Extracting information from weak optical signals is a critical challenge across a broad range of technologies. Conventional imaging techniques, constrained to integrating over detected signals and classical postprocessing, are limited in signal-to-noise ratio from shot noise accumulation in the postprocessing algorithms. We show that these limitations can be circumvented by coherently encoding photonic amplitude information into qubit registers and applying quantum algorithms to process the stored information from asynchronously arriving optical signals. As a specific example, we develop a quantum algorithm for imaging unresolved point sources and apply it to exoplanet detection. We demonstrate that orders-of-magnitude improvements in performance can be achieved under realistic imaging conditions using relatively small-scale quantum processors.

Current quantum computing platforms suffer from readout errors, where faulty measurement outcomes are reported by the device. These errors are particularly harmful in quantum programs that rely on branch statements, where operations in later parts of the program are dynamically determined by mid-circuit measurements. We propose a general protocol for mitigating mid-circuit measurement errors. Our solution works for any number of mid-circuit measurement and feedforward layers without increasing circuit depth or two-qubit gate counts, is efficient under structural assumptions on error channels, and is suitable for immediate application on noisy intermediate-scale quantum devices. We experimentally demonstrate up to a $\sim 60\mathrm{\%}$ reduction in error on superconducting quantum processors across several practically relevant feedforward circuits, including dynamic qubit resets, shallow-depth Greenberger–Horne–Zeilinger state preparation, and multistage quantum teleportation. This work paves the way for more error-resilient adaptive quantum circuits, crucial for current and future quantum computing applications.

Fusion-based quantum computing with dual-rail qubits is a leading candidate for scalable quantum computing using linear optics. This paradigm requires single photons which are entangled into small resource states before being fed into a fusion network. The most common sources for single optical photons and for small entangled states are probabilistic and heralded. The realization of a single reliable deterministic source requires many redundant probabilistic sources and a complex optical network for rerouting and retiming probabilistic outputs. In this work, we show how optomechanics enables reliable production of resources for photonic quantum computing without the redundancy of the all-optical approach. This is achieved by using acoustic modes as caches of quantum resources, ranging from single-particle states to small entangled states, with on-demand readout. The advantages of acoustic modes as optical quantum memories, compared to other technologies, include their intrinsically long lifetimes and that they are solid state, highly tailorable, and insensitive to electromagnetic noise. We show how the resource states can be prepared directly in the acoustic modes using optical controls. This is still probabilistic and heralded, as in the all-optical approach, but the acoustic modes act as a quantum memory which is integrated into the production of the states. The quantum states may be deterministically transferred from acoustic modes to optical modes, on demand, with another optical drive.

Preparation of high-fidelity logical magic states is crucial for fault-tolerant quantum computation. Among previous attempts to reduce the substantial cost of magic state preparation, magic state cultivation (MSC) [Gidney et al., Magic state cultivation: growing T states as cheap as CNOT gates, arXiv:2409.17595], a recently proposed protocol for logical $\mathrm{T}$ state preparation without magic state distillation, achieves state-of-the-art efficiency. Inspired by this work [Gidney et al., Magic state cultivation: growing T states as cheap as CNOT gates, arXiv:2409.17595], we propose a MSC procedure that can produce logical $\mathrm{T}$ states on the rotated surface code at a further reduced cost. To maintain high efficiency throughout our protocol, we design structured codes along with compact circuits bridging between them. More specifically, we construct a code family, the RP code, by putting the rotated surface code on ${\mathbb{R}\mathbb{P}}^2$ (a two-dimensional manifold), as well as two self-dual Calderbank-Shor-Steane codes, named SRP-3 and SRP-5, respectively. In our MSC protocol, we start with a cultivation process, in which a high-fidelity $\mathrm{T}$ state is prepared on a small RP code with distance 3 or 5. Then, to preserve the logical $\mathrm{T}$ state, we use an efficient and easy-to-decode expansion stage to grow a small RP code to a larger rotated surface code in one syndrome extraction (SE) round. The RP code serves as an efficient transfer station with efficient SE circuits and compact interfaces between the SRP-3 (or SRP-5) code—used in the cultivation process to efficiently verify the correctness of the logical $\mathrm{T}$ state—and larger rotated surface codes for preserving the prepared logical $\mathrm{T}$ state. Our MSC protocol utilizes nonlocal connectivity, available on both neutral atom array and ion trap platforms. According to our Monte Carlo sampling results, our MSC protocol requires about an order of magnitude smaller space-time volume to reach a target logical error rate of around ${10}^{-9}$ compared to the original MSC protocol.

The optimal quantum measurements for estimating different unknown parameters in a parameterized quantum state are usually incompatible with each other. Traditional approaches to addressing the measurement incompatibility issue, such as the Holevo Cramér-Rao bound, suffer from multiple difficulties toward practical applicability, as the optimal measurement strategies are usually state-dependent, difficult to implement and also take complex analyses to determine. Here we study randomized measurements as an approach for multiparameter quantum metrology. We show quantum measurements on single copies of quantum states given by $3$-designs perform near-optimally when estimating an arbitrary number of parameters in pure states and more generally, approximately low-rank well-conditioned states, whose metrological information is largely concentrated in a low-dimensional subspace. The near-optimality is also shown in estimating the maximal number of parameters for three types of mixed states that are well conditioned on their supports. Examples of fidelity estimation and Hamiltonian estimation are explicitly provided to demonstrate the power and limitation of randomized measurements in multiparameter quantum metrology.

Quantum spin Hall insulators (QSHIs) host helical electronic edge states that are protected from backscattering due to time-reversal symmetry (TRS). Despite considerable work investigating QSHI edge states, there is still an open question about their unexpected resilience to large magnetic fields where TRS is undoubtedly broken. In this work, we investigate the transport properties of helical edge states in a QSHI-superconductor (QSHI-SC) junction formed by a $\mathrm{In}\mathrm{As}$(15 nm)/$\mathrm{Ga}\mathrm{Sb}$(5 nm) double quantum well and a superconducting tantalum ($\mathrm{Ta}$) constriction. We observe a robust conductance plateau up to 2 T, signaling resilient edge-state transport. Using a modified Landauer-Büttiker analysis, we find that the zero-field conductance is consistent with 98% Andreev-reflection probability owing to the high transparency of the ($\mathrm{In}\mathrm{As}$/$\mathrm{Ga}\mathrm{Sb}$)-$\mathrm{Ta}$ interface. Such resilience is consistent with the Dirac point for the edge states being buried in the bulk valence band. We further theoretically show that a buried Dirac point does not affect the robustness of the quasi-one-dimensional topological superconducting phase. We find that a buried Dirac point favors the hybridization of Majorana Kramers pairs (MKPs)—predicted to exist in a QSHI-SC constriction—and fermionic modes in the QSHI vacuum edge resulting in extended MKP states, highlighting the subtle role of buried Dirac points in probing MKPs.

The double descent phenomenon challenges traditional statistical learning theory by revealing scenarios where larger models do not necessarily lead to reduced performance on unseen data. While this counterintuitive behavior has been observed in a variety of classical machine learning models, particularly modern neural network architectures, it remains elusive within the context of quantum machine learning. In this work, we analytically demonstrate that linear regression models in quantum feature spaces can exhibit double descent behavior by drawing on insights from classical linear regression and random matrix theory. Additionally, our numerical experiments on quantum kernel methods across different real-world datasets and system sizes further confirm the existence of a test error peak, a characteristic feature of double descent. Our findings provide evidence that quantum models can operate in the modern, overparameterized regime without experiencing overfitting, potentially opening pathways to improved learning performance beyond traditional statistical learning theory.

We construct examples of highly entangled two-dimensional states by exploiting a correspondence between stochastic processes in $d$ dimensions and quantum states in $d+1$ dimensions. The entanglement structure of these states, which we explicitly calculate, can be tuned between area law, sub-volume law, and volume law. This correspondence also enables a sequential generation protocol: the states can be prepared through a series of unitary transformations acting on an auxiliary system. We also discuss the conditions under which these states have local, frustration-free parent Hamiltonians.

To implement arbitrary quantum circuits in architectures with restricted interactions, one may effectively simulate all-to-all connectivity by routing quantum information. We consider the entanglement dynamics and routing between two regions only connected through an intermediate “bottleneck” region with few qubits. In such systems, where the entanglement rate is restricted by a vertex boundary rather than an edge boundary of the underlying interaction graph, existing results such as the small incremental entangling theorem give only a trivial constant lower bound on the routing time (the minimum time to perform an arbitrary permutation). We significantly improve the lower bound on the routing time in systems with a vertex bottleneck. Specifically, for any system with two regions $L,R$ with $N_L,N_R$ qubits, respectively, coupled only through an intermediate region $C$ with $N_C$ qubits, for any $\delta >0$ we show a lower bound of $\mathrm{\Omega }(N_R^{1-\delta }/\sqrt{N_L}{N}_C)$ on the Hamiltonian quantum routing time when using piecewise time-independent Hamiltonians, or time-dependent Hamiltonians subject to a smoothness condition. We also prove an upper bound on the average amount of bipartite entanglement between $L$ and $C,R$ that can be generated in time $t$ by such architecture-respecting Hamiltonians in systems constrained by vertex bottlenecks, improving the scaling in the system size from $O(N_{L}t)$ to $O(\sqrt{N_L}t)$. As a special case, when applied to the star graph (i.e., one vertex connected to $N$ leaves), we obtain an $\mathrm{\Omega }(\sqrt{N^{1-\delta }})$ lower bound on the routing time and on the time to prepare $N/2$ Bell pairs between the vertices. We also show that, in systems of free particles, we can route optimally on the star graph in time $\mathrm{\Theta }(\sqrt{N})$ using Hamiltonian quantum routing, obtaining a speedup over gate-based routing, which takes time $\mathrm{\Theta }(N)$.

Estimating the fidelity between a desired target quantum state and an actual prepared state is essential for assessing the success of experiments. For pure target states, we use functional representations that can be measured directly and determine the number of copies of the prepared state needed for fidelity estimation. In continuous variable (CV) systems, we use the Wigner function, which can be measured via displaced parity measurements. We provide upper and lower bounds on the sample complexity required for fidelity estimation, considering the worst-case scenario across all possible prepared states. For target states of particular interest, such as Fock and Gaussian states, we find that this sample complexity is characterized by the $L^1$-norm of the Wigner function, a measure of Wigner negativity widely studied in the literature, in particular in resource theories of quantum computation. For discrete variable systems consisting of $n$ qubits, we explore fidelity estimation protocols using Pauli string measurements. Similarly as for the CV approach, the sample complexity is shown to be characterized by the $L^1$-norm of the characteristic function of the target state for both Haar random states and stabilizer states. Furthermore, in a general black box model, we prove that, for any target state, the optimal sample complexity for fidelity estimation is characterized by the smoothed $L^1$-norm of the target state. To the best of our knowledge, this is the first time the $L^1$-norm of the Wigner function provides a lower bound on the cost of some information processing task.

The simplicity of encoding a qubit in the state of a single electron spin and the potential for their integration into industry-standard microchips continue to drive the field of semiconductor-based quantum computing. After a series of key first-principles demonstrations validating universal gate operations, initialization and readout, three-qubit algorithms have already been realized with silicon-based quantum dots in past years. Devices containing more qubits have become available since then but experiments have not gone beyond meeting the DiVincenzo criteria. In this work, we fully exploit the capacity of a spin-qubit array and implement a six-qubit quantum circuit, the largest utilizing semiconductor quantum technology. By programming the quantum processor, we execute quantum circuits across all permutations of three, four, five, and six neighboring qubits, demonstrating successful programmable multi-qubit operation throughout the array. Using an error model that incorporates quasi-static noise allows us to qualitatively explain some key trends in our experimental results and highlight the necessity to minimize idling times through simultaneous operations, extending dephasing times, and consistently improving state preparation and measurement fidelities.

Many-body localization, an emergent quantum phenomenon, challenges the fundamental principles of thermodynamics and has attracted significant attention. In this study, we demonstrate Stark many-body localization in a one-dimensional Bose gas under the continuous limit, a scenario that extends well beyond the conventional tight-binding model commonly employed in most discussions. This localization occurs within a background harmonic trap without any random disorder, where the potential gradient effectively halts thermalization. Contrary to prior studies on Stark localization, our findings reveal that an increasing gradient does not necessarily lead to greater localization. Instead, we observe a complex transition within this continuous interacting system. Another surprising result is that a higher initial density, which typically correlates with a higher collisional rate and thermalization rate, actually induces stronger localization in this one-dimensional model. Additionally, we observe a density plateau with zero compressibility and low-density fluctuations, which may signal an insulating behavior associated with prethermalization during nonequilibrium dynamics.

Protected superconducting qubits such as the 0-$\pi$ qubit promise to substantially reduce physical error rates. However, a key challenge in the field is designing gates for these qubits that do not compromise their protection or become infeasibly slow as the protection of the qubit is improved. In this work we propose a protected phase gate that is compatible with the protected regime of the 0-$\pi$ qubit and does not suffer from spurious coupling to additional circuit modes. Our gate utilizes an internal mode of the circuit as an ancilla and is achieved by varying the qubit-ancilla coupling via a tunable Josephson element. Through numerical simulations, we study how the gate error scales with the circuit parameters of the 0-$\pi$ qubit and the tunable Josephson element that enacts the gate. Ultimately, we find that a protected gate with the 0-$\pi$ qubit is possible with near-term circuit parameters. Our work opens up the possibility of performing protected gates on protected superconducting qubits, which may significantly reduce hardware overheads for quantum computation.

Understanding quantum noise is an essential step towards building practical quantum information processing systems. Pauli noise is a useful model that has been widely applied in quantum benchmarking, error mitigation, and error correction. Despite intensive study into Pauli noise learning, most existing works focus on learning Pauli channels associated with some specific gates rather than treating the gate set as a whole. A learning algorithm that is self-consistent, complete, and efficient at the same time has yet to be established. In this work, we study the task of gate set Pauli noise learning, where a set of quantum gates, state preparation, and measurements all suffer from unknown Pauli noise channels with a customized noise ansatz. Using tools from algebraic graph theory, we analytically characterize the self-consistently learnable degrees of freedom for Pauli noise models with arbitrary linear ansatz, and design experiments to efficiently learn all the learnable information. Specifically, we show that all learnable information about the gate noise can be learned to relative precision, under mild assumptions on the noise ansatz. We then demonstrate the flexibility of our theory by applying it to concrete physically motivated ansatze (such as spatially local or quasi-local noise) and experimentally relevant gate sets (such as parallel CZ gates). These results not only enhance the theoretical understanding of quantum noise learning, but also provide a feasible recipe for characterizing existing and near-future quantum computing devices.

We develop a comprehensive framework for characterizing fluctuations in quantum transport and nonequilibrium thermodynamics using two complementary approaches: full counting statistics and first-passage times. Focusing on open quantum systems governed by Markovian Lindblad dynamics, we derive general ensemble relations that connect the two approaches at all times, and we clarify how the steady states reached at long times relate to those reached at large jump counts. In regimes of metastability, long-lived intermediate states cause violations of experimentally testable cumulant relations, as we discuss. We also formulate a fluctuation theorem governing the probability of rare fluctuations in the first-passage time distributions based on results from full counting statistics. Our results apply to general integer-valued trajectory observables that do not necessarily increase monotonically in time. Three illustrative applications, a two-state emitter, a driven qubit, and a variant of the Su-Schrieffer-Heeger model, highlight the physical implications of our results and provide guidelines for practical calculations. Our framework provides a complete picture of first-passage time statistics in Markovian quantum systems, encompassing multiple earlier results, and it has direct implications for current experiments in quantum optics, superconducting circuits, and nanoscale heat engines.

This tutorial offers a pedagogical guide to hybrid quantum processors that integrate discrete-variable (DV) qubits and continuous-variable (CV) oscillators. Aimed at computer scientists, engineers, and physicists, it provides an overview of the experimental, algorithmic, and architectural aspects of this novel and rapidly developing hardware model. Experimental realizations of this model include superconducting, trapped-ion, and neutral-atom platforms. By combining DV and CV components, hybrid oscillator-qubit processors enable a powerful new paradigm that offers complementary strengths for quantum control, error correction, computation, and simulation. Working toward the goal of a full-stack system connecting applications to CV-DV hardware, we define and formulate abstract machine models and instruction set architectures. These essential abstractions enable codesign of hardware and software, and resource estimation for exploring the potential of current and future hardware for computational and simulation tasks. Using these abstractions, we present both new and existing examples that illustrate the benefits of hybrid CV-DV processors relative to traditional DV-only hardware in computation as well as quantum simulation of physical models. Examples include algorithms for transferring states between DV and CV systems, performing the quantum Fourier transform, and simulation of lattice gauge theories. Relative to qubit-only hardware, the bosonic degrees of freedom natively available in hybrid architectures can substantially reduce the circuit complexity of simulations for physical models containing bosons. A key technique is the extension of quantum signal processing ideas to CV-DV systems. This work is intended to serve as a timely and comprehensive guide to this relatively unexplored yet promising approach to quantum computation and to provide a road map to guide future development.

Precise state-dependent control of optical potentials is of great importance for various applications utilizing cold neutral atoms. In particular, tune-out wavelengths for the clock state pair in alkaline-earth(-like) atoms provide maximally state-selective trap conditions that hold promise for the realization of novel approaches in quantum computation and simulation. While several ground-state tune-out wavelengths have been determined, similar experimental studies for metastable excited states are challenged by inelastic collisions and Raman losses, so far prohibiting precise measurements of excited-state tune-out conditions. In this work we report on the measurement of a tune-out wavelength for the metastable ${}^3{\mathrm{P}}_0$ clock state in ${}^{\text{174}}\mathrm{Yb}$ at $519.920(9)$ THz. In order to circumvent collisional losses, we isolate individual ${}^3{\mathrm{P}}_0$ atoms in a two-dimensional clock-magic-wavelength lattice at 759 nm. To minimize the limitation imposed by Raman scattering, we further implement resolved sideband cooling on the clock transition, which allows us to reduce the lattice depth and surpass lifetimes of 5 s. The precision of the tune-out measurement is further enhanced by fluorescence imaging in a triple-magic configuration, where we implement molasses cooling on the ${}^3{\mathrm{P}}_1$ intercombination line and identify a magic angle of $38.5(9{)}^{\circ }$ in the clock-magic lattice.

Understanding the computational complexity of quantum states is a central challenge in quantum many-body physics. In qubit systems, fermionic Gaussian states can be efficiently simulated on classical computers and hence can be employed as a natural baseline for evaluating quantum complexity. In this work, we develop a framework for quantifying fermionic magic resources, also referred to as fermionic non-Gaussianity, which constitutes an essential resource for universal quantum computation. We leverage the algebraic structure of the fermionic commutant to define the fermionic antiflatness (FAF)—an efficiently computable and experimentally accessible measure of non-Gaussianity, with a clear physical interpretation in terms of Majorana fermion correlation functions. Studying systems in equilibrium, we show that FAF detects phase transitions, reveals universal features of critical points, and uncovers special solvable points in many-body systems. Extending the analysis to out-of-equilibrium settings, we demonstrate that fermionic magic resources become more abundant in highly excited eigenstates of many-body systems. We further investigate the growth and saturation of FAF under ergodic many-body dynamics, highlighting the roles of conservation laws and locality in constraining the increase of non-Gaussianity during unitary evolution. This work provides a framework for probing quantum many-body complexity from the perspective of fermionic Gaussian states and opens up directions for investigating fermionic magic resources in many-body systems. Our results establish fermionic non-Gaussianity, alongside entanglement and nonstabilizerness, as a resource relevant not only to foundational studies but also to experimental platforms aiming to achieve quantum advantage.

Discovering low-overhead quantum error-correcting codes is of significant interest for fault-tolerant quantum computation. For hardware capable of long-range connectivity, the bivariate bicycle codes offer significant overhead reduction compared to surface codes with similar performance. In this work, we present “Z semidirect Z (ZSZ) codes,” a simple nonabelian generalization of the bivariate bicycle codes based on the group ${\mathbb{Z}}_{\ell }⋊{\mathbb{Z}}_m$. We numerically demonstrate that certain instances of this code family achieve competitive performance with the bivariate bicycle codes under circuit-level depolarizing noise using a belief-propagation and ordered-statistics decoder, with an observed threshold around $0.5\mathrm{\%}$. We also benchmark the performance of this code family under local “self-correcting” decoders, where we observe significant improvements over the bivariate bicycle codes, including evidence of a sustainable threshold around $0.095\mathrm{\%}$, which is higher than the $0.06\mathrm{\%}$ that we estimate for the four-dimensional toric code under the same noise model. These results suggest that ZSZ codes are promising candidates for scalable self-correcting quantum memories. Finally, we describe how ZSZ codes can be realized with neutral atoms trapped in movable tweezer arrays, where a complete round of syndrome extraction can be achieved using simple global motions of the atomic arrays.

We study the interplay between magic and entanglement in quantum many-body systems. We show that nonlocal magic, which is supported by the quantum correlations is lower bounded by the nonflatness of entanglement spectrum and upper bounded by the amount of entanglement in the system. We then argue that a smoothed version of nonlocal magic bounds the hardness of classical simulations for incompressible states. In conformal field theories (CFTs), we conjecture that the nonlocal magic should scale linearly with entanglement entropy but sublinearly when an approximation of the state is allowed. We support the conjectures using both analytical arguments based on unitary distillation and numerical data from an Ising CFT. If the CFT has a holographic dual, then we prove that the nonlocal magic vanishes if and only if there is no gravitational backreaction. Furthermore, we show that nonlocal magic is approximately equal to the rate of change of the minimal surface area in response to the change of cosmic brane tension in the bulk.