The in-plane motion of an electron on helium can couple to superconducting microwave resonators via electrical dipole coupling, offering a robust and rapid readout scheme. In previous efforts, microwave resonator designs for electrons on helium have lacked the coupling strength to reach the strong coupling regime, where coherent quantum effects outlast both electron and resonator decoherence rates. High-impedance superconducting microwave resonators offer a path to strong coupling, but integrating such resonators with electrons on helium remains an outstanding challenge. Here, we introduce a high-impedance resonator design compatible with strong coupling to electrons on helium. We fabricate and measure titanium nitride resonators with median internal quality factors of $3.9\times {10}^5$ and average impedance of 2.5 $\mathrm{k}\mathrm{\Omega }$, promising a sevenfold increase in coupling strength compared with standard 50-$\mathrm{\Omega }$ resonators. In addition, we develop a simplified resonator model from the capacitance matrix and sheet inductance that accurately predicts the mode frequencies, significantly simplifying the design process of future resonators for investigating quantum effects with electrons on helium.

Shadow tomography is a promising way for quantum system characterization, enabling the extraction of multiple properties of unknown quantum states using only single-copy randomized measurements. While single-copy measurements are experimentally friendly and efficient for estimating linear functions, they show limited efficiency for nonlinear ones. In contrast, multicopy measurements enabled by joint entangling operations can remarkably reduce the sample complexity for nonlinear functions. Here, we design and realize a deterministic optical three-qubit Fredkin gate by comprehensive control over multiple degrees of freedom of a single photon, achieving controlled-swap operation with high process fidelity of $0.935\pm 0.001$. Utilizing this Fredkin gate, we demonstrate hybrid shadow tomography, in which the higher-order functions like the moments can be efficiently estimated from the two-copy swap operation in conjunction with the single-copy randomized measurement. Our demonstration indicates a significant reduction in sample complexity compared with the single-copy setting. Furthermore, we show the potential advantage of our scheme in a proof-of-principle quantum metrology experiment, wherein the accuracy of parameter estimation is enhanced with the assistance of virtual distillation. Our results suggest that hybrid shadow tomography is an efficient and powerful tool for shadow-estimation-enabled quantum information processing.

Semiconductor quantum dots (QDs) are a promising platform for multiple different qubit implementations, all of which are voltage controlled by programmable gate electrodes. However, as the QD arrays grow in size and complexity, tuning procedures that can fully autonomously handle the increasing number of control parameters are becoming essential for enabling scalability. We propose a bootstrapping algorithm for initializing a depletion-mode QD device in preparation for subsequent phases of tuning. During bootstrapping, the QD device functionality is validated, all gates are characterized, and the QD charge sensor is made operational. We demonstrate the bootstrapping protocol in conjunction with a coarse-tuning module, showing that the combined algorithm can efficiently and reliably take a cooled-down QD device to a desired global-state configuration in under $8$ min, with a success rate of $96\mathrm{\%}$. Finally, by following heuristic approaches to QD device initialization and combining the efficient ray-based measurement with the rapid radio-frequency reflectometry measurements, the proposed algorithm establishes a reference in terms of performance, reliability, and efficiency against which alternative algorithms can be benchmarked.

We present thin-film niobium ($\mathrm{Nb}$) spiral resonators exhibiting large geometric inductance, high critical magnetic fields, and high single-photon quality factors. These low-loss geometric inductors can be a compelling alternative to kinetic inductors to create high-impedance superconducting devices for applications that require magnetic fields. By varying the spiral pitch, we realize resonators with characteristic impedances ranging from $3.25–7.09\mathrm{k}\mathrm{\Omega }$. We measure the temperature and magnetic-field-dependent losses and find that the high-impedance resonators maintain an intrinsic quality factor above approximately ${10}^5$ for parallel magnetic fields of up to 1 T. These properties make spiral $\mathrm{Nb}$ resonators a promising candidate for quantum devices that require circuit elements with high impedance and magnetic-field resilience.

The characterization of nonstabilizerness is fruitful due to its application in gate synthesis and classical simulation. In particular, the resource monotone called the stabilizer extent is a useful tool to estimate the simulation cost using rank-based simulators, one of the state-of-the-art simulators of Clifford+$T$ circuits. In this work, we propose faster numerical algorithms to compute the stabilizer extent. Our algorithm utilizes the column generation method, which iteratively updates the subset of pure stabilizer states used for calculation. This subset is selected based on the overlaps between all stabilizer states and a target state. In order to update the subset, we make use of a newly proposed subroutine for calculating the stabilizer fidelity that (i) achieves linear time complexity with respect to the number of stabilizer states, (ii) superexponentially reduces the space complexity by in-place calculation, and (iii) prunes unnecessary states for the computation. As a result, our algorithm can compute the stabilizer fidelity and the stabilizer extent for Haar random pure states up to $n=9$ qubits, which naively requires a memory of $305\mathrm{Ei}\mathrm{B}$. We further show that our algorithm runs faster when the target state vector is real. We prove that the problem size is reduced by $\mathcal{O}(2^n)$ compared to the general cases, which makes it computable for the case of $n=10$ qubits.

Quantum gate teleportation provides an approach to introduce nonlinear interactions between long-distance qubits, facilitating distributed quantum computation. We present two alternative protocols for deterministically teleporting controlled-not (cnot) and controlled-phase-flip (cpf) gates, using minimal resources—one entangled qubit (ebit) and two classical bits (cbits). Our protocols eliminate the need for local cnot gates at each party, which are typically required in the standard quantum circuit model [Eisert et al. Phys. Rev. A 62, 052317 (2000)]. Instead, by using a superposition of single-qubit gate orders, our approach makes quantum gate teleportation more experimentally feasible and flexible. Remarkably, the teleportation of the cpf gate and other two-qubit controlled unitary gates can be achieved by adjusting inherent single-qubit operations, reducing the need for single-qubit gates in cnot-based constructions. Additionally, we develop two optical architectures for the teleportation of these gates. The polarization-based and transverse spatial-based setups show that this approach is more feasible and practical, and pave a way for a distributed quantum computing network.

Partial differential equations (PDEs) are crucial for modeling various physical phenomena such as heat transfer, fluid flow, and electromagnetic waves. In computer-aided engineering (CAE), the ability to handle fine resolutions and large computational models is essential for improving product performance and reducing development costs. However, solving large-scale PDEs, particularly for systems with spatially varying material properties, poses significant computational challenges. In this paper, we propose a quantum algorithm for solving second-order linear PDEs of nonconservative systems with spatially varying parameters, using the linear combination of the Hamiltonian simulation (LCHS) method. Our approach transforms those PDEs into ordinary differential equations represented by qubit operators, through spatial discretization using the finite-difference method. Then, we provide an algorithm that efficiently constructs the operator corresponding to the spatially varying parameters of PDEs via a logic minimization technique, which reduces the number of terms and subsequently the circuit depth. We also develop a scalable method for realizing a quantum circuit for LCHS, using a tensor-network-based technique, specifically a matrix product state (MPS). We validate our method with applications to the acoustic equation with spatially varying parameters and the dissipative heat equation. Our approach includes a detailed recipe for constructing quantum circuits for PDEs, leveraging efficient encoding of spatially varying parameters of PDEs and scalable implementation of LCHS, which we believe marks a significant step towards advancing quantum computing’s role in solving practical engineering problems.

The two-qubit entangler is one of the most important components of quantum computers. The cross-cross resonance (CCR) gate has recently been proposed and shown to operate faster than the cross-resonance (CR) gate as an iSWAP gate. However, unlike the CR gate, the CCR gate requires frequency modifications of the input microwave pulses induced by a Stark shift. We present a theoretical study on the CCR gate in the two-transmon system. Using the Schrieffer-Wolff transformation and the rotating-wave approximation, we obtain both the effective Hamiltonian and the input microwave pulse frequencies of the CCR gate. It is well known that the static $ZZ$ term degrades the performance of the CR gate in the transmon system. For the CCR gate, an additional dynamic $ZZ$ term arises from the combined coupling to the qubits by the input microwave pulses, whose strength increases as the product of the two pulse amplitudes ${\mathrm{\Omega }}_0{\mathrm{\Omega }}_1$. We perform numerical simulations using the set of parameters for the IBM Hanoi quantum processor. We show that the CCR gate has a phase tunability and that varying the relative phase of the input microwave pulses can significantly improve the gate performance. This demonstrates that the static $ZZ$ term in the CCR gate can be effectively reduced by optimizing the relative phase of the input microwave pulses.

Quantum teleportation is not merely a fascinating corollary of quantum entanglement, it also finds utility in quantum processing and circuit compilation. In this paper, we measure and track the entanglement and fidelity of two-qubit states prepared on a 127-qubit IBM Quantum computer, as one of the qubits is teleported across 19 qubits. We design, evaluate, and compare two distinct approaches to teleportation: postselected measurement categorization and dynamic circuit corrections based on midcircuit measurements, and compare with direct-state transportation using swap gates. By optimally choosing the teleportation path, which exhibits the highest total negativity entanglement measure across nearest-neighbor pairs, we show the entanglement of a two-qubit graph state is sustained after at least 19 hops in teleportation using the postselection approach and 17 hops using the dynamic circuit approach. We observe a higher level of teleported entanglement in paths determined from two-qubit negativities compared to those obtained from gate errors, demonstrating an advantage in using the negativity map over the gate error map for compiling quantum circuits.

Continuous-variable measurement-device-independent quantum key distribution (CV-MDI QKD) can address vulnerabilities on the detection side of a QKD system. The core of this protocol involves continuous-variable Bell measurements performed by an untrusted third party. However, in high-speed systems, spectrum broadening causes Bell measurements to deviate from the ideal single-mode scenario, resulting in mode mismatches, reduced performance, and compromised security. Here, we introduce temporal modes (TMs) to analyze the performance of CV-MDI QKD under continuous-mode scenarios. The mismatch between Bob’s transmitting mode and Bell-measurement mode has a more significant effect on system performance compared to that on Alice’s side. When the Bell receiver is close to Bob and the mismatch is set to just 5%, the transmission distance drastically decreases from 87.96 km to 18.50 km. In comparison, the same mismatch for Alice reduces the distance to 86.83 km. This greater degradation on Bob’s side can be attributed to the asymmetry in the data modification step. Furthermore, the mismatch in TM characteristics leads to a significant reduction in the secret key rate by 83% when the transmission distance is set to 15 km, which severely limits the practical usability of the protocol over specific distances. These results indicate that, in scenarios involving continuous-mode interference, such as large-scale MDI network setups, careful consideration of each user’s TM characteristics is crucial. Rigorous precalibration of these modes is essential to ensure the system’s reliability and efficiency.

Achieving high-quality solutions faster than classical solvers on computationally hard problems is a challenge for quantum optimization to deliver utility. Using a superconducting quantum computer, we experimentally investigate the performance of a hybrid quantum-classical algorithm inspired by semidefinite programming approaches for solving the maximum-cut problem on 3-regular graphs up to several thousand variables. We leverage the structure of the input problems to address sizes beyond what current quantum machines can naively handle. We attain an average approximation ratio of 99% over a random ensemble of thousands of problem instances. We benchmark the quantum solver against similarly high-performing classical heuristics, including the gurobi optimizer, simulated annealing, and the Burer-Monteiro algorithm. A run-time analysis shows that the quantum solver on large-scale problems is competitive against gurobi but short of others on a projected 100-qubit quantum computer. We explore multiple leads to close the gap and discuss prospects for a practical quantum speedup.

Stimulated Raman adiabatic passage (STIRAP) is a powerful technique for robust state transfer capabilities in quantum systems. This method, however encounters challenges for its implementation as a gate in qubit-subspace due to its sensitivity to initial states. By incorporating single-photon detuning into the protocol, the sensitivity to the initial state can effectively be mitigated, enabling STIRAP to operate as a gate. In this study, we experimentally demonstrate the implementation of robust $\pi$ and $\pi /2$ rotations in a dual-rail qubit formed by two strongly coupled fixed-frequency transmon qubits. We achieve state preparation fidelity in excess of 0.98 using such rotations. Our analysis reveals these gates exhibit significant resilience to errors. Furthermore, our numerical calculations confirm that these gates can achieve fidelity levels in excess of 0.999. This work suggest a way for realizing quantum gates which are robust against minor drifts in pulse or system parameters.

Microwave cavity modes with long coherence times are used in many different quantum computing systems. During normal operation of such systems, these modes, called “memory modes,” often need to be set to different coherent photonic occupations. In this work we present a technique we call “Rabi-driven reset” in which the state of a memory mode is transferred into a decaying mode. This is done through a Rabi-driven qubit that is coupled to both modes via sideband driving tones. The outcome of the method is the initialization of the memory mode in any required coherent state. Simulations are presented to demonstrate the effectiveness of this technique, along with a comparison to an existing coupling method. Our simulations predict a decrease of an order of magnitude in initialization times compared with existing methods.

Low-noise amplifiers are of great significance in the field of quantum technologies. We study a thermally driven parametric amplifier based on a superconductor-insulator-graphene-insulator-superconductor ($S$-$I$-$G$-$I$-$S$) junction coupled to a superconducting microwave cavity. The strong nonlinearity in the temperature dependence of our device leads to thermal self-modulation that produces impedance oscillations at frequencies around twice the angular cavity resonance frequency ${\omega }_{\mathrm{r}}$. In particular, reactance modulation of the effective capacitance yields a gain of 18.6 dB over a frequency span of 125 kHz with a minimum noise temperature of $T_{\mathrm{N}}=1.4\mathrm{K}$. Our theoretical modeling gives insight into the exact mixing processes, confirmation of the electron-phonon coupling parameter and possible improvements of the studied system.

Demonstrating the utility of quantum algorithms is a long-standing challenge, where quantum machine learning becomes one of the most promising candidates that can be resorted to. In this study, we investigate a quantum neural compressive-sensing algorithm for ghost imaging to showcase its utility. The algorithm utilizes the variational quantum circuits to reparameterize the inverse problem of ghost imaging and uses the inductive bias of the physical forward model to perform optimization. To validate the algorithm’s effectiveness, we conduct optical ghost-imaging experiments, capturing signals from objects at different physical sampling rates and detection signal-to-noise ratios. The experimental results show that our proposed algorithm surpasses conventional methods in both visual appearance and quantitative metrics, achieving state-of-the-art performance. Of note, we observe that the quantum neural network, guided by prior knowledge of physics, effectively overcomes the challenge of barren plateau in the optimization process. The proposed algorithm demonstrates robustness against various quantum noise levels, making it suitable for near-term quantum devices. Our study leverages a physical inductive-bias-guided variational quantum algorithm, underscoring the potential of quantum computation in tackling a broad range of optimization and inverse problems.