Quantum Devices Research Articles (Page 1)

Abstract Solving quantum many-body systems is one of the most significant regimes where quantum computing applies. Currently, as a hardware-friendly computational paradigms, variational algorithms are often used for finding the ground energy of quantum many-body systems. However, running large-scale variational algorithms is challenging, because of the noise as well as the obstacle of barren plateaus. In this work, we propose the quantum version of matrix product state (qMPS), and develop variational quantum algorithms to prepare it in canonical forms, allowing to run the variational MPS method, which is equivalent to the Density Matrix Renormalization Group method, on near term quantum devices. Compared with widely used methods such as variational quantum eigensolver, this method can greatly reduce the number of qubits required, and thus can mitigate the effects of Barren Plateaus while obtain comparable or even better accuracy. Our method holds promise for distributed quantum computing, offering possibilities for fusion of different computing systems.

We present Noise-Directed Adaptive Remapping (NDAR), a heuristic algorithm for approximately solving binary optimization problems by leveraging certain types of noise. We consider access to a noisy quantum processor with dynamics that features a global attractor state. In a standard setting, such noise can be detrimental to the quantum optimization performance. Our algorithm bootstraps the noise attractor state by iteratively gauge-transforming the cost-function Hamiltonian in a way that transforms the noise attractor into higher-quality solutions. The transformation effectively changes the attractor into a higher-quality solution of the Hamiltonian based on the results of the previous step. The end result is that noise aids variational optimization, as opposed to hindering it. We present an improved Quantum Approximate Optimization Algorithm (QAOA) runs in experiments on Rigetti's quantum device. We report approximation ratios 0.9 - 0.96 for random, fully connected graphs on n = 82 qubits, using only depth p = 1 QAOA with NDAR. This compares to 0.34 - 0.51 for standard p = 1 QAOA with the same number of function calls.

Threading defects in diamond degrade the performance of diamond-based quantum and electronic devices. Although the disorder of the atomic arrangement induced by the threading defects is considered to be the cause of the performance degradation, yet quantitative and spatially resolved evaluation of the stress tensor that characterizes the magnitude of the disorder has remained challenging. In this study, we applied the evaluation technique of the stress tensor based on nitrogen-vacancy (NV) centers to the mapping of the stress field around a threading defect in a chemical vapor deposition diamond film. Furthermore, we compared the stress tensor measured using NV centers with that obtained by conventional methods such as Raman spectroscopy and x-ray topography. Around the threading defect, the components of the stress tensor σxy, σyz, σzx, and σxx + σyy + σzz varied by approximately 0.2, 0.2, 0.3, and 1.2 GPa, respectively, and each component exhibited a rotationally symmetric distribution extending over a diameter of approximately 10–20 μm. We calculated the Raman shift mapping from the stress tensor obtained using NV centers, and the Raman peak was estimated to decrease by approximately 0.9 cm−1 due to the stress tensor at the center of the threading defect. This value was comparable to the experimental result of Raman shift mapping. These results indicate that the components of the stress tensor measured by NV centers accurately reflect the stress induced by the threading defects. The stress tensor and x-ray topography images suggest that the threading defect measured in this study was a bundle dislocation.

In recent years, the Variational Quantum Eigensolver (VQE) has emerged as one of the most popular algorithms for solving the electronic structure problem on near-term quantum computers. The utility of VQE is often hindered by the limitations of current quantum hardware, including, but not limited to, short qubit coherence times and low gate fidelities. These limitations become particularly pronounced when VQE is used along with deep quantum circuits, such as those required by the "Unitary Coupled Cluster Singles and Doubles" (UCCSD) ansatz, often resulting in significant errors. To address these issues, we propose a low-depth ansatz based on parallelized Givens rotations, which can recover substantial correlation energy while drastically reducing circuit depth and two-qubit gate counts for an arbitrary active space (AS). Also, considering the current hardware architectures with low qubit counts, we introduce a systematic way to select molecular orbitals to define active spaces (ASs) that retain significant electron correlation. We validate our approach by computing bond dissociation profiles of water and strongly correlated systems, such as molecular nitrogen and oxygen, across various ASs. Noiseless simulations using the new ansatz yield ground-state energies comparable to those from the UCCSD ansatz while reducing circuit depth by 50-70%. Moreover, in noisy simulations, our approach achieves energy error rates an order of magnitude lower than that of UCCSD. Considering the efficiency and practical usage of our ansatz, we hope that it becomes a potential choice for performing quantum chemistry calculations on near-term quantum devices.

The Quantum Internet, a network of quantum-enabled infrastructure, represents the next frontier in telecommunications, promising capabilities that cannot be attained by classical counterparts. A crucial step in realizing such large-scale quantum networks is the integration of entanglement distribution within existing telecommunication infrastructure. Here, we demonstrate a real-world scalable quantum networking testbed deployed within Deutsche Telekom’s metropolitan fibers in Berlin. Using commercially available quantum devices and standard add-drop multiplexing hardware, we distributed polarization-entangled photon pairs over dynamically selectable looped fiber paths ranging from 10 m to 60 km and showed entanglement distribution over up to approximately 100 km. Quantum signals, transmitted at 1324 nm (O-band), coexist with conventional bidirectional C-band traffic without dedicated fibers or infrastructure changes. Active stabilization of the polarization enables robust long-term performance, achieving entanglement Bell-state fidelity bounds between 85% and 99% and Clauser–Horne–Shimony–Holt parameter S -values between 2.36 and 2.74 during continuous multiday operation. By achieving a high-fidelity entanglement distribution with less than 1.5% downtime, we confirm the feasibility of hybrid quantum-classical networks under real-world conditions at the metropolitan scale. These results establish deployment benchmarks and provide a practical roadmap for telecom operators to integrate quantum capabilities.

This study presents the development of advanced resonant tunneling diodes (RTD) based on InGaAs/AlAs that have an impressive 80% indium content in the quantum well. These cutting-edge diodes were meticulously grown in-house using the precise technique of molecular beam epitaxy. The proposed RTD showcased remarkable negative-differential resistance characteristics, achieving a cm-2 peak current density (Jp) at V, and a peak-to-valley current ratio of 8.5. A large-signal model of the fabricated RTD was developed in LTspice using experimental current-voltage (I-V) data, enabling the simulation of an RTD-based frequency multiplier circuit. A frequency multiplier with a multiplication factor of three (x3) was created and tested by arranging two RTDs in series to introduce non-linearity in the circuit. The experiment successfully demonstrated a threefold increase in frequency, converting an input signal of 166 MHz (3.07 mW) to an output frequency of 500 MHz (51.57 µW). The results highlighted the potential of InGaAs/AlAs RTDs for cost-effective ultra-high-frequency applications, particularly for communication systems, radar, and signal processing.

Optical frequency conversion plays a key role in realizing large-scale quantum networks, including multi-qubit discrete-variable quantum computers and quantum communication links where photons serve as the fundamental qubits. However, achieving efficient conversion via nonlinear optical processes for specific target wavelengths remains a significant challenge, as precise dispersion control is essential to satisfy phase-matching conditions across specific frequency ranges. An intriguing approach to solve this challenge is leveraging the modal degree of freedom in spatially multimoded waveguides and realizing intermodal nonlinear interaction. Following this approach, we present the experimental demonstration of tunable, number-state-preserving frequency conversion of true single photons emitted from a quantum dot. The conversion is achieved in a multimode fiber and exhibits a peak internal efficiency of 85% while retaining single photon purity of 99% during conversion. Our results show that the intermodal platform presents a promising and versatile approach for overcoming phase-matching limitations in quantum frequency conversion, thus allowing the efficient interfacing of different optical quantum devices.

Abstract We investigate an opto-magnomechanical hybrid system composed of two coupled cavity
 modes, where one cavity is integrated with a quantum dot (QD) and the other with a
 magnomechanical subsystem. We analyze the time dynamics of the systems various modes
 by varying di erent parameters associated with the setup. Our results demonstrate highly
 e cient energy transfer between the modes with reduced damping e ects. Additionally, we
 study the entanglement dynamics between the mechanical mode and the magnon mode, as
 well as between the magnon mode and the quantum dot. The analysis reveals that the
 entanglement can be signi cantly inuenced and sustained for longer periods by suitably
 tuning the system parameters. The results of this work could contribute to the development
 of more e cient quantum communication protocols, enhance quantum metrology techniques,
 and provide insights into the design of hybrid quantum devices that harness the unique
 properties of multiple quantum subsystems.

Near term quantum devices have recently garnered significant interest as promising candidates for investigating difficult-to-probe regimes in many-body physics. To this end, various qubit encoding schemes targeting second quantized Hamiltonians have been proposed and optimized. In this study, we investigate two qubit representations of the planar rotor lattice Hamiltonian. The first representation is realized by decomposing the rotor Hamiltonian projectors in binary and mapping them to spin-1/2 projectors. The second approach relies on embedding the planar rotor lattice Hilbert space in a larger space and recovering the relevant qubit encoded system as a quotient space projecting down to the physical degrees of freedom. This is typically called the unary mapping and is used for bosonic systems. We establish the veracity of the two encoding approaches using sparse diagonalization on small chains and discuss quantum phase estimation resource requirements to simulate small planar rotor lattices on near-term quantum devices. We also examine the utility of variational approaches for simulating planar rotor chains using these encodings.

Quantum Selected Configuration Interaction (QSCI) and an extended protocol known as Sample-based Quantum Diagonalization (SQD) have emerged as promising algorithms to solve the electronic Schrödinger equation with noisy quantum computers. In QSCI/SQD a quantum circuit is repeatedly prepared on the quantum device, and measured configurations form a subspace of the many-body Hilbert space in which the Hamiltonian is diagonalized classically. For the dissociation of N2 and a model [2Fe - 2S] cluster (correlating 10 electrons in 26 orbitals and 30 electrons in 20 orbitals, respectively) we show that a nonperturbative stochastic approach, phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC), using truncated SQD trial wave functions obtained from quantum hardware can recover a substantial amount (e.g., (100) mHa) of correlation energy. This hybrid quantum-classical combination has the potential to greatly reduce the sampling burden placed on the QSCI/SQD procedure, and is a compelling alternative to recently proposed hybrid ph-AFQMC algorithms that rely on quantum state tomography.

Two-dimensional electron gases (2DEGs) formed at complex oxide interfaces offer a unique platform to engineer quantum nanostructures. However, the scalable fabrication of devices in these materials remains challenging. Here, we demonstrate an efficient fabrication approach by patterning narrow constrictions in a superconducting KTaO3-based heterostructure which are individually tunable via coplanar side gates within the 2DEG plane. Leveraging the high dielectric permittivity of KTaO3, we achieve strong electrostatic modulation of the superconducting 2DEG. Within the superconducting state, we demonstrate efficient modulation of the critical current and Berezinskii-Kosterlitz-Thouless transition temperature at the weak link. Further tuning enables a transition to a dissipative state. All of these states are achievable with a side gate voltage ≲ 1 V. The fabrication process is scalable and versatile, enabling a platform for quantum devices and the study of a wide array of physical phenomena at complex oxide interfaces.

We provide a polynomial-time classical algorithm for noisy quantum circuits. The algorithm computes the expectation value of any observable for any circuit, with a small average error over input states drawn from an ensemble (e.g., the computational basis). Our approach is based upon the intuition that noise exponentially damps nonlocal correlations relative to local correlations. This enables one to classically simulate a noisy quantum circuit by keeping track of only the dynamics of local quantum information. Our algorithm also enables sampling from the output distribution of a circuit in quasipolynomial time, so long as the distribution anticoncentrates. A number of implications are discussed, including a fundamental limit on the efficacy of noise mitigation strategies: For constant noise rates, any quantum circuit for which error mitigation succeeds in polynomial-time on most input states can also be classically simulated in polynomial-time on most input states. Our algorithms scale exponentially in the inverse noise rate, which is fundamental and makes them impractical for current quantum devices.

Abstract Information engines harness measurement and feedback to convert energy into useful work. In this study, we investigate the fundamental trade-offs between ergotropic output power, thermodynamic efficiency and information-to-work conversion efficiency in such engines, explicitly accounting for the finite time required for measurement. As a model engine, we consider a two-level quantum system from which work is extracted via a temporarily coupled quantum harmonic oscillator that serves as the measurement device. This quantum device is subsequently read out by a classical apparatus. We compute trade-offs for the performance of the information engine using Pareto optimisation, which has recently been successfully used to optimise performance in engineering and biological physics. Our results offer design principles for future experimental implementations of information engines, such as in nano-mechanical systems and circuit QED platforms.Joint Probability in the Absence of Interaction

Quantum-correlated interferometer is an emerging tool in quantum technology that offers classical-limit-breaking phase sensitivity. However, to date, there exists a configurational bottleneck for its practicability due to the low phase-sensing power limited by the current detection strategies. Here, we establish an innovative development termed as "quantum twin interferometer" with dual pairs of entangled twin beams arranged in the parallel configuration, allowing full exploitation of the quantum resource through the configuration of entangled detection. We observe the distributed phase sensing with 3-decibel quantum noise reduction in phase-sensing power at the level of milliwatts, which advances the record of signal-to-noise ratio so far achieved in photon-correlated interferometers by three orders of magnitude. The developed techniques in this work can be used to revolutionize a diversity of quantum devices requiring phase measurement.

Controlling nanoscale light propagation via novel materials and phenomena is at the core of nanophotonics. Hyperbolic polaritons in anisotropic materials offer extreme field confinement and directional propagation. However, most of the studies are concentrated in mid-infrared, limiting their potential for future nanophotonics and quantum devices. Here, we study the manipulation of near-infrared low-loss hyperbolic plasmon polaritons in van der Waals material MoOCl2 by using light field as the control parameter through photoemission electron microscopy. Both the oblique incidence direction and the polarization can serve as independent degrees for dynamic control in the near-infrared range. Our method presents that through the configuration of the light source to break the intrinsic symmetry, hyperbolic polariton modes can be achieved in a desired way. This study confirms that near-infrared hyperbolic polaritons can be effectively achieved and tuned, providing an exotic platform for on-chip applications.

We present Learning-Driven Annealing (LDA), a framework that links individual quantum annealing evolutions into a global solution strategy to mitigate hardware constraints such as short annealing times and integrated control errors. Unlike other iterative methods, LDA does not tune the annealing procedure (e.g. annealing time or annealing schedule), but instead learns about the problem structure to adaptively modify the problem Hamiltonian. By deforming the instantaneous energy spectrum, LDA suppresses transitions into high-energy states and focuses the evolution into low-energy regions of the Hilbert space. We demonstrate the efficacy of LDA by developing a hybrid quantum-classical solver for large-scale spin glasses. The hybrid solver is based on a comprehensive study of the internal structure of spin glasses, outperforming other quantum and classical algorithms (e.g., reverse annealing, cyclic annealing, simulated annealing, Gurobi, Toshiba's SBM, VeloxQ and D-Wave hybrid) on 5580-qubit problem instances in both runtime and lowest energy. LDA is a step towards practical quantum computation that enables today's quantum devices to compete with classical solvers.

4H-silicon carbide is a promising platform for solid-state quantum technology due to its commercial availability as a wide bandgap semiconductor and ability to host numerous spin-active color centers. Integrating color centers into suspended nanodevices enhances defect control and readout, key advances needed to fully harness their potential. However, challenges in developing robust fabrication processes for 4H-SiC thin films, due to the material's chemical and mechanical stability, limit their implementation in quantum applications. Here, we report on a new fabrication approach that first synthesizes suspended thin films from a monolithic platform and then patterns devices. With this technique, we fabricate and characterize structures tailored for defect integration, demonstrating 1D photonic crystal cavities, with and without waveguide interfaces, and lithium niobate on 4H-SiC acoustic cavities. This approach allows for greater fabrication flexibility, supporting high temperature annealing and heterogeneous material platform compatibility, providing a versatile platform for scalable fabrication of 4H-SiC devices for quantum technologies.

Quantum computing has the potential to revolutionize fields like quantum optimization and quantum machine learning. However, current quantum devices are hindered by noise, reducing their reliability. A key challenge in gate-based quantum computing is improving the reliability of quantum circuits, measured by process fidelity, during the transpilation process, particularly in the routing stage. In this paper, we address the Fidelity Maximization in Routing Stage (FMRS) problem by introducing FIDDLE, a novel learning framework comprising two modules: a Gaussian Process-based surrogate model to estimate process fidelity with limited training samples and a reinforcement learning module to optimize routing. Our approach is the first to directly maximize process fidelity, outperforming traditional methods that rely on indirect metrics such as circuit depth or gate count. We rigorously evaluate FIDDLE by comparing it with state-of-the-art fidelity estimation techniques and routing optimization methods. The results demonstrate that our proposed surrogate model is able to provide a better estimation on the process fidelity compared to existing learning techniques, and our end-to-end framework significantly improves the process fidelity of quantum circuits across various noise models.

The dynamic mobility and placement of buckminsterfullerene (C60) molecules on moiré-patterned graphene as oxygen atoms are adsorbed across the interface are illustrated in this work. The diffusivity of C60 molecules is shown to be greatly reduced on oxygen-decorated epitaxial graphene grown on Ru(0001), and thin films of C60 grown on O/graphene/Ru(0001) are observed to be disordered compared to the commensurate packing demonstrated on pristine graphene/Ru(0001). Previously well-ordered C60 domains are observed to disorder upon scattering O(3P) onto the surface. Overall, the results demonstrate the role that trace coverages (<1%) of atomic oxygen play in limiting the growth of well-ordered thin films on moiré-patterned materials, materials that show promise as platforms for quantum devices, sensors, and next-generation catalysts. Furthermore, STM images presented herein illustrate a new direction in molecular scattering in which on-surface outcomes are visualized with angstrom-level precision after exposure to supersonic molecular beams.

Abstract A hardware‐efficient optimization scheme is presented for quantum chemistry calculations, utilizing the Sampled Quantum Diagonalization (SQD) method. This algorithm, optimized SQD (SQDOpt), combines the classical Davidson method technique with added multi‐basis measurements to optimize a quantum Ansatz on hardware using a fixed number of measurements per optimization step. This addresses the key challenge associated with other quantum chemistry optimization protocols, namely Variational Quantum Eigensolver (VQE), which must measure in hundreds to thousands of noncommuting Pauli terms to estimate energy on hardware, even for molecules with less than 20 qubits. Numerical results for various molecules, including hydrogen chains, water, and methane, demonstrate the efficacy of this method compared to classical and quantum variational approaches, and the performance on the ibm‐cleveland quantum hardware is confirmed, where instances are found where SQDOpt either matches or exceeds the solution quality of practical implementations of noiseless VQE. A runtime scaling indicates that SQDOpt on quantum hardware is competitive with classical state‐of‐the‐art methods, with a crossover point of 1.5 seconds/iteration for the SQDOpt on quantum hardware and classically simulated VQE with the 20‐qubit molecule. These findings suggest that the proposed SQDOpt framework offers a scalable and robust pathway for quantum chemistry simulations on noisy intermediate‐scale quantum (NISQ) devices.