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The experimental investigation of quantum devices incorporating mechanical resonators has opened up new frontiers in the study of quantum mechanics at a macroscopic level. It has recently been shown that surface acoustic waves (SAWs) can be piezoelectrically coupled to superconducting qubits, and confined in high-quality Fabry–Perot cavities in the quantum regime. Here we present measurements of a device in which a superconducting qubit is coupled to a SAW cavity, realising a surface acoustic version of cavity quantum electrodynamics. We use measurements of the AC Stark shift between the two systems to determine the coupling strength, which is in agreement with a theoretical model. This quantum acoustodynamics architecture may be used to develop new quantum acoustic devices in which quantum information is stored in trapped on-chip acoustic wavepackets, and manipulated in ways that are impossible with purely electromagnetic signals, due to the 10 5 times slower mechanical waves. In this work, Manenti et al. present measurements of a device in which a tuneable transmon qubit is piezoelectrically coupled to a surface acoustic wave cavity, realising circuit quantum acoustodynamic architecture. This may be used to develop new quantum acoustic devices.

Superposition states created with more than 100 photons enable the storage of multiple bits of quantum information. [Also see Report by Vlastakis et al. ] When Schrödinger came up with his thought experiment connecting the fate of a cat in a box to the quantum-mechanical process of radioactive decay ( 1 ), he probably did not consider that the idea might one day be used in technology. However, the transfer of the state of a superconducting quantum bit (qubit) to a 100-photon light state to map and store the contained quantum information, as reported by Vlastakis et al. on page 607 of this issue ( 2 ), is analogous to this iconic thought experiment. In contrast with the original version, however, the researchers are in complete control of the process and envision its use to store multiple bits of quantum information in a future quantum processor.

Josephson parametric amplification is a tool of paramount importance in circuit-QED especially for the quantum-noise-limited single-shot read-out of superconducting qubits. We developed a Josephson parametric amplifier (JPA) based on a lumped-element LC resonator, in which the inductance L is composed by a geometric inductance and an array of 4 superconducting quantum interference devices (SQUIDs). We characterized the main figures of merit of the device, obtaining a −3 dB bandwidth BW = 15 MHz for a gain G = 21 dB and a 1 dB compression point P 1dB = −115 dBm. The obtained results are promising for the future use of such JPA as the first stage of amplification for single-shot readout of superconducting qubits.

Russian countryside is some of the world's most lovely, from the celebrated explosions of wild flowers that fill its forests in the spring, to the icy winter tundra that defeated the advances of Napoleon and Hitler, and provided the backdrop for the drama of many of Russian literature's celebrated scenes. And no one immortalized it better than Ivan Shishkin (1832-1898), a Russian landscape painter. In this comprehensive work of scholarship, Irina Shuvalova and Victoria Charles make a thorough examination of Shishkin's work.

Gate-set tomography (GST) characterizes the process matrix of quantum logic gates, along with measurement and state preparation errors in quantum processors. GST typically requires extensive data collection and significant computational resources for model estimation. We propose a more efficient GST approach for qudits, utilizing the qudit Hadamard and virtual Z gates to construct fiducials while assuming virtual Z gates are error-free. Our method reduces the computational costs of estimating characterization results, making GST more practical at scale. We experimentally demonstrate the applicability of this approach on a superconducting transmon qutrit.

Using quantum systems with more than two levels, or qudits, can scale the computation space of quantum processors more efficiently than using qubits, which may offer an easier physical implementation for larger Hilbert spaces. However, individual qudits may exhibit larger noise, and algorithms designed for qubits require to be recompiled to qudit algorithms for execution. In this work, we implemented a two-qubit emulator using a 4-level superconducting transmon qudit for variational quantum algorithm applications and analyzed its noise model. The major source of error for the variational algorithm was readout misclassification error and amplitude damping. To improve the accuracy of the results, we applied error-mitigation techniques to reduce the effects of the misclassification and qudit decay event. The final predicted energy value is within the range of chemical accuracy. Our work demonstrates that qudits are a practical alternative to qubits for variational algorithms.

A powerful control method in experimental quantum computing is the use of spin echoes, employed to select a desired term in the system's internal Hamiltonian, while refocusing others. Here we address a more general problem, describing a method to not only turn on and off particular interactions but also to rescale their strengths so that we can generate any desired effective internal Hamiltonian. We propose an algorithm based on linear programming for achieving time-optimal rescaling solutions in fully coupled systems of tens of qubits, which can be modified to obtain near time-optimal solutions for rescaling systems with hundreds of qubits.

Machine learning is seen as a promising application of quantum computation. For near-term noisy intermediate-scale quantum (NISQ) devices, parametrized quantum circuits (PQCs) have been proposed as machine learning models due to their robustness and ease of implementation. However, the cost function is normally calculated classically from repeated measurement outcomes, such that it is no longer encoded in a quantum state. This prevents the value from being directly manipulated by a quantum computer. To solve this problem, we give a routine to embed the cost function for machine learning into a quantum circuit, which accepts a training dataset encoded in superposition or an easily preparable mixed state. We also demonstrate the ability to evaluate the gradient of the encoded cost function in a quantum state.