Guest Editors Muamer Kadic, Daniel Torrent, and Abdelkrim Khelif present a collection of papers in Physical Review Applied on sound-wave research, both to offer a comprehensive view of diverse developments in the field and to memorialize a colleague.

Topological mechanical metamaterials offer platforms to control the propagation of mechanical waves, but are challenging to integrate into physical systems, because of their complex fabrication requirements. This study uses theoretical arguments and numerical simulations to design a self-supporting topological mechanical metamaterial that can isolate vibrations from sensitive payloads. Improving the ability to isolate vibration, however, comes at the cost of the ability to support an external load. The architecture described here shows promise for integrating topological mechanical metamaterials into engineered solutions to vibration isolation and impact mitigation.

Wood, plants, and cellulosic materials contain a lot of bound water, present as nanoscale inclusions within a solid matrix. That bound water plays a major role in drying and moisture exchange, but exactly how is poorly understood. This work examines the transport of bound water by filling wood’s pores with oil and observing via MRI. Surprisingly, bound water’s diffusivity is independent of concentration and direction, and depends exponentially on inverse temperature, suggesting an activated process. As long as a local deficit exists, bound water enables efficient, long-distance moisture transport through plant structures, whether voids are closed, empty, or full of nonaqueous liquid.

This article presents a compact, broadband SNAIL (superconducting nonlinear asymmetric inductive element) parametric amplifier that delivers flat-top 20-dB gain across 250 MHz, with near-quantum-limited noise performance. By combining impedance matching with an on-chip pump filter, the device remains robust, reproducible, and straightforward to fabricate. The authors demonstrate simultaneous high-fidelity readout of multiple qubits, with negligible crosstalk, showing clear promise for scaling up quantum processors and improving microwave measurement technologies.

If-then-else statements are at the heart of computer programming. Their quantum counterparts are multicontrolled (MC) quantum gates, which form the bedrock of most quantum algorithms, making it crucial to implement them through the cheapest (least number of fundamental gates) quantum circuits possible. This study shows that MC gates can be implemented at linear cost, even for the most restricted qubit connectivity. Unlike previous approaches with quadratic cost just to swap qubits and bring them next to each other, the methods here avoid extra cost by implementing arbitrary MC gates without such swaps. For ${10}^3$ qubits, this means using ${10}^4$ CNOT gates instead of ${10}^6$.

In quantum metrology, Lindblad estimation spans a host of applications, from probing weak stochastic signals to noise spectroscopy and characterization of emerging technologies. Regrettably, the ultimate quantum limits of Lindblad estimation are not understood in general. By examining the performance of the optimal sequential strategy that can simulate any other metrological strategy, the authors show that it is best to rapidly projectively measure and reinitialize the quantum state. This protocol may accelerate searches for stochastic gravitational waves, quantum gravity, and axionic dark matter, and also may enhance noise spectroscopy with qubits and Pauli Lindblad estimation.

Networks of nonlinear resonators can emulate complex systems and perform analog computations. Nevertheless, combining high quality factors with strongly coupled and tunable nonlinear resonators in a scalable architecture remains challenging. In this work, the authors realize a network of parametrically driven silicon nitride membranes that fulfills all of these criteria. By metallizing the membranes and actuating them capacitively, the team achieves tunable frequencies and controllable coupled parametric responses. This platform could be used to tackle computationally hard problems and explore emergent collective phenomena.

Josephson traveling-wave parametric amplifiers (JTWPAs) are key to fast, frequency-multiplexed measurements in superconducting circuits. JTWPAs with periodic modulation are attractive, as they phase match without flux or dc bias, but they often suffer from significant gain ripples. Another common limitation in JTWPAs is intrinsic loss, which has prevented them from reaching the quantum limit of added noise. Here researchers address both issues by implementing an all-aluminum coplanar lumped-element JTWPA, using a qubit-compatible fabrication recipe. Their modulated device suppresses gain ripples for smooth, bias-free amplification, and approaches the quantum limit of added noise.

The planned redefinition of the second in the international system of units (SI), to be based on optical clocks, requires instruments with low uncertainty and high uptime. By tackling the dominant contributions to systematic uncertainty, the authors demonstrate a strontium single-ion optical clock with an estimated total systematic uncertainty of 7.9×10${}^{-19}$, among the lowest reported to date. Measuring its absolute frequency against International Atomic Time over 10 months with an uptime of 84% yields good agreement with other recent measurements and a total uncertainty of 9.8×10${}^{-17}$, setting a record for accuracy in frequency measurement.

Generating of out-of-plane-polarized spin currents is essential for advancing spintronic applications, yet remains challenging due to the inherent limitations of conventional heavy metals, as well as difficulties in fabricating suitable materials. The authors overcome this challenge through structural design of heavy-metal layers with asymmetry, inducing the desired spin currents and enabling deterministic switching of perpendicular magnetization without applying a magnetic field. The team also identifies the essential structural conditions required for out-of-plane spin polarization within these configurations. This approach offers a feasible pathway toward practical spintronic devices.

The spatial photonic Ising machine (SPIM) is a promising architecture that leverages the scalability and parallelism of photons to solve Ising problems in combinatorial optimization. SPIMs have been held back fundamentally by limited computational accuracy. This study overcomes that limitation through a hybrid approach, integrating aberration correction with a dynamic algorithm to update simultaneously a number of flipped spins, rather than just one. The method demonstrably improves computational accuracy and presents a way for SPIMs to scale to significantly larger systems (order 10${}^5$ spins), a milestone on the path to practical application.

This study develops a method to overcome the effect of loss in optical fibers, which is one of the main obstacles to long-distance quantum communication. The work discusses an architecture for quantum repeaters, in which information is encoded in error-correcting codes capable of handling both photon loss and logical errors. Advantages of this approach are that it can work with very limited resources at each repeater station, and is more robust to errors than previous techniques. The results can promote long-distance quantum communication at very high rates.

Quantum overlapping tomography (QOT), which focuses on reconstructing subsystems of quantum systems, has emerged as a promising approach for quantum state learning, especially when full-state tomography is infeasible. QOT has attracted considerable interest and seen substantial development, but has not yet reached its ultimate limit. The authors introduce a unified framework for optimal QOT by mapping the problem to the “clique cover” model from graph theory. This framework provides superlative efficiency and experimental feasibility in measurement strategies. This validation of QOT’s utility paves the way for advanced quantum system characterization and state-property learning.

Coherent exchange of quantum information between atoms and photons using cavities is a key tool in quantum science, but the challenge remains to attain the coupling strengths necessary for deterministic atom-atom entanglement while maintaining sufficient optical access for single-atom trapping. The authors develop and characterize an optically accessible millimeter-wave Fabry-Perot resonator with finesse of $5.8(1)\times {10}^7$ and numerical aperture of 0.56. Careful tuning of the cavity’s geometry yields the high finesse, which will enable high-fidelity cavity-mediated entanglement with trapped atomic arrays.

Traditional spin valves are key to spintronics but require a lot of power to switch, making them difficult to integrate with low-power electronic chips. In this Letter the authors design a “Rashba spin valve” that uses the electric field to control the flow of electron spins, achieving a very high on-off ratio and operating without any magnetic elements, which drastically cuts its power needs and material requirements. The effect is strong and reliable, even in imperfect two-dimensional materials. These findings suggest a route toward creating ultralow-power spintronic devices that are controlled entirely by electricity.

Finding crystalline semiconductors with intrinsically low lattice thermal conductivity ${\kappa }_l$ is crucial for thermoelectric applications, as tuning ${\kappa }_l$ often compromises electrical transport properties. Using density functional theory, Boltzmann transport theory, and explicit consideration of phonon-phonon and electron-phonon interactions, the authors show that BaCuGdTe${}_3$ exhibits ultralow ${\kappa }_l$, due to its layered structure and local distortions that strongly suppress acoustic phonon modes, and excellent electrical transport properties, due to strong covalent bonding within its layers. These results can be used as a basis for designing compounds with ultralow ${\kappa }_l$.

Revealing the generation mechanism of oxygen-vacancy defect centers $E_{\gamma }^{\prime }$ in $a$-SiO${}_2$ is at the heart of hardening silicon electronics against radiation damage in extreme applications. The nature of these defect centers is not fully understood, though. This study addresses the problem using state-of-the-art first-principles calculations, and proposes a “nonradiative carrier capture–structural relaxation” mechanism and a fractional-power-law model to predict defect generation and evolution dynamics. The calculated defect behaviors align with experiments over wide dose and temperature ranges, and can support a range of oxygen-vacancy-related reliability and functionality studies.

Active metamaterials with nonreciprocal couplings guide waves unidirectionally, but these waves often suffer from amplitude growth or decay, and dispersion. By harnessing nonlinearity, the authors demonstrate a powerful balancing mechanism that stabilizes such waves into robust solitons that propagate undistorted through nonreciprocal lattices. This finding opens the door to tunable, one-way transmission of energy and signals in active non-Hermitian media.

Wood, plants, and cellulosic materials contain a lot of bound water, present as nanoscale inclusions within a solid matrix. That bound water plays a major role in drying and moisture exchange, but exactly how is poorly understood. This work examines the transport of bound water by filling wood’s pores with oil and observing via MRI. Surprisingly, bound water’s diffusivity is independent of concentration and direction, and depends exponentially on inverse temperature, suggesting an activated process. As long as a local deficit exists, bound water enables efficient, long-distance moisture transport through plant structures, whether voids are closed, empty, or full of nonaqueous liquid.

This article presents a compact, broadband SNAIL (superconducting nonlinear asymmetric inductive element) parametric amplifier that delivers flat-top 20-dB gain across 250 MHz, with near-quantum-limited noise performance. By combining impedance matching with an on-chip pump filter, the device remains robust, reproducible, and straightforward to fabricate. The authors demonstrate simultaneous high-fidelity readout of multiple qubits, with negligible crosstalk, showing clear promise for scaling up quantum processors and improving microwave measurement technologies.

Analog in-memory computing based on crossbar arrays offers a path to energy-efficient AI hardware, but has been limited by reliance on bulky, power-hungry analog-to-digital converters. This study introduces stochastic nanomagnets driven by spin-orbit torque as intrinsic analog-to-digital interfaces, enabling compact, fast, low-power digitization while maintaining high computational accuracy. These results suggest a promising direction for energy-efficient AI hardware accelerators and significant advances in next-generation machine-learning hardware.

If-then-else statements are at the heart of computer programming. Their quantum counterparts are multicontrolled (MC) quantum gates, which form the bedrock of most quantum algorithms, making it crucial to implement them through the cheapest (least number of fundamental gates) quantum circuits possible. This study shows that MC gates can be implemented at linear cost, even for the most restricted qubit connectivity. Unlike previous approaches with quadratic cost just to swap qubits and bring them next to each other, the methods here avoid extra cost by implementing arbitrary MC gates without such swaps. For ${10}^3$ qubits, this means using ${10}^4$ CNOT gates instead of ${10}^6$.

Oscillator-based Ising machines are currently explored for their potential to solve complex optimization problems. A key challenge lies in developing theoretically sound models that capture both phase and amplitude dynamics of a given system. This study uses Wirtinger calculus to develop a rigorous mathematical framework with a complex-valued oscillator representation, introducing a real-valued energy function and corresponding dynamics that faithfully represent the system’s behavior. This provides a stronger theoretical foundation and practical design principles for engineering next-generation oscillator-based Ising machines.

Topological mechanical metamaterials offer platforms to control the propagation of mechanical waves, but are challenging to integrate into physical systems, because of their complex fabrication requirements. This study uses theoretical arguments and numerical simulations to design a self-supporting topological mechanical metamaterial that can isolate vibrations from sensitive payloads. Improving the ability to isolate vibration, however, comes at the cost of the ability to support an external load. The architecture described here shows promise for integrating topological mechanical metamaterials into engineered solutions to vibration isolation and impact mitigation.

Synaptic spintronic devices based on domain-wall motion driven by spin-orbit torque are appealing for neuromorphic computing applications. Unlike in other technologies, though, here the long-term stability of multiple consecutive synaptic states is rare. To address this issue, the authors exploit the high thermal stability of magnetization in a heavy-metal/ferromagnetic-metal stack with graded thickness of the heavy metal. In micrometer-scale devices made from the stack, they demonstrate the stability of approximately 30 synaptic states for up to 1200 seconds, with some states stable for up to 50,000 seconds. These results pave the way for spintronic neuromorphic computing technologies.

In quantum metrology, Lindblad estimation spans a host of applications, from probing weak stochastic signals to noise spectroscopy and characterization of emerging technologies. Regrettably, the ultimate quantum limits of Lindblad estimation are not understood in general. By examining the performance of the optimal sequential strategy that can simulate any other metrological strategy, the authors show that it is best to rapidly projectively measure and reinitialize the quantum state. This protocol may accelerate searches for stochastic gravitational waves, quantum gravity, and axionic dark matter, and also may enhance noise spectroscopy with qubits and Pauli Lindblad estimation.

A neuromorphic metasurface embodies mechanical intelligence by realizing physical neural architectures, enabling computational capabilities within an elastic substrate with minimal energy requirements. However, well-established constraints on wave propagation in finite media have limited such systems to a single task. This work introduces a wave-based dual classifier that utilizes a single neuromorphic metasurface to concurrently execute two completely independent classification problems, via dynamic modulation of embedded waveguides. These results point to paradigms in wave-based computing systems that have thus far been elusive.

Steering opposed elastic waves independently with a single platform is crucial for multifunctional mechanical devices, but has remained out of reach. This study presents a Janus elastic metasurface that enables independent bidirectional control of broadband flexural waves, supported by a comprehensive theoretical framework and experimental verification. Leveraging high reflection and bianisotropic phase shifts, the design overcomes the one-sided limitation of conventional elastic metasurfaces. This innovative advance opens avenues to multifunctional devices, vibration control, and energy harvesting.

Networks of nonlinear resonators can emulate complex systems and perform analog computations. Nevertheless, combining high quality factors with strongly coupled and tunable nonlinear resonators in a scalable architecture remains challenging. In this work, the authors realize a network of parametrically driven silicon nitride membranes that fulfills all of these criteria. By metallizing the membranes and actuating them capacitively, the team achieves tunable frequencies and controllable coupled parametric responses. This platform could be used to tackle computationally hard problems and explore emergent collective phenomena.

Probabilistic Ising machines (PIMs) show promise in solving optimization problems. However, the binary nature of probabilistic bits (p-bits) does not permit the natural mapping of more than two state variables, which are common in real-world applications. To sidestep the potential increase in time to solution for these problems, the authors investigate the concept of a d-dimensional probabilistic bit (p-dit). Three different implementations of p-dit-based computers show large improvements over traditional PIMs, showcasing their adaptability.

Josephson traveling-wave parametric amplifiers (JTWPAs) are key to fast, frequency-multiplexed measurements in superconducting circuits. JTWPAs with periodic modulation are attractive, as they phase match without flux or dc bias, but they often suffer from significant gain ripples. Another common limitation in JTWPAs is intrinsic loss, which has prevented them from reaching the quantum limit of added noise. Here researchers address both issues by implementing an all-aluminum coplanar lumped-element JTWPA, using a qubit-compatible fabrication recipe. Their modulated device suppresses gain ripples for smooth, bias-free amplification, and approaches the quantum limit of added noise.

The planned redefinition of the second in the international system of units (SI), to be based on optical clocks, requires instruments with low uncertainty and high uptime. By tackling the dominant contributions to systematic uncertainty, the authors demonstrate a strontium single-ion optical clock with an estimated total systematic uncertainty of 7.9×10${}^{-19}$, among the lowest reported to date. Measuring its absolute frequency against International Atomic Time over 10 months with an uptime of 84% yields good agreement with other recent measurements and a total uncertainty of 9.8×10${}^{-17}$, setting a record for accuracy in frequency measurement.

Generating of out-of-plane-polarized spin currents is essential for advancing spintronic applications, yet remains challenging due to the inherent limitations of conventional heavy metals, as well as difficulties in fabricating suitable materials. The authors overcome this challenge through structural design of heavy-metal layers with asymmetry, inducing the desired spin currents and enabling deterministic switching of perpendicular magnetization without applying a magnetic field. The team also identifies the essential structural conditions required for out-of-plane spin polarization within these configurations. This approach offers a feasible pathway toward practical spintronic devices.

The spatial photonic Ising machine (SPIM) is a promising architecture that leverages the scalability and parallelism of photons to solve Ising problems in combinatorial optimization. SPIMs have been held back fundamentally by limited computational accuracy. This study overcomes that limitation through a hybrid approach, integrating aberration correction with a dynamic algorithm to update simultaneously a number of flipped spins, rather than just one. The method demonstrably improves computational accuracy and presents a way for SPIMs to scale to significantly larger systems (order 10${}^5$ spins), a milestone on the path to practical application.

This study develops a method to overcome the effect of loss in optical fibers, which is one of the main obstacles to long-distance quantum communication. The work discusses an architecture for quantum repeaters, in which information is encoded in error-correcting codes capable of handling both photon loss and logical errors. Advantages of this approach are that it can work with very limited resources at each repeater station, and is more robust to errors than previous techniques. The results can promote long-distance quantum communication at very high rates.

Quantum overlapping tomography (QOT), which focuses on reconstructing subsystems of quantum systems, has emerged as a promising approach for quantum state learning, especially when full-state tomography is infeasible. QOT has attracted considerable interest and seen substantial development, but has not yet reached its ultimate limit. The authors introduce a unified framework for optimal QOT by mapping the problem to the “clique cover” model from graph theory. This framework provides superlative efficiency and experimental feasibility in measurement strategies. This validation of QOT’s utility paves the way for advanced quantum system characterization and state-property learning.

A single magnetic tunnel junction (MTJ) can operate as an entropy source for probabilistic bits (p-bits), thanks to tunable stochasticity, CMOS compatibility, and room-temperature operation. The average probability measured from the random signals generated by MTJs can be tuned through e.g. spin torques or voltage-controlled exchange coupling, and furthermore such mechanisms can be combined. This article reviews experimental and theoretical work on all of the biasing mechanisms that have been proposed for MTJ-based p-bits, and provides an overview of the advantages and disadvantages of each biasing mechanism.