Papers

A minimal method to fabricate Al/AlO$_x$/Al Josephson junctions (JJs) using photolithography and argon etching, before metallization and oxidation, is demonstrated. JJs with areas ranging from 1 to 6 $\mu$m$^2$ can be fabricated and, with the appropriate oxidation conditions, the junction resistance can be varied by $\sim$2 orders of magnitude. Transmission electron microscopy reveals the successful fabrication of JJs with few grain boundaries suggesting reduced energy loss from two-level-systems. Superconducting QUantum Interference Devices (SQUIDs) fabricated from this methodology exhibit reduced resistance variation over multiple chips, compared with electron beam lithography, and the devices can sustain repeated thermal cycles to 10 mK with the excellent flux response remaining unchanged. The quantum applications of this technology are demonstrated by embedding a SQUID resonator into a 3D cavity and parametrically amplifying low photon numbers with gains of $\sim$40 dB. This work establishes the simplest approach to fabricating JJs to date, and it could prove pivotal to the widespread utilization of superconducting circuit-based quantum technologies.

Network tomography refers to the use of inference techniques for inferring internal network states from end-to-end probes. Quantum probes, implemented by sending blocks of $n$ coherent-state pulses augmented with continuous-variable (CV) squeezing ($n=1$) or weak temporal-mode entanglement ($n>1$) over a lossy channel to a receiver with homodyne detection capabilities, are known to carry information about the channel transmissivity. Assuming a subset of nodes in an optical network is capable of sending and receiving such probes through intermediate nodes with all-optical switching capabilities, we leverage these quantum probes to estimate link transmissivities. To determine how to route the probes in a network, we propose a probe construction algorithm that guarantees link identifiability, while maximizing the number of information orthogonal sets of transmissivities. A set of probes induces a Fisher information matrix (FIM). We then derive two metrics, the determinant of the FIM and the trace of its inverse, to evaluate the performance of the probes. In particular, our results can be used to characterize the quantum improvement in estimating link transmissivities in a general optical network.

Inverse design has transformed vast physical parameter spaces into a substrate for emergent functionality, raising the tantalizing prospect of relocating intelligence from the digital domain into the physical world itself. Nowhere is this prospect more consequential than in sensing, where the analog-to-digital interface imposes a fundamental bottleneck: information not captured by the hardware is irrevocably lost to any downstream algorithm. Existing approaches improve information capture through either sensor hardware optimization or adaptive measurement strategies operating on fixed hardware, but rarely both in concert. A principled migration of intelligence from digital to physical demands their joint optimization: the sensing geometry must be co-designed with a policy that determines what to measure next. We formulate this co-design as joint dynamic programming (joint-DP), a unified optimization over sensor geometry and a Bellman-optimal adaptive measurement policy. The outer hardware gradient is obtained through differentiable dynamic programming with a sharp Bellman maximum. A hierarchy of relaxations extends the framework from small discrete POMDPs to freeform photonic topologies with more than $10^5$ design pixels.

Among the many solid-state emitters being explored for scalable quantum technologies, the silicon T centre is a leading candidate offering long-lived spin qubits, a telecommunications-band spin-photon interface, and integration with on-chip photonic circuits. However, nanophotonic integration broadens both the inhomogeneous spectral distribution and individual emitter linewidths. Here, we integrate single T centres into silicon nanophotonic cavities with p-i-n diodes for local electronic control. These devices enable Stark tuning up to 30 GHz, sufficient to bring 55(2)% of on-chip T centres into mutual resonance, and demonstrate tunable lifetime reduction across the cavity resonance. A model of the joint excitation probability shows an orders-of-magnitude increase in entanglement rate by tuning distinct emitters into mutual resonance. Luminescence modulation at high reverse biases reveals a transition to a dark charge state. Finally, bias-induced modulation of the optical transition splitting uncovers a potential mechanism for electrically driven excited-state spin mixing via spin-orbit coupling. Localized and individual spectral tuning increases the yield of performant silicon spin-photon interfaces and the number of devices per chip available for large-scale entanglement and quantum information technologies.

Demonstrating quantum advantage for combinatorial optimization requires more than standalone algorithmic results; it calls for end-to-end case studies that integrate problem modelling, quantum execution, and classical refinement into practical workflows. This paper presents a sandbox platform for experimenting with hybrid quantum-classical workflows in graph optimization, enabling the systematic study of end-to-end optimization pipelines. Using our platform, we investigate three classically intractable and mutually reducible graph problems – Minimum Vertex Cover, Maximum Independent Set, and Maximum Clique – by transforming them into an unconstrained problem and solving the resulting instances with QAOA on IBM platforms. Our workflow combines classical pre-processing to reduce instance size, quantum optimization on the reduced problem, and classical postprocessing to map quantum outputs to high-quality feasible solutions, thereby avoiding direct constraint encoding in the quantum circuit. We evaluate the approach on synthetic graphs, benchmark instances, and real-world networks, and report hardware experiments on IBM Quantum System One at PINQ2 in Bromont, Quebec, powered by IBM's 156-qubit Heron r2 processor on graphs up to 128 vertices, with circuits involving up to 128 qubits and 13,555 two-qubit gates. The results illustrate how sandbox-style end-to-end experimentation can expose bottlenecks, clarify the role of classical-quantum workload partitioning, and provide domain experts and practitioners with a practical guide for interpreting quantum optimization outputs and assessing quantum utility on the road to quantum advantage in combinatorial optimization.

In an extension of the Unconventional Noiseless Intermediate Quantum Emulator, this work introduces a classical emulation of the quantum Harrow-Hassidim-Lloyd algorithm for sampling from the solution space of linear systems. The emulated HHL algorithm scales exponentially with the number of qubits required to represent the linear system, which is an advantage over the state vector simulation of the HHL algorithm, which scales exponentially as a function of both the size of the linear system and the magnitude of its largest (scaled) eigenvalue. We benchmark our emulator by comparing it with the Intel Quantum Simulator and demonstrate a runtime advantage for small linear systems.

We present an alternative scheme to achieve nonreciprocal unconventional magnon blockade (NUMB) in a hybrid system formed by two microwave cavities and one yttrium iron garnet (YIG) sphere, where the pump and signal cavities interact nonlinearly with each other and the signal cavity is coupled to the YIG sphere. It is found that the nonlinear coupling occurs between the pump cavity and magnon modes due to the dispersive interactions among three bosonic modes. Meanwhile, the Kerr nonlinearity is present in the pump cavity. Based on these nonlinear effects, a nonreciprocal magnon blockade could be achieved with the help of weak parametric driving of the pump cavity. The present work provides an alternative method to prepare single magnon resource, which may be helpful for quantum information processing.

We propose a parallel protocol for implementing distributed nonlocal quantum gates between spatially separated stationary qubits encoded in dual-species quantum emitters (i.e., color-center and superconducting qubits). By utilizing entangled photon pairs with distinct frequencies as a quantum data bus, our approach connects spatially separated devices without requiring quantum frequency conversion or preshared entanglement, while maintaining an always-ready and resource-efficient property for distributed quantum computing and networks. Furthermore, we demonstrate the feasibility of implementing parallel distributed nonlocal quantum gates on multiple pairs of spatially separated qubits using a single high-dimensional entangled photon pair, which directly benefits from the enhanced quantum capacity provided by optical qudit encoding. Our protocol establishes a scalable and practically implementable framework for distributed quantum networks, potentially enabling the development of future large-scale quantum computing architectures.

We solve the time-dependent Schrödinger equation by learning the score function, the gradient of the log-probability density, on Bohmian trajectories. In Bohm's formulation of quantum mechanics, particles follow deterministic paths under the classical potential supplemented by a quantum potential depending on the score function of the evolving density. These non-crossing Bohmian trajectories form a continuous normalizing flow governed by the score. We parametrize the score with a neural network and minimize a self-consistent Fisher divergence between the network and the score of the resulting density. We prove that the zero-loss minimizer of this self-consistent objective recovers Schrödinger dynamics for nodeless wave functions, a condition naturally met in quantum vibrations of atoms. We demonstrate the approach on wavepacket splitting in a double-well potential and anharmonic vibrations of a Morse chain. By recasting real-time quantum dynamics as a self-consistent score-driven normalizing flow, this framework opens the time-dependent Schrödinger equation to the rapidly advancing toolkit of modern generative modeling.

To grow the quantum information science and technology workforce, opportunities for students to gain experiential learning and build a sense of belonging in the broader community are essential. The Undergraduate School on Experimental Quantum Information Processing (USEQIP) is a two-week summer school for undergraduate students that has been held since 2009 with the goal of introducing undergraduate students from around the world to the tools of quantum information research, paired with a summer internship program. Here we report on the structure, impact, and outlook of the program, including hands-on laboratory activities refined over many iterations of the program. We highlight the career trajectories of program alumni, many of whom have made significant contributions to the quantum field.

Electronic band structures and the Fermi energy provide essential information for understanding the electronic properties of solids. In semiconductors, the Fermi energy is determined by the donor and acceptor concentrations. For diamond, the relationship between the Fermi energy and the donor-acceptor concentrations is highly nonlinear; therefore, experimental determination of the Fermi energy is important. Here, we report a method to determine the Fermi energy of diamond based on photoluminescence (PL) measurements. The density-functional-theory (DFT) study by Deák et al.~\cite{deak2014formation} showed the relationship between the Fermi energy and the formation energies of nitrogen-vacancy centers in the negatively charged (NV$^-$) and neutrally charged (NV$^0$) charge states. In the present method, we measure the relative populations of the NV$^-$ and NV$^0$ centers from PL spectral analysis and determine the Fermi energy using these populations and the DFT result. Moreover, we show the application of the method by extracting the Fermi energies of several diamond samples and discuss the spin coherence times of their NV centers and their stability against the charge state conversion. The PL-based method is advantageous for determining the Fermi energy with high spatial and fast time resolutions, even in extreme environments, and can be extended to determine various wide bandgap semiconductors.

Near-term FTQC system designs are constrained by limited error budgets and largely sequential execution of non-Clifford gates. As a result, reducing the number of the most-error prone instructions becomes critical for successful program execution. In this work, we study the extractor architecture, a recently proposed FTQC design that enables universal quantum computation on spatially-efficient QEC codes such as the BB code family. In these architectures, over $90\%$ of the total program error arises from the synthillation process, which involves $\lvert T\rangle$-state preparation and injection to implement non-Clifford gates. We observe that standard Rz synthillation requires multiple sequential $\lvert T\rangle$-state injections, each incurring an inter-module measurements, the most expensive instruction in the architecture, which cumulatively dominate the overall error budget. To address this bottleneck, we propose INJEQT, a $2$-factory design that uses an auxiliary code capable of synthesizing $Rz(\theta)$ states with lower error rates. These states are then injected into the extractor modules using only a constant number of inter-module measurements. This approach reduces overall error rates by up to $22\times$. We further reduce the time overhead by a pre-fetching strategy that prepares the Rz states and their correction states in parallel. This approach improves the wall-clock time by up to $13\times$ and reduces the space-time cost by up to $7.2\times$, for an optimal choice of the number of INJEQT factories for each metric. We evaluate INJEQT for multiple state preparation techniques such as distillation, cultivation and STAR, and model the execution times for both lattice surgery-based and transversal CNOT based injections. Our results demonstrate that INJEQT is robust across factory choices and device technologies, enabling more efficient architectural designs for FTQC.

The performance of the Quantum Approximate Optimization Algorithm (QAOA) on noisy intermediate-scale quantum (NISQ) devices is strongly limited by sparse qubit connectivity. When interactions required by QAOA Hamiltonians are not aligned to the hardware topology, transpilation introduces SWAP gates, increasing circuit depth and noise. We propose a SWAP-free QAOA framework based on modifying the cost Hamiltonian so that it can be implemented natively on the hardware. We formulate this as a mixed-integer semidefinite program (MISDP) that selects a hardware-compatible approximation of the original cost matrix and optimizes the allocation of logical variables to physical qubits. We prove that the associated decision problem is NP-complete and derive theoretical guarantees relating the MISDP objective to the loss in the original optimization problem through the Lovász number of the hardware graph. Since solving MISDPs is practical only for small instances, we introduce heuristics based on spectral properties of the problem matrix and hardware graph. Our experiments on a cardinality-constrained quadratic optimization model for index tracking show competitive performance against a baseline representing ideal QAOA under SWAP-induced noise. These results indicate that, on sparse NISQ architectures, a hardware-aware approximation of the objective may be more effective than an exact but heavily transpiled Hamiltonian implementation.

We analyze the dynamics of a quantum particle in a one-dimensional bistable potential within the framework of Bohm's quantum mechanics. We give arguments that evidence the fallacy of certain claims found in the literature dealing with the impossibility of chaotic behavior of Bohmian trajectories in one-dimensional systems. We find that an appropriate choice for the initial position and wave packet causes the particle to undergo periodic, quasiperiodic, or chaotic motion. The transitions between these regimes occur in a continuos fashion.

To implement quantum algorithms on a quantum computer, we must overcome the twin problems of fault-tolerance – how can we realize a relatively noiseless computation by cleverly combining noisy components? – and compilation – how can we realize an arbitrary quantum algorithm given the basic operations available on the quantum device at hand? We show how treating the former problem via error-correcting codes enables greater flexibility in resolving the latter. Specifically, we explicitly leverage the fact that error-correcting codes introduce redundancy which renders physically distinct operators logically indistinguishable. In terms of computation, it suffices to implement any operator logically equivalent to some target, yet from a compilation perspective, certain choices may be preferable to others. Our novel contribution is making this intuition precise in the general setting of the special unitary group. In particular, we describe how to reduce the problem of making a compilation-ideal choice to a least squares problem and provide a closed form solution thereof. Using our framework, it is possible to circumvent inserting costly swaps to adhere to hardware connectivity; instead, we could realize the logical target through a distinct physical Hamiltonian that is natively accessible. We elucidate our approach using the $[[4,2,2]]$ code. We discuss connections to compressed sensing that may pave the way to efficient compilation leveraging physical degrees of freedom.

Quantum entanglement between gamma-ray photons emitted following electron-positron annihilation is expected to be maximal and may be characterized via non-classical polarization correlations. However, this is difficult to verify experimentally because there are no established schemes that approach ideal projective-polarization measurements for high-energy photons. Hence, polarization entanglement between MeV-scale annihilation photons has not yet been conclusively demonstrated. We develop here a framework that models polarization measurements of high-energy photons via Compton polarimetry, employing the formalism of positive operator-valued measures (POVMs). We extend the POVM description to sequences of repeated interactions and show that the measurement converges toward an ideal projective measurement of linear polarization as the number of interactions increases. We demonstrate that this progressive improvement in measurement sharpness can enable the experimental violation of CHSH inequalities.

Quantum low-density parity-check (QLDPC) codes with good parameters are promising candidates for low-overhead fault-tolerant quantum computing, but their non-local stabilizers require long-range connectivity and frequent qubit movement, introducing practical challenges. Prior work has studied the networked implementation of topological codes, where each node only holds one or a few qubits of the entire code, and demonstrated competitive performance under practical constraints such as the quality of network-provided entanglement. However, since these codes are already geometrically local, such a networked setting might not be essential. In this work, we propose and study the networked implementation of better QLDPC codes, specifically bivariate bicycle codes due to their similarity to surface codes and the controlled amount of long-range connections in their stabilizers. We begin by recreating networked surface codes in Stim, with one code qubit per node, and provide additional insights into their circuit-level noise performance. We then extend this approach to bipartitions of bivariate bicycle codes, using balanced min-cut partitioning on their combined X-Z Tanner graph to identify optimal qubit splits. For stabilizers spanning nodes, we implement teleported CNOTs and vary the Bell pair fidelity enabling these gates. Through circuit-level noise simulations with BP-OSD decoding, we provide the first insights into networked realizations of these codes and compare their performance with monolithic implementations. We conclude by outlining advantages, limitations, and future directions.

Classical optical frameworks such as the discrete dipole approximation (DDA) assume that the linear spectrum of coupled quantum emitters can be computed solely from the linear susceptibilities of individual constituents. However, recent polariton studies show that cavity linear response can encode nonlinear optical susceptibilities. Here, we demonstrate that this phenomenon is more general: emitter-emitter interactions allow nonlinearities of individual emitters to emerge in the linear response of arrays, without cavities or permutational symmetry. To illustrate this phenomenon, we show linear spectra for coupled heterodimers and linear chains, and demonstrate that Raman features of individual monomers show up as vibrational sidebands of collective resonances. Moreover, tuning Raman-type anharmonicities enables systematic control of spectral features, establishing a genuine quantum optical effect in molecular aggregates and quantum emitter arrays, which goes beyond mean-field descriptions in light-matter interactions.

This paper describes the design and implementation of a two-day quantum hackathon for underrepresented high school students in Nova Scotia, Canada. The first day of the hackathon is spent introducing students to quantum computing through hands-on activities, whereas the second day teaches students to apply this knowledge through guided challenges. Both days are informed by the theory of mastery learning and specification grading, with the full curriculum being crafted within the Integrated Course Design framework. This requires identifying situational factors unique to our target demographics, from which we develop learning outcomes, and then work backwards to a full curriculum with educative assessments. A novel aspect of our hackathon is that all circuit simulations are performed within Quirk: a decision based on best practices in computer science education. Based on feedback from students, we conclude that our hackathon successfully introduced students to the basics of quantum computing, and was able to reach most of our target demographics.

Recent theoretical studies propose that Hawking radiation may not emerge strictly at the event horizon but rather from the spatially extended region surrounding a black hole, commonly referred to as the quantum atmosphere. In this work, we explore how this concept influences nonlocal quantum correlations in a bosonic bipartite system located at certain distance from a Schwarzschild black hole. By employing the measurement-induced nonlocality (MIN), as a quantifier of quantum correlations, we analyze the response of bosonic fields to the thermal and geometric characteristics associated with the Hartle-Hawking vacuum. In this manner, we extend previous studies that primarily focused on the fermionic systems. Our results reveal that, when quantum atmosphere is taken into account, the behavior of MIN departs from its conventional near-horizon profile. In particular, bosonic nonlocal correlations are found to exhibit a pronounced degradation at a finite radial distance from the event horizon and to ultimately vanish as scaled distance increases further. To some extent this behavior contrasts with the previously considered fermionic case, indicating that bosonic fields provide potentially stronger response to the quantum atmosphere.

Sparse quantum state preparation is a common subroutine in quantum algorithms, where classical data with few nonzero entries must be loaded into a quantum state. In this work, we consider the Grover-Rudolph algorithm, which has recently been shown to efficiently prepare sparse states, and we propose two improvements. First, we extend an existing gate-merging procedure by allowing rotations to merge with virtual zero-angle gates on unreachable branches of the preparation tree, reducing the number of CNOTs and control qubits. Second, we introduce an approximate variant in which rotations with similar but not identical angles are merged at the cost of a small, controllable error in the prepared state. We derive a classically computable estimate of the resulting overlap with the target state, which is used to guide the merging decisions.

The maximum flow problem asks to find the largest possible flow from a source to a sink in a capacitated network. It arises frequently in scheduling, project selection, and as a core subroutine in broader optimisation tasks. Classically, it can be efficiently solved using Dinic's algorithm, which repeatedly performs breadth-first search (BFS) and blocking flow computations on the graph. As a potential candidate for quantum speedups, these BFS subroutines can be naturally replaced with quantum BFS (qBFS), an instantiation of Grover's search algorithm. In this paper, we evaluate the expected performance of qBFS on standard classical datasets. These instances are too large to be solved directly on current quantum hardware, so we adopt a hybrid benchmarking approach: (i) we run a classical implementation of Dinic's algorithm and isolate the runtime of its BFS subroutines; (ii) we analytically estimate the number of two-qubit gates required to implement qBFS, where we use the classically logged data. Our results indicate that, on every tested instance, matching classical BFS would require two-qubit gate operation times below the current physical record by one to two orders of magnitude for most instances, and by a factor of about 1.3 for the smallest, most favourable ones. Since this comparison uses physical rather than error-corrected gate times, the actual gap is larger still.

We introduce HAML (Hamiltonian Adaptation via Meta-Learning), a framework for fast online adaptation of effective Hamiltonian models of superconducting quantum processors. HAML proceeds in two phases. A supervised training phase uses an ensemble of simulated devices to learn an offline map from control inputs and device parameters to effective Hamiltonian coefficients. An online adaptation phase then uses a small number of hardware-accessible measurements to identify the unknown parameters of a new device. By training directly against effective two-qubit coefficients extracted from full multi-mode simulations, HAML implicitly learns the reduction from full multi-mode Hamiltonians to effective qubit descriptions without invoking perturbation theory. We further show that a variance-maximizing greedy selection of measurement configurations boosts online adaptation efficiency. We demonstrate HAML on a transmon-coupler-transmon system, recovering effective two-qubit coefficients across a wide range of operating regimes, including parameter regions where Schrieffer-Wolff perturbation theory (SWPT) breaks down. This establishes a scalable, sample-efficient approach to Hamiltonian reduction and characterization for near-term quantum processors, with direct implications for calibration, control, and error mitigation.

Quantum simulation offers a promising framework for quantum field theory calculations. Obtaining reliable results, however, requires careful characterization of systematic uncertainties. One important source is the boson truncation error, which arises from representing infinite-dimensional local Hilbert spaces with finite-dimensional ones. Previous studies have examined this problem from several perspectives. In particular, Jordan, Lee, and Preskill (arXiv:1111.3633) derived an energy-based bound applicable to generic low-energy states across a broad class of field theories. However, this approach often yields overly conservative bounds, especially at large volumes. In this work, we introduce a new methodology that significantly tightens the energy-based boson truncation bound through two complementary advances: an improved analytic derivation and a Monte Carlo-based numerical procedure. We demonstrate the method in (1+1)-dimensional scalar field theory and (2+1)-dimensional U(1) gauge theory in the dual formalism. Our approach substantially mitigates the volume dependence of the required truncation cutoff, achieving reductions nearly proportional to the volume in some cases and to the square root of the volume in others.

Dual-species Rydberg atom arrays extend single-species platforms by introducing competing interaction scales and enhanced quantum fluctuations, enabling phenomena beyond homogeneous settings. In this work, we study the ground-state phase diagram of a one-dimensional dual-species Rydberg atom ladder using large-scale density-matrix renormalization group calculations. We identify disordered phases, multiple ordered phases with $\mathbb{Z}_2$, $\mathbb{Z}_3$, and $\mathbb{Z}_4$ symmetry, as well as floating phases characterized by incommensurate wave vectors and algebraically decaying correlations. Importantly, we observe a smooth crossover between distinct $\mathbb{Z}_2$-ordered regimes, reflecting a reorganization of low-energy degrees of freedom rather than a true phase transition, which is absent in single-species Rydberg arrays. We further uncover a multi-critical point at the boundary between the $\mathbb{Z}_2 \otimes \mathbb{Z}_2$ and $\mathbb{Z}_3 \otimes \mathbb{Z}_3$ ordered phases, where Ising, chiral, and first-order transition lines intersect. Our results demonstrate that dual-species Rydberg atom arrays provide a unique platform for realizing crossover physics and multi-critical behavior inaccessible in single-species architectures.

Quantum neural networks generalize classical artificial neural networks into the quantum domain. They are formulated as parameterized quantum circuits which are optimized by measuring and minimizing a suitably chosen loss function. The core challenge in understanding, implementing and ultimately using quantum neural networks is that they represent many-body systems with an exponentially large Hilbert space, in combination with a large parameter search space. Moreover, noise – which is inherent to any quantum measurement – sets practical limits for the estimation of training loss. Here, we study physics-informed large-scale quantum neural networks that are trained through a finite number of noisy loss function measurements. We show that this architecture permits the construction of nontrivial decision boundaries that enable the classification of quantum states through measuring an order parameter. Our approach can directly process quantum data that is output from quantum simulators and computers and is well suited for implementation on current hardware. Moreover, owed to a close link between the neural network dynamics and the evolution of Markovian open many-body quantum systems, one may expect a certain robustness to noise, which is ubiquitous in the current NISQ era.

This article discusses incorrect statements appearing in textbooks on quantum field theory (QFT); some of these mistakes also appear in the research literature. The focus is not on errors made by an individual author, but on conceptual muddledness that is widespread in introductory textbooks. We start from a bare-bones summary of QFT, meant to establish the notation. We then turn to our six paradigmatic themes, in each case quoting a specific example of the textbook mistake, a summary of material that is known to experts but is frequently mishandled in introductory works, pointers to authoritative references where the relevant concept is handled properly, as well as a concise correction that rectifies any issues. The goal of this work is to warn readers of the existence of several pitfalls and thereby stop these errors from further propagating in the literature on QFT.

Quantum random number generation (QRNG) relies on the inherent unpredictability of quantum mechanical phenomena to efficiently generate high-quality random numbers that can be used in a wide range of cryptography and simulation applications. Here we report the experimental demonstration of QRNG from the arrival times of photons emitted by nitrogen vacancy (NV) centres in fluorescent nanodiamonds. The generation rates achieved range from 0.173 Mbits/s for a region with a single NV centre to 4.77 Mbits/s for a region with just under 50 NV centres, where the latter demonstrates an order of magnitude improvement compared to the highest generation rate previously achieved with NV centres. For all the regions investigated, the generated bits passed the ENT and NIST Statistical Test Suites without post-processing. The results are consistent with our theoretical analysis, where we show that the min-entropy is very close to the ideal value of one per bit for all the regions investigated. This work opens up new possibilities for robust QRNG in highly compact on-chip settings.

The development and spread of entanglement in complex quantum systems is central to exploring many-body phenomena out of equilibrium. Measuring entanglement dynamics can shed light on information scrambling and thermalisation, namely on transitions from many-body quantum chaos to localisation in disordered, interacting systems. In quantum computing systems, entanglement entropy and other nonlinear functions of the density matrix have been recently measured, in particular by using the randomised measurement toolbox. However, it is difficult to implement the required arbitrary unitary rotations on specific subsystems without universal local control. Here we devise and demonstrate the measurement of entanglement entropy in a programmable analogue quantum simulator using a randomised measurement protocol that leverages local energy tuning together with a global field to bypass the need for local gate control. We implement this on a commercially available neutral-atom quantum simulator, QuEra's Aquila, and use it to show how programmable disorder in the local Hamiltonian parameters leads to a transition from chaotic to localised entanglement dynamics. Given current decoherence times, we clearly resolve disorder-specific, time-dependent entanglement spreading in small systems. Our work extends the utility of programmable analogue quantum simulators, and opens further opportunities for wider randomised measurement toolboxes in a range of other analogue systems.

We study a chain of periodically driven Rydberg atoms and identify a class of drive protocols for which the system exhibits emergent prethermal Bethe integrability at special drive frequencies. We provide a perturbative analytic expression of its Floquet Hamiltonian in the large drive amplitude regime. We demonstrate integrability of the leading term of this Floquet Hamiltonian at special drive frequencies, which we identify, by mapping it to the Hamiltonian of the paradigmatic spin-$1/2$ ${\rm XXZ}$ chain. We support our analytical results by exact diagonalization studies on finite chains. Our numerical results on level statistics, half-chain entanglement entropy, and longitudinal magnetization of the driven chain brings out its emergent integrable nature at the special drive frequencies which persists up to a large prethermal timescale.