase the execution time (i.e. its application takes zero seconds) and that quantum errors are isotropic. We show that the quantum code increases the fidelity of the states perturbed by quantum errors but that this improvement is not enough to justify the use of quantum codes. Namely, we prove that, taking into account that the time interval between the application of the two corrections is multiplied (at least) by the number of qubits n (due to the coding), the best option is not to use quantum codes, since the fidelity of the uncoded state over a time interval n times smaller is greater than that of the state resulting from the quantum code correction.

Qubit dynamics driven by dipole field in thermal noise environment

Quantum computing is a new way to process quantum information by using superposition and entanglement of the quantum system. Quantum state’s vast Hilbert space allows it to perform operations that classical computers cannot. The quantum computing has unique advantages in dealing with some complex problems, so it has attracted wide attention. Computing a single qubit is the first of seven fundamental stages needed to achieve a large-scale quantum computer that is universal, scalable and fault-tolerant. In other words, the primary task of quantum computing is the careful preparation and precise regulation of qubits. At present, the physical systems that can be used as qubits include superconducting qubits, semiconductor qubits, ion trap systems and nitrogen-vacancy (NV) color centers. These physical systems have made great progress of decoherence time and scalability. Owing to the vulnerability of qubits, ambient thermal noise can cause quantum decoherence, which greatly affects the fidelity of qubits. Improving the fidelity of qubits is therefore a key step towards large-scale quantum computing. Based on the dipole field driven qubit, the stochastic dynamic structure decomposition method is adopted and the Kubo-Einstein fluctuation-dissipation theorem is used to study the qubit control in a thermal noise environment. The dipole field has components in three directions, not just in one plane, which allows more flexible control of quantum states. Without considering the noise, the quantum state can reach the target state 100%. In the noisy environment, thermal noise will cause the deviation between the actual final state and the target final state caused by thermal fluctuation, which becomes the main factor affecting the quantum fidelity. The influence of thermal noise is related to temperature and the evolution trajectory of quantum state. Therefore, this paper proposes an optimal scheme to improve the qubit fidelity in the thermal noise environment. The feasibility of this method is verified by numerical calculation, which can provide a new solution for further guiding and evaluating the experiment. The scheme is suitable for qubit systems of various physical control fields, such as semiconductor qubits and nitrogen vacancy center qubits. This work may have more applications in the development of quantum manipulation technology and can also be extended to multi-qubit systems, the details of which will appear in the future work.

Parallel-mode-channel approach to multimode Gaussian state estimation

The imaging process and its corresponding state estimation are treated as parallel mode channels and two estimation subproblems, respectively. In the two subproblems, the displacement vector and covariance matrix of the object field are estimated independently from the measured ones of the aperture field by minimizing their respective cost functions defined quantum mechanically. The measurement scheme is also briefly discussed. As an application, the location estimation of a point feature of a disk object is studied analytically and numerically. The results show that the estimation that uses only the displacement vector does not enhance the resolution of the amplitude image to a large extent. On the contrary, the quantum Cram\'er-Rao bound of spatial resolution can be pushed down toward the uncertainty relation. Furthermore, the high quantum fidelities verify the effectiveness of the estimation and the broad applicability to arbitrary multimode Gaussian states.

Quantum fidelity of the Aubry-André model and the exponential orthogonality catastrophe

Advances in experimental tools allow to study quantum fidelity in unprecedented controlled settings. While the fidelity in metals, when adding a local impurity, is well known to show the Anderson orthogonality catastrophe (AOC), there remain outstanding questions in other quantum phases and settings. Here, we aim to tackle these by exploring systematically the ground-state fidelity of the Fermi liquid in the (extended) Aubry-Andr\'e (AA) model, which allows to explore the AOC in both localized extended and critical phases. We discover that the AOC is typically exponential in the critical regime of the AA model and at the mobility edge of the extended AA model for an extended impurity, while it decays in the AA model with a power law for a weak single-site impurity. We explain this in terms of critical correlations and multipoint correlations. The OC is found to be exponential in the insulating regime, due to a fundamentally different, statistical mechanism, which is explained in detail. Furthermore, we consider a parametric perturbation to the AA model, and find an exponential OC numerically, in agreement with an analytical derivation which we provide here.

On the learnability of quantum state fidelity

Current quantum processing technology is generally noisy with a limited number of qubits, stressing the importance of quantum state fidelity estimation. The complexity of this problem is mainly due to not only accounting for single gates and readout errors but also for interactions among which. Existing methods generally rely on either reconstructing the given circuit state, ideal state, and computing the distance of which; or forcing the system to be on a specific state. Both rely on conducting circuit measurements, in which computational efficiency is traded off with obtained fidelity details, requiring an exponential number of experiments for full information. This paper poses the question: Is the mapping between a given quantum circuit and its state fidelity learnable? If learnable, this would be a step towards an alternative approach that relies on machine learning, providing much more efficient computation. To answer this question, we propose three deep learning models for 1-, 3-, and 5-qubit circuits and experiment on the following real-quantum processors: ibmq_armonk (1-qubit), ibmq_lima (5-qubit) and ibmq_quito (5-qubit) backends, respectively. Our models achieved a mean correlation factor of 0.74, 0.67 and 0.66 for 1-, 3-, and 5-qubit random circuits, respectively, with the exponential state tomography method. Additionally, our 5-qubit model outperforms simple baseline state fidelity estimation method on three quantum benchmarks. Our method, trained on random circuits only, achieved a mean correlation factor of 0.968 while the baseline method achieved 0.738. Furthermore, we investigate the effect of dynamic noise on state fidelity estimation. The correlation factor substantially improved to 0.82 and 0.74 for the 3- and 5-qubit models, respectively. The results show that machine learning is promising for predicting state fidelity from circuit representation and this work may be considered a step towards efficient end-to-end learning.