Papers

Free-fall atom interferometers offer a powerful platform for accurate, absolute gravitational sensing. Szigeti et al. [Phys. Rev. Lett. 125, 100402 (2020)] recently proposed a quantum-enhanced scheme that uses a spin-squeezed Bose-Einstein condensate as an input state to improve the phase sensitivity of the interferometer. The spin squeezing, generated via one-axis twisting interactions, was limited by condensate expansion. Here we present an improved state preparation in which a sudden trapping potential – a delta kick – is initially applied to focus the condensate. The resulting increase in density enhances the one-axis-twisting interactions and produces greater spin squeezing. Using multimode truncated-Wigner simulations, we quantify the performance of the interferometer and find that, for an optimal kick strength, the phase sensitivity surpasses the standard quantum limit by a factor of $\sim 20$. This represents a fourfold improvement over the original scheme without the delta kick and is well captured by a two-mode approximation.

Deep neural networks remain highly vulnerable to adversarial perturbations, limiting their reliability in security- and safety-critical applications. To address this challenge, we introduce QShield, a modular hybrid quantum-classical neural network (HQCNN) architecture designed to enhance the adversarial robustness of classical deep learning models. QShield integrates a conventional convolutional neural network (CNN) backbone for feature extraction with a quantum processing module that encodes the extracted features into quantum states, applies structured entanglement operations under realistic noise models, and outputs a hybrid prediction through a dynamically weighted fusion mechanism implemented via a lightweight multilayer perceptron (MLP). We systematically evaluate both classical and hybrid quantum-classical models on the MNIST, OrganAMNIST, and CIFAR-10 datasets, using a comprehensive set of robustness, efficiency, and computational performance metrics. Our results demonstrate that classical models are highly vulnerable to adversarial attacks, whereas the proposed hybrid models with entanglement patterns maintain high predictive accuracy while substantially reducing attack success rates across a wide range of adversarial attacks. Furthermore, the proposed hybrid architecture significantly increased the computational cost required to generate adversarial examples, thereby introducing an additional layer of defense. These findings indicate that the proposed modular hybrid architecture achieves a practical balance between predictive accuracy and adversarial robustness, positioning it as a promising approach for secure and reliable machine learning in sensitive and safety-critical applications.

Uncertainty quantification (UQ) is essential for deploying machine learning models in safety-critical physical systems, yet classical Bayesian approaches incur substantial computational overhead. We establish a formal connection between Born-rule measurement statistics from variational quantum circuits (VQCs) and Bayesian posterior uncertainty, proving that repeated quantum measurements naturally produce calibrated prediction intervals without requiring explicit Bayesian neural network (BNN) machinery. We demonstrate this framework on physics-constrained VQCs trained on PDE residuals. Systematic experiments comparing quantum shot-based UQ against MC Dropout and Deep Ensemble baselines show that quantum UQ achieves coverage probabilities within 1-3% of target confidence levels at N >= 5000 shots, while MC Dropout systematically over-covers by 4-5%. Physics-constrained circuits reduce the expected calibration error (ECE) by 34-40% compared to unconstrained counterparts, with interval widths 14-30% narrower at equivalent coverage. Information-theoretic analysis reveals that quantum circuits extract ~15% more bits of UQ information per evaluation than MC Dropout and ~42% more than Deep Ensembles (M = 10), owing to the exponential Hilbert space accessible through Born-rule sampling. These results establish quantum measurement statistics as a principled, computationally efficient framework for uncertainty quantification in physics-informed learning.

Two-photon interference effects arise because photons are indistinguishable particles. In the wellknown Hong-Ou-Mandel (HOM) effect, the transmission of two photons at a beam splitter interferes destructively with the reflection of both photons, requiring both photons to "bunch up" by leaving the beam splitter on the same side. Here, we show that the interference between locally propagating photons and photons exchanged by a mode swap can be implemented by post-selecting spatially separated photon outputs of a four-path interferometer. Even though the photons detected at spatially separated locations must have travelled along paths that never met up at the same beam splitter, the Hong-Ou-Mandel effect can be observed in correlations between the output ports that originate from the association of detection events with non-local output modes defined by the two single photon inputs. Local phase shifts can be used to map out non-classical correlations between the photons detected at different output locations, clarifying the role of linear optics in generating entanglement between spatially separated photons. Our work thus establishes a fundamental relation between multiphoton interference and entanglement, opening the door to new possibilities in optical quantum technologies.

Differential privacy is a mathematical notion of data privacy that has fast become the de facto standard in privacy-preserving data analysis. Recently a lot of work has focused on differential privacy in the quantum setting. Continuing on this line of study, we investigate how to answer counting queries on a quantum encoded dataset with differential privacy. An example of a counting query is “How many people in the dataset are over the age of 25 and with a university education?” Counting queries form the most basic but nonetheless rich set of statistics extractable from a dataset. We show that answering these queries on a quantum encoded dataset reduces to measuring the amplitude of one of two orthogonal states. We then analyze the differential privacy properties of two algorithms from literature to measure amplitude: one which performs repeated measurements in the computational basis, and the other which utilizes the classic amplitude estimation algorithm. For the first technique, we prove privacy results for the case of counting queries that improve on previously known results on general queries, and show that the mechanism in fact amplifies privacy due to inherent randomness. For the second method, we derive a tight bound on maximum possible change in the amplitude if we add or remove a single item in the dataset, a quantity called global sensitivity which is central in making an algorithm differentially private. We then show a differentially private version of the amplitude estimation algorithm for counting queries. We also discuss how these methods can be outsourced to a quantum server to blindly compute counting queries with differential privacy.

Hyperentangled states are fascinating resources in quantum information processing as they can significantly increase the channel capacity and enhance noise resistance. We explore a hyperfusion mechanism to fuse one n photon hyper-W state and one m-photon hyper-W state into a large-scale (n+m-2)-photon hyper-W state. Another mechanism to fuse one n-photon hyper-W state, one m-photon hyper-$W$ state, and one $t$-photon hyper-W state into an (n+m+t-3)-photon hyper-W state is also proposed. These two hyperfusion mechanisms are constructed employing only polarizing beam splitters, balanced beam splitters, half-wave plates, single-photon detectors, and cross-Kerr nonlinearities. Conditional quantum gates, path couplers, and ancillary photons are not required in our constructions. Moreover, our fused $W$ states are hyperentangled in the polarization and spatial degrees of freedom of single-photon systems. The presence of only one garbage output state demonstrates that high efficiency can be achieved in our schemes.

The computational demands of deep learning motivate the investigation of alternative approaches to computation. One alternative is physical neural networks~(PNNs), in which learning and inference are performed directly via physical processes. Stochastic PNNs arise when the underlying neurons are realized by the dynamics of a stochastic activation switch. Here we propose novel electronic and photonic stochastic neurons. The electronic realization is implemented by single-electron tunneling through a quantum dot. The photonic realization is implemented via a single-photon source driving one of two modes coupled via a controllable beam-splitter-like interaction. In the electronic case, the charge state of the quantum dot forms the basis for the stochastic neuron, whereas in the photonic case the occupation of the undriven mode serves as the basis for the stochastic neuron. Training of stochastic PNNs is performed with models of stochastic neurons, as well as with coherently-driven, single-photon detector stochastic neurons previously introduced. Several training strategies for MNIST handwritten digit classification have been investigated using single-hidden-layer stochastic PNNs, including varying the number of trials in each layer to control forward pass stochasticity and employing either true probability or empirical outputs in the backward pass to evaluate their influence on gradient estimation. We show that when empirical outputs are used in the backward pass, the network achieves more than 97% test accuracy with few trials per layer. Despite the simplicity of the model architecture, high test accuracy is maintained in the presence of a high degree of noise and model uncertainty. The results demonstrate the potential of embracing stochastic PNNs for deep learning.

This work develops a symplectic framework for quantum computing to be applied to classical Hamiltonian systems, exploiting the intrinsic geometric compatibility between unitary quantum evolution and symplectic phase-space dynamics in a two-fold way. The first part is devoted in establishing an exact correspondence between quantum evolution and classical Hamiltonian flow on a Kahler manifold. This correspondence enables a geometric quantization scheme that identifies a family of classical Hamiltonian systems admitting exponentially compressed quantum representations-appropriate for quantum simulation. In the second part we demonstrate that Liouville-integrable Hamiltonian dynamics induce finite-dimensional unitary evolution through action-angle variables and Koopman-von Neumann encoding. This allows efficient quantum representation and parallel evolution of large phase-space ensembles, where entangled encodings provide exponential compression in ensemble size and enable quantum speed-ups in observable estimation via amplitude estimation techniques. For non-integrable systems, Lie canonical perturbation theory is incorporated to construct near-symplectic transformations that map dynamics to approximately integrable forms, preserving unitary evolution up to a controlled error. We derive the resulting quantum computational complexity of the proposed quantum-symplectic scheme, revealing both an exponential compression in memory requirements and a potential polynomial speed-up with respect to the system size. Finally, the transport evolution equation governing the quantum phase-space observables is obtained.

We derive a bound on the efficiency of thermal engines that can be sharper than Carnot's limit. It is a function of statistical correlations between the engine internal state and Hamiltonian, can be saturated even in finite-time cycles, and applies to both classical and quantum engines. Specifically, the bound establishes the exact maximal efficiency of engines operating with multiple baths, tightening the upper limit set by Carnot's theorem. Then, we show that an engine made of a quantum dot coupled with fermionic baths can achieve the bound, even when operating beyond the quasistatic regime. The result provides a design principle for realistic energy harvesting machines.

We present an analytical study of the relativistic quantum Otto cycle driven by a time-dependent harmonic oscillator. By imposing an asymmetry on the two adiabatic processes of this cycle, we obtain distinct scenarios of sudden compression and sudden expansion, and analyze how asymmetry affects the performance of the relativistic quantum Otto engine. By leveraging the Omega function as a unified performance metric, we analytically characterize the efficiency in both scenarios. Our findings demonstrate that the efficiency approaches unity in the sudden compression case, while it is restricted to one-half for the sudden expansion case. Furthermore, we investigate the impact of increasing oscillator velocity on the extracted work and identify parameter regimes where either sudden compression or sudden expansion dominates. Additionally, we examine the optimal operating point using parametric efficiency-work plots, whose loop-shaped structure shows that increasing oscillator velocity enhances both work output and efficiency. Finally, through a detailed phase diagram analysis of the Otto cycle, we observe that the operational region corresponding to the engine mode expands with increasing oscillator velocity, while the refrigeration regime shrinks correspondingly.

Classical reaction dynamics suggests transport through an index-1 saddle is organized not just by flux, but by local symplectic width scales of bounded proxy neighborhoods near the bottleneck. We investigate if a related geometric effect appears in the quantum regime for highly squeezed Gaussian wavepackets. Building on de Gosson's symplectic approach, we analyze how transverse bath-mode squeezing modifies transmission across a quantum normal-form (QNF) bottleneck. To avoid the instability of propagating states with extreme phase-space eccentricity, we use the Weyl-symbol formulation of the QNF. For the quadratic saddle-center model, we derive an exact baseline transmission formula by convolving the bath's squeezed-state number distribution with the 1D Kemble transmission factor. For anharmonic truncated QNF models, we enforce strict algebraic energy conservation and evaluate exact Gaussian expectation-value diagnostics of the Weyl symbol via Wick-Isserlis moment formulas. Results reveal a pronounced squeeze-induced suppression of transmission. As the squeezed state's bath-plane geometric scale grows relative to the classical candidate width, the expected bath action grows rapidly. Consequently, effective reactive energy is strongly depleted, driving transmission into a severely suppressed regime. We interpret this as evidence of a quantum geometric suppression mechanism consistent with the classical candidate symplectic-width picture. While not yet a rigorous quantum non-squeezing theorem, this work provides a concrete framework linking squeezed-state covariance geometry, normal-form action scales, and mode-specific quantum reactivity near an index-1 saddle.

H-EFT-VA established a physics-informed solution to the Barren Plateau (BP) problem via a hierarchical EFT UV-cutoff, guaranteeing gradient variance in Omega(1/poly(N)). However, localization restricts the ansatz to a polynomial subspace, creating a reference-state gap for states distant from |0>^N. We introduce Adaptive H-EFT-VA (A-H-EFT) to navigate the trainability-expressibility tradeoff by expanding the reachable Hilbert space along a safe trajectory. Gradient variance is maintained in Omega(1/poly(N)) if sigma(t) <= 0.5/sqrt(LN) (Theorem 1). A Safe Expansion Corollary and Monotone Growth Lemma confirm expansion without discontinuous jumps. Benchmarking across 16 experiments (up to N=14) shows A-H-EFT achieves fidelity F=0.54, doubling static H-EFT-VA (F=0.27) and outperforming HEA (F~0.01), with gradient variance >= 0.5 throughout. For Heisenberg XXZ (Delta_ref=1), A-H-EFT identifies the negative ground state while static methods fail. Results are statistically significant (p < 10^-37). Robustness over three decades of hyperparameters enables deployment without search. This is the first rigorously bounded trajectory through the VQA landscape.

Quantum cloud providers can identify a user's algorithm and secret problem structure without ever seeing actual quantum data, simply by analyzing routine metadata collected for billing and system management. Existing confidentiality tools such as blind quantum computation and quantum homomorphic encryption protect the quantum payload itself, but they do not protect this classical orchestration metadata. This leaves an unexplored security risk in the logs generated when a large quantum program is split into smaller pieces to fit onto limited hardware, a process known as circuit cutting. These fragments leak sensitive information through what we term the topological transpilation penalty: the unavoidable depth and gate inflation added when a compiler reorganizes a program for a restricted hardware topology. Tests on a 156-qubit production Quantum Processing Unit (QPU) show that traditional timing side-channels fail in this setting, since hardware control-plane delays mask actual quantum execution time. The unique shape of the transpilation penalty acts instead as a persistent structural fingerprint for the hidden workload. Using 12,000 circuit fragments across eight algorithm families, our attack recovers algorithm family and Hamiltonian k-locality with near-perfect accuracy, achieving instance-disjoint AUC = 1.000 for both. This leakage persists under size-holdout evaluation on unseen circuit scales, with AUC = 0.987 and 0.986 respectively. The cutting mechanism is inferred with AUC = 0.991, and hardware topology is recovered well above chance with AUC = 0.818. These results show that circuit cutting exposes algorithmic intent, and potentially proprietary problem structure, through metadata alone, without any need to observe quantum data.

We investigate how the junction law for multipartite entanglement is realized in confining holographic backgrounds, using genuine multi-entropy (GM) as our main diagnostic. We first study an AdS$_3$ hard-wall toy model as an analytic benchmark, where multi-way cuts and junction geometries can be analyzed explicitly. In this setup, we classify the relevant saddles, determine the dominant phases, and show that the genuinely multipartite contribution diagnosed by GM is localized near the junction. We also examine how this structure depends on subsystem sizes, asymmetry, and the confinement scale, including phase transitions between competing saddles. We then move beyond the hard-wall benchmark to smooth confining geometries, focusing on the D4-soliton and D3-soliton backgrounds and formulating the corresponding framework also for the Klebanov–Strassler background. In the smooth-cap examples, we find that the junction picture persists, while the detailed phase structure differs from the hard-wall case: in particular, the hard-wall plateau does not survive, and GM instead decreases monotonically and vanishes at a finite critical scale. We also find that the short-distance behavior is background-dependent, with $\mathrm{GM}^{(3)}\sim L^{-4}$ in the D4-soliton background, $\mathrm{GM}^{(3)}\sim L^{-2}$ in the D3-soliton background, and $\mathrm{GM}^{(3)}\sim L^{-2}\cdot (\log L)^{2}$ in the Klebanov–Strassler background. These results clarify which features of the junction-law picture are robust in confining holography and which features of the phase structure and short-distance scaling are background-dependent.

The hypothesis that disentanglement spontaneously occurs in quantum systems is motivated by some outstanding issues in the foundations of quantum mechanics. However, for some cases, spontaneous disentanglement enables the violation of the causality principle. To mitigate the conflict with causality, a formulation for the hypothesis, which is based on the maximum entropy principle, is proposed. The method of Lagrange multipliers is implemented to ensure consistency with causality. The proposed formulation is applicable for any quantum system having a Hilbert space of finite dimensionality.

Accurately evaluating finite-temperature properties of quantum many-body systems remains a central challenge. Many existing quantum approaches require thermal-state preparation at each target temperature, making low-temperature calculations especially demanding in terms of circuit depth and accuracy. In this work, we present the finite-temperature quantum Krylov (FTQK) method, which combines stochastic trace estimation with a Hermitian generalized eigenvalue problem based on $\cos(\tilde H)$. The only quantum-side input required is the temperature-independent real-time overlap sequence $g_n=\langle\phi|e^{-in\tau H}|\phi\rangle$. All benchmark results presented in this work were obtained from classical numerical simulations of the quantum-circuit protocol. For periodic spin-$\frac{1}{2}$ Heisenberg chains with $N=14$ and $18$, FTQK accurately reproduces the specific heat, magnetic susceptibility, and entropy in the noiseless case. It also accurately reproduces the specific heat of the $N=14$ honeycomb Kitaev model, demonstrating that FTQK can be applied beyond simple one-dimensional chains to a model on a two-dimensional lattice with bond-dependent interactions. Furthermore, in a proof-of-principle finite-shot study for the $N=14$ Heisenberg chain, the main thermodynamic features remain well preserved even at $\sigma=10^{-3}$. Here, $\sigma=1/\sqrt{N_{\mathrm{shot}}}$, where $N_{\mathrm{shot}}$ is the number of shots used to estimate an overlap. These results demonstrate the potential of FTQK for finite-temperature calculations using reusable, temperature-independent real-time overlap data.

$SU(\infty)$ Quantum GRavity (QGR) is a recently proposed fundamentally quantum approach to gravity and cosmology. In this model the Hilbert space of the Universe represents $SU(\infty)$ symmetry. Its fragmentation generates approximately isolated subsystems (particles) representing, in addition to $SU(\infty)$, finite-rank local symmetries. The common $SU(\infty)$ is associated to quantum gravity, and at lowest quantum order the effective action for all symmetries is Yang-Mills on a 4D parameter space $\Xi$. Nonetheless, physical processes and measurables must be independent of the geometry of $\Xi$. In previous works we demonstrated that diffeomorphism of $\Xi$ can be neutralized by $SU(\infty)$ gauge transformation. In this work we show that the invariance of action under variation of $\Xi$'s metric leads to a constraint resembling Einstein equation. It consists of energy-momentum tensors for all components of the model, including the spin-1 gravitons. In addition, through calculation of quantum information measures we study the effect of Hilbert Space Fragmentation (HSF) on the emergent classical spacetime and different cosmological era, such as inflation, reheating, and late time accelerating expansion. The results of this preliminary and approximate investigation show that HSF may be classically interpreted as these phenomena. Consequently, inflaton, quintessence, and similar fields associated to these processes may be order parameters that phenomenologically present them.

Although optimization is one of the most promising applications of quantum computers, the development of effective optimization strategies requires real-world test cases. When planning our recent wedding reception, we realized that the problem of optimally seating our guests, given constraints related to guests' relatedness, shared interests, and physical needs, could be mapped to a cost function network (CFN) form solvable with classical or quantum optimization algorithms. We compared the seating optimization performance of classical Monte Carlo CFN solvers in the Masala software suite to that of quantum annealing-based CFN optimization algorithms using one-hot, domain-wall, and approximate binary mappings, which we had developed for protein design problems. Surprisingly, the D-Wave Advantage 2 system, which performs well on similarly-structured CFN problems for protein design, struggled to return optimal seating arrangements that were easily found by classical Monte Carlo methods. We provide our seating optimization benchmark set, and code to convert seating optimization problems to CFN problems, as a plugin library for Masala, permitting this class of real-world problems to be used to benchmark performance of current and future classical CFN solvers, quantum optimization algorithms, and quantum computing hardware.

We present a computational platform for modeling chemical reactions in complex molecular environments, focused on ligand-protein binding in drug discovery. The platform implements our new quantum-in-quantum-in-classical (QM/QM/MM) multiscale embedding model that integrates molecular dynamics with a quantum-information-enhanced density matrix embedding theory and quantum chemistry solvers, including explicit solvent. Quantum-information metrics are utilized to generate entanglement-consistent orbitals, enabling a high-accuracy description of strongly correlated regions. The framework supports multiple computational backends, including multi-CPU, NVIDIA multi-GPU architectures, and quantum hardware (IQM, IonQ, IBM) integrated under CUDA-Q, and is designed for compatibility with future fault-tolerant quantum systems. The new platform's capabilities are demonstrated by modeling covalent docking of zanubrutinib to Bruton's tyrosine kinase via a Michael addition mechanism, computing the full reaction energy profiles and energy barriers at a reduced computational cost relative to existing methods. As a 2nd-generation anticancer agent, zanubrutinib serves as a proof of concept for covalent inhibitor discovery. Accurate first-principles reaction barrier estimations provided by our method can contribute to reducing false positive and negative rates in drug discovery pipelines. Scalability is validated through benchmarks on GPU clusters, cloud-based CPU infrastructures. We demonstrate integration with quantum devices (up to 20 qubits), alongside resource estimates for fault-tolerant quantum computing, indicating potential speedups of up to 20x. Beyond single reactions, the platform supports the construction of reaction networks in chemical metric space, facilitating ligand screening and systematic exploration of reactive pathways.

Hybridization between bosonic excitations can produce topological bands, but their Chern numbers conceal how constituent modes generate the resulting Berry geometry. Here we derive an exact decomposition of Berry curvature in multicomponent bosonic Bogoliubov-de Gennes systems into weighted constituent-sector contributions and a term associated with single-quasiparticle entanglement. The latter couples momentum-space variations of the sector imbalance to a gauge-invariant relative Berry connection and has an equivalent gauge-covariant pseudospin representation. Applying this framework to magnon-phonon excitations in a ferromagnetic skyrmion crystal, we find that magnetoelastic coupling generates topological low-energy hybrid bands despite the trivial topology of the two lowest uncoupled magnon bands and degeneracies among folded phonons. The resulting Chern numbers are assembled through constructive, entanglement-assisted, and entanglement-compensated geometric mechanisms. In particular, bands with identical Chern numbers can exhibit opposite entanglement contributions. Our approach thus resolves nonquantized internal geometric contributions hidden within quantized hybrid-band invariants, without requiring an effective two-level Hamiltonian.

We investigate the integrable structure and soliton dynamics of a coupled modified Korteweg-de Vries (cmKdV) system with a real symmetric coupling matrix. We introduce a vector reformulation of Hirota's bilinear formalism in which both the bilinear equations and their solutions are expressed directly at the vector level, rather than through a component-wise construction. This formulation preserves the intrinsic structure of the coupled system and provides a compact framework for multi-component nonlinear wave dynamics. Within this approach, we construct explicit one-, two-, and three-soliton solutions in closed vector form and recover the three-soliton condition directly at the vector level, confirming consistency with integrability. The method enables a unified treatment of focusing, defocusing, and mixed-sign regimes. In particular, for indefinite coupling, it reveals the existence of nontrivial vector ground states, leading to soliton solutions on non-zero backgrounds. These results highlight the structural advantages of the vector bilinear approach and open perspectives for the study of more general nonlinear excitations in multi-component integrable systems.

Optical imaging of quantum emitters is essential for a wide range of quantum applications. Conventional confocal imaging relies on point-by-point raster scanning, which is inherently time-consuming and photon-inefficient, particularly for sparse emitter distributions and photon-limited samples. Here, we demonstrate a compressive sensing-based imaging approach, where spatially structured wide-field excitation replaces raster scanning, enabling reconstruction of sparse emitters. In our implementation, random binary patterns are used to acquire compressive measurements, from which the spatial fluorescence distribution is reconstructed using a GPSR-BB algorithm. We experimentally demonstrate this approach using nitrogen-vacancy (NV) centers in diamond as a representative platform, with high-fidelity image reconstruction achieved using only approximately $20\%$ of the measurements required for conventional raster scanning. In addition to intensity reconstruction, we extend this framework to reconstruct spatial maps of the second-order correlation function $g^{(2)}(0)$ from compressive measurements. This enables identification of single-photon emitters through antibunching signatures using significantly reduced data.

In [\href{https://quantum-journal.org/papers/q-2022-12-07-872/}{Quantum 6, 872, 2022}], Linden and de Wolf proposed a lightweight protocol for verifying average-case correctness of the quantum Fourier transform (QFT). They showed that good average-case QFT performance is sufficient for good worst-case performance in several quantum information-processing tasks. In this work, we study whether such average-case guarantees are also sufficient when the QFT is used coherently inside the Harrow–Hassidim–Lloyd algorithm. We show that the original average-case condition is not quite strong enough for this purpose, due to possible relative phase errors between different eigenspaces. To address this, we introduce a strengthened Linden–de Wolf-type verification condition that controls the relevant coherences, and prove that it guarantees worst-case correctness of the HHL algorithm in several natural settings.

Equilibrium quantum phase transitions profoundly reshape the ground state of light-matter systems; yet, the resulting quantum correlations, such as squeezing and entanglement, remain experimentally inaccessible since they involve virtual ground state excitations. We investigate how a nonadiabatic modulation of a Hamiltonian parameter can convert these virtual excitations into real photons, enabling quantum vacuum radiation. We show that proximity to the critical point strongly enhances the emitted photon flux and the non-classical nature of the emitted radiation, even when thermal fluctuations are expected to dominate. In addition, higher-order processes become relevant even for small modulation amplitudes, and we develop a framework that systematically incorporates them. Our results reveal that criticality can act as an efficient amplifier of vacuum fluctuations, offering new routes to probe and exploit quantum critical ground states.

This is the text for a one quarter or one semester undergraduate course on quantum computing that has been given at the University of California Santa Cruz. It is intended for students in the physical sciences who have already studied linear algebra (though a review of this topic is given in the course). No prior knowledge of quantum mechanics is required. The most important topics covered are Shor's algorithm and an introduction to quantum error correction. Most of the text is a build-up to these topics.

In hybrid classical-quantum computing, variational quantum algorithms (VQAs) have emerged as a promising approach in the Noisy Intermediate-Scale Quantum (NISQ) era; however, their performance is often hindered by barren plateaus, where gradients vanish exponentially, rendering optimization ineffective. In this work, we introduce a residual-inspired quantum circuit architecture that incorporates a single messenger qubit, referred to as Q-LINK. By conducting numerical simulations on random quantum states, we observe that Q-LINK significantly enhances optimization behavior by sustaining larger gradient variance and accelerating convergence. Additionally, Q-LINK improves convergence efficiency by 4-6 times and increases gradient variance by up to two orders of magnitude compared with the Vanilla model. To further characterize the impact of the proposed structure, we analyze the expressibility of the circuits before and after introducing Q-LINK and find that the overall expressibility value remains largely unchanged, indicating that the original representational capacity of the circuit is preserved. In addition, we visualize the loss landscapes of different architectures to provide insights into how the proposed design reshapes the cost function landscape. These results demonstrate that introducing only a single messenger qubit can effectively mitigate barren plateau effects while maintaining the ability to explore the Hilbert space of variational quantum circuits.

Some skeptical arguments on the physical reality of quantum states are given. First, I argue that the algebraic formalism of quantum field theory in curved spacetime (algebraic QFTCS, AQFTCS) leads to such a skepticism. Of course we have the purely mathematical notion of states on a $C^{*}$-algebra $\mathfrak{A}$, but usually in non-relativistic quantum mechanics and quantum field theory in Minkowski spacetime (QFTM), not all of them are considered to be physically real; Some of them are physically real (or realizable) states, but others are non-physical “fictional” states. Only the states which can be expressed as a density matrix on a fixed “physical Hilbert space” (the GNS representation space of $\mathfrak{A}$ w.r.t. the vacuum) are viewed to be physically real. On the other hand, in QFTCS, there is no distinguished physical Hilbert space; no distinguished vacuum state. Thus we cannot distinguish physically real states from fictional states. The second part of my argument is a counterargument to what I call “pragmatic realism on quantum states”, which insists as follows: “We are permitted to regard a quantum state as a physical reality, because the concept of quantum state is indispensable in quantum physics.” I argue that the concept of quantum state is indeed dispensable in non-relativistic QM, and hence this pragmatic realist thesis is vacuous there. I give a conjecture that it is also dispensable in QFTM and QFTCS, and some preliminary considerations on it.

Many nonlinear PDEs have singular or oscillatory solutions or may exhibit physical instabilities or uncertainties. This requires a suitable concept of physically relevant generalized solutions. Dissipative measure-valued solutions have been an effective analytical tool to characterize PDE behavior in such singular regimes. They have also been used to characterize limits of standard numerical schemes on classical computers. The measure-valued formulation of a nonlinear PDE yields an optimization problem with a linear cost functional and linear constraints, which can be formulated as a linear programming problem. However, this linear programming problem can suffer from the curse of dimensionality. In this article, we propose solving it using quantum linear programming (QLP) algorithms and discuss whether this approach can reduce costs compared to classical algorithms. We show that some QLP algorithms, such as the quantum central path algorithm, have up to polynomial advantage over the classical interior point method. For problems where one is interested in the dissipative weak solution, namely the expected values of the Young measure, we show that the QLP algorithms offer no advantage over direct classical solvers. Moreover, for random PDEs, there can be up to polynomial advantage in obtaining the Young measure over direct classical PDE solvers. This is a significant advantage over standard PDE solvers, since the Young measure provides a more detailed description of the solution. We also propose some open questions for future development in this direction.

According to various no-go results in the foundations of quantum mechanics, for any system associated to a Hilbert space of dimension higher than two, it is not possible to assign definite truth values to all propositions pertaining to the system without generating a Kochen-Specker contradiction. In this respect, the Bub-Clifton uniqueness theorem is utilized for arguing that truth-value definiteness is consistently restored with respect to a determinate sublattice of propositions defined by the state of the quantum system concerned and a particular observable to be measured. On this basis, a perspectivist/contextual account of truth valuation in the quantum domain is produced that satisfies Tarski's criterion of material adequacy for a theory of truth. In light of the latter, perspectivist truth conforms to perspective or context-bound correspondence of a de re nature, designating locally an objectively existing state of affairs. Such an account derives by virtue of the microphysical nature of physical reality in displaying a context-dependence of facts; thus, it essentially opposes a non-perspectival, metaphysically fixed point of reference, or a panoptical standpoint from which to state all facts of nature.

We develop a detector-based framework in which quantum theory and spacetime geometry arise within a common inferential structure. Detector states and a detector kernel assign amplitudes to measurement events, allowing quantum theory to be interpreted as weighting hypothetical configurations consistent with observed detector clicks. Using a Gaussian detector model with phase structure, we show that distinguishability induces an information geometry on detector-state space, described by the quantum geometric tensor. A Lorentzian spacetime metric is reconstructed from coupled position and time detector sectors, with both amplitude and phase deformations contributing to geometry. Scalar curvature acquires an operational interpretation as a local deficit of distinguishable outcomes. We construct an effective consistency functional combining detector-deformation cost with a geometric term selected by locality and diffeomorphism invariance. Its stationary configurations yield the Einstein equation, with a stress-energy tensor arising from detector deformations. Vacuum configurations need not be flat, while local deformations provide an operational notion of matter and recover standard field-theoretic behavior in the scalar sector.