Papers

We expand on the ideas developed by C. Jarzynski in Physica A 552, 122077 (2020), where an integral fluctuation theorem was derived with the aim of obtaining thermodynamic inequalities stronger than those implied by the Jarzynski equality. Restricting ourselves to the quantum setting, we derive the corresponding detailed fluctuation theorem and additional detailed and integral fluctuation theorems; we also provide a clear physical interpretation of the stochastic quantities defined in the previous reference. Furthermore, we show that their averages are given by the nonadiabaticity parameter (i.e., the relative entropy between the final state after a finite-time driving protocol and the corresponding adiabatically evolved state) and the excess work (also known as inner friction). We elaborate on the inequalities derived from the fluctuation theorems and discuss their connection to irreversibility and formulations of the Second Law.

Many production quantum programming languages represent hybrid quantum computations by extending a classical base language with a quantum effect, where qubits are addressed by reference, and quantum operations are understood to mutate some external quantum state. However, the semantics of this view of quantum computation remains underdeveloped, especially when the language allows mid-circuit measurements and non-termination. In this work, we provide a general method for building denotational semantics for such languages, by defining the quantum orchestra monad, which precisely captures this style of quantum effect. The monad has a concrete presentation, being based on the formalism of quantum instruments, a common tool in quantum information theory for capturing the action of a quantum process along with its classical outcomes. It acts on the category DCPO, and so enables the interpretation of divergent hybrid programs. The quantum orchestra monad serves as a natural extension of both the classical state monad and the probabilistic powerdomain monad. We investigate some of the subtleties present when trying to naïvely extend these definitions to the quantum non-commutative case.

Imaginary-time evolution (ITE) is a powerful method for ground-state preparation of a given Hamiltonian. The normalized ITE can be viewed as a gradient flow of the energy expectation value with respect to the Fubini–Study metric. In this work, we propose a generalized nonlinear imaginary-time evolution (NITE) for more general quantum state-preparation tasks. We further present a hardware-efficient variational implementation of NITE and reveal its connection to quantum natural gradient descent. NITE is applied to several subroutine tasks, including variance minimization in variational eigensolvers, probe-state preparation in variational quantum sensing, and excited-state preparation using penalty terms. We prove that NITE achieves a local exponential convergence rate under reasonable assumptions. Our results show that NITE outperforms standard gradient descent and can serve as an efficient optimization method for variational tasks beyond ground-state preparation.

Active selection of the measurement basis underpins quantum protocols including device-independent quantum key distribution, quantum teleportation with active feed-forward, and Bell tests. High-speed operation is crucial to minimize the latency between consecutive measurement choices, enabling faster protocol execution and higher achievable communication rates. Here, we demonstrate a GHz-rate all-fiber polarization state analyzer enabling active, trial-by-trial reconfiguration of the measurement basis, which we validate by performing a CHSH Bell test. The state analyzer is based on a fiber Sagnac interferometer incorporating a lithium niobate electro-optic phase modulator, built entirely using off-the-shelf fiber-optic components. Operating at a nominal repetition rate of 1 GHz, the system performs dynamic polarization measurements for the CHSH Bell test, achieving polarization visibilities up to 99% and a violation of $S = 2.6975 \pm 0.0005$, certifying entanglement at an unprecedented rate. Furthermore, the system demonstrates excellent long-term stability, preserving the Bell-inequality violation for more than 6 hours without realignment. These results establish the proposed state analyzer as a versatile, scalable platform for quantum communication protocols requiring fast, reconfigurable polarization-state measurements.

We introduce two non-adiabatic semiclassical methods that employ two coupled Gaussian wavepackets, each one traveling on a separate diabatic potential energy surface. The wavepackets take the form of thawed Gaussians and are driven by classical equations of motion which account for the diabatic coupling. The classical equations of motion are derived in one case by enforcing the thawed Gaussian ansatz, while in the other the time-dependent variational principles to the thawed Gaussian ansatz. After a sanity check where both approximations reproduce Rabi oscillations, the methods are applied to two non-adiabatic potential energy scenarios. The first one involves two coupled displaced harmonic oscillators, as in a typical electron transfer reaction. The second one comprises a Morse potential coupled to an upper dissociative state, modeling a photo-dissociation process. In both scenarios, the variational thawed Gaussian approach is quite accurate, while the standard thawed Gaussian one fails to fully capture the non-adiabatic effects. Ultimately, non-adiabatic molecular dynamics is reproduced by means of two classical trajectories without introducing any artificial jump or other ad-hoc non-classical effects.

Controlling the energy scales of a quantum system is essential for defining robust qubits. In silicon spin qubits, the nearly degenerate conduction-band valleys create a leakage channel from the single-spin computational basis, posing a challenge to scaling and to shuttling-based architectures. Here, we measure the relevant energy scales of single-electron spin qubits in buried silicon quantum wells co-designed for low disorder and high valley splitting. Across a linear array of four quantum dots with an average orbital energy of 2.4(2) meV, we report an average single-electron valley splitting of 0.40(6) meV and an average two-electron singlet-triplet splitting of 0.24(7) meV. In three dots, we observe a strong correlation between valley splitting and orbital energy, with an average linear coefficient of $\approx 0.22$ (meV/meV), demonstrating that electrostatic confinement can increase the valley splitting by several hundred microelectronvolts. In contrast, the remaining dot exhibits the highest valley splitting of 0.76(2) meV and low correlation, suggesting excellent characteristics for spin-qubit operation. Our findings demonstrate that strong confinement can be exploited in buried quantum wells to effectively enhance the valley splitting, thereby establishing a viable path toward the realization of shuttling and sparse-occupation-based architectures in low-disorder heterostructures.

Processes with indefinite causal orders (ICOs), such as the quantum switch, are higher-order quantum processes that superpose the order in which quantum operations are performed. Such coherent control yields computational advantages but is not faithfully captured by existing quantum programming languages: either they are restricted to the unitary case, and thus cannot combine ICOs with measurement, or they treat coherent control nonlinearly. In both cases, they do not realize the full computational power of ICOs. We introduce a higher-order quantum functional language that supports general quantum computation, not merely the permutation of channels, and whose linear type system allows quantum control to be well-defined beyond the unitary case, on arbitrary quantum channels. We equip this language with a small-step operational semantics that synchronizes measurement outcomes across superposed branches, using device references and a memory function. We also give a denotational semantics by means of completely positive maps. With linearity as the only constraint, some well-typed terms would denote unphysical maps. We therefore impose a typing discipline that goes beyond linearity, and interpret programs in the causal category Caus[CPM], under which every well-typed program is physically meaningful, a property that can be checked statically and efficiently. We prove soundness, and study the language's expressive power: it can express every quantum channel at first order, and at second order a large subclass of the so-called quantum circuits with quantum control (QC-QCs), containing the quantum switch. Last but not least, we show that this language is well-designed enough to be extended to the nonlinear setting with recursion.

We introduce a hybrid qubit-boson beam-splitter gate in which a microwave cavity mode couples to an exchange-dressed two-level subsystem of an interacting two-qubit system in the presence of Kerr nonlinearity. Starting from a general circuit quantum electrodynamics (cQED) model, we derive the corresponding hybrid qubit-cavity interaction, develop its open-system description including photon- and qubit-sector-bath-induced dissipation and obtain in the weak-dissipation regime an analytical expression for the average gate fidelity. We further identify carbon-nanotube circuit QED as a concrete platform for implementing and controlling the gate, provide a representative operating regime and perform noiseless and noisy numerical simulations to study the gate dynamics and benchmark the analytical results. Beyond this implementation route, the proposed hybrid primitive provides a natural building block for many-body dynamics, including quantum-cellular-automaton (QCA) and lattice-gauge-inspired architectures and, through its collision-model reformulation, also suggests connections to noisy QCA, non-Markovian extensions and reservoir-style quantum information processing.

Majorana zero modes provide a route to fault-tolerant qubits by encoding information non-locally in fermion parity. Their sensitivity to noise is expected to decrease exponentially with increasing separation between the Majoranas, a suppression known as topological protection. Kitaev chains engineered in quantum dot-superconductor arrays provide a tunable platform in which separated Majorana zero modes can emerge at the ends of the chain, even in two-site chains. These minimal-chain modes are known as poor man's Majoranas and retain characteristic Majorana properties, including near-zero energy and equal electron-hole character, but have only limited protection. A key outstanding challenge is to move beyond identifying such modes in electrical transport measurements and achieve coherent qubit control in the time domain. Here, we demonstrate a Majorana parity qubit by realizing coherent coupling between two-site Kitaev chains. Since total fermion parity is conserved, the system separates into global even and odd parity manifolds. We observe coherent parity oscillations in both manifolds with equal oscillation frequencies at the Majorana sweet spot, as predicted for isolated Majorana zero modes. We further show that the oscillation frequency and coherence depend systematically on inter-chain coupling and quantum-dot detunings, in close agreement with our model for short, partially protected chains. Our results establish the first coherent control of a Majorana qubit, encoded in the fermion parity of Majorana zero modes in minimal Kitaev chains.

Hybrid devices integrating quantum dots with Josephson junctions are gaining interest because they combine spin-based quantum computing with circuit quantum electrodynamics (circuit QED) methods. In particular, Andreev spin qubits have shown significant experimental progress including strong two-qubit coupling, and are predicted to exhibit all-to-all connectivity. Here we propose superconducting singlet-triplet (SST) qubits that rely on parallel-aligned double quantum dots in Josephson junctions. While Andreev spin qubits require spin-orbit interaction to unlock the spin degree-of-freedom, SST qubits do not require spin-orbit interaction, making the advantages of hybrid devices available to a wider range of materials. Similar to Andreev spin qubits, the qubit states couple to the superconducting phase across the junction, which allows for control and readout using circuit QED, and supports all-to-all connectivity. Only $N$ flux lines are required to perform any single- and two-qubit gate among $N$ qubits, and thus the overhead of control lines is small. Finally, linear protection from charge or flux noise makes these qubits interesting candidates for a future quantum processor.

Chaotic many-body spectra are expected to densely fill their energy window. We show that constrained spin chains with chiral symmetry evade this expectation by hosting an exponentially large manifold of symmetry-protected exact zero modes separated from the surrounding spectrum by a sharp gap at zero energy. The gap is generated by chaotic level repulsion, with width set by the number of zero modes times the mean level spacing. We verify this mechanism in an East-West kinetically constrained chain, develop a minimal random-matrix description, and show how the gap can be detected through linear-response spectroscopy.

Bayesian games, also known as games of incomplete information, are a fruitful arena for exploring the impact of correlations on a set of independent agents (players) via the game equilibria to which they give rise. It was realised some time ago that quantum states shared between the players can lead to new and beneficial equilibria, compared to classical correlation. While until now examples of this effect required an entangled state, here we show that even separable states can create new, genuinely quantum equilibria in games, that are advantageous with respect to all classically correlated equilibria. This shows that non-classical correlations beyond entanglement are indeed a resource, even in otherwise entirely classical situations. Our result brings quantum advantage in games significantly closer to possible realisation.

Quantum subspace-diagonalization methods, particularly Quantum Krylov Diagonalization (QKD), provide a promising route for computing low-energy spectra of quantum many-body Hamiltonians. However, existing quantum Krylov approaches rely on non-orthogonal Krylov bases, requiring overlap-matrix regularization that limits numerical stability and accuracy. In this work, we introduce an Orthogonal Quantum Krylov Diagonalization (OQKD) framework that reformulates the classical Lanczos recursion at the operator level, enabling an orthogonal quantum implementation of Krylov-subspace diagonalization. By expressing Lanczos vectors as polynomial transformations of the Hamiltonian, OQKD reproduces the orthogonality, tridiagonal structure, and convergence behavior of the classical Lanczos algorithm thus eliminating the need for overlap-matrix regularization. We further show that the required Lanczos polynomials can be implemented using block encoding and Generalized Quantum Signal Processing with the same asymptotic query complexity as Chebyshev-based QKD methods. Numerical simulations of the $J_1$–$J_2$ Heisenberg model confirm the classical Lanczos convergence and numerical stability of the proposed method, while the measurement-complexity scaling is established analytically. Building upon the OQKD framework, we then introduce a restarted state-preparation protocol that replaces a single high-degree polynomial transformation with a sequence of fixed low-degree transformations, maintaining an affordable block encoding success probability while retaining comparable convergence. These results establish OQKD as an orthogonal quantum analog of the classical Lanczos algorithm and identify the restarted protocol as a promising state-preparation strategy for Quantum Phase Estimation.

We introduce a new notion of distance between two graph states $|G\rangle$ and $|G'\rangle$ on the same set of qubits. This distance is the minimum number of ancilla qubits in a graph state $|\widehat{G}\rangle$ from which both $|G\rangle$ and $|G'\rangle$ can be “easily prepared”. (When preparing graph states, we are only allowed to use one-qubit Clifford gates, one-qubit Pauli measurements, and classical communication.) We give a graphical description of this distance through the lens of vertex-minors. We then show how this distance yields quantum network analogs of many graph edit-distance problems. Using this framework, we develop classical algorithms for identifying the “highly entangled clusters” of a graph state $|G\rangle$. The ancilla integrity problem asks, given a graph $G$ and integer $k$, for the minimum – over all graph states $|G'\rangle$ with distance at most $k$ from $|G\rangle$ – of the maximum component size of $G'$. Up to a factor of $2$ in the number of ancilla qubits, this problem is equivalent to rank integrity, where the distance between $G$ and $G'$ is instead the minimum rank of the sum of their adjacency matrices over $\text{GF}(2)$. We prove that rank integrity is XP parameterized by $k$. We also prove the complementary hardness result that rank integrity is W[1]-hard in $k$. Finally, we give an explicit $\mathcal{O}(n^6)$-time algorithm for ancilla integrity when $G$ has $n$ vertices and $k=1$.

Quantum reservoirs offer a hardware-friendly route to quantum machine learning, replacing trainable circuits with fixed random dynamics and a classical readout. Because the reservoir is not optimized, performance depends entirely on the choice of reservoir family, yet existing diagnostics demand resources that grow exponentially with system size. We introduce a scalable, hardware-agnostic framework built on two complementary quantities. The first is a task-independent order-statistics (ORS) expressivity score, which compares only the largest output probabilities of a reservoir ensemble against an analytical Haar baseline. It never reconstructs the full output distribution, is cost-independent of Hilbert-space dimension, and admits a closed-form depolarizing noise correction, making it directly usable on hardware. The second is the task-dependent effective rank $R_{\mathrm{eff}}$ of the feature matrix, which measures how much input-dependent information reaches the readout. We validate the ORS score against established complexity diagnostics and confirm it remains informative under simulated noise and on IBM quantum hardware. Across synthetic and real quantum extreme learning machine and quantum reservoir computing benchmarks, ORS captures the intrinsic expressivity hierarchy of reservoir families while $R_{\mathrm{eff}}$ determines when that expressivity becomes usable predictive information.

Cold neutral atoms is a powerful tool for many experiments ranging from frequency standards and sensing to quantum simulations. Sensitive experiments often demand transferring cold atoms from one vacuum chamber to the other with better optical access and vacuum. In case the transfer is done with a beam waist controlled by a focus-tunable lens, repeatability of the transfer as well as stability of the final position can be below the experimental demands. Here we implement the scheme for stabilization of the axial waist position of the optical dipole trap in the whole range of transfer (38 cm) using speckle patterns, which provides enough stability to achieve Bose-Einstein condensation of around 4*10^4 thulium atoms in the trap formed by the transport beam and the other beam in the target vacuum volume

In this work, we investigate quantum complexity diagnostics of primordial curvature perturbations within the inflationary paradigm. We compare canonical scalar-field inflation with the modified gravity model $f(\phi,R)$, focusing on the evolution of the two-mode squeezed state generated by the coupling between the $\vec{k}$ and $-\vec{k}$ momentum sectors. Starting from the quadratic action for curvature perturbations, we derive the evolution equations for the squeezed strength $r_k$ and squeezed angle $\phi_k$, utilizing them to evaluate both circuit complexity and Krylov-space diagnostics. Specifically, we compute the Krylov complexity, Krylov entropy, Lanczos coefficients $b_n$, and an effective dissipative contribution $c_n$ within an open-system extension. Our numerical results demonstrate that the $f(\phi,R)$ coupling enhances the squeezed strength relative to the canonical scalar field inflation. Since the Krylov complexity of the two-mode squeezed state is directly controlled by the mean pair number ($K=\sinh^2 r_k$), this enhancement leads to a smaller growth in Krylov complexity and related Krylov-space quantities. Furthermore, circuit complexity displays a more pronounced evolution in the $f(\phi,R)$ framework, particularly after the horizon exit regime. Ultimately, our work sheds new light on the quantum complexity of modified gravity $f(\phi,R)$.

Quantum simulation is a leading application of quantum computing, but scaling to chemically relevant problems requires modular architectures composed of interconnected quantum processing units. In such systems, inter-core quantum communication becomes a major performance bottleneck. In this work, we present COSMA, a communication-aware compilation framework for fermionic simulation kernels targeting modular quantum architectures. Our approach jointly optimizes fermion-to-qubit mapping, Pauli scheduling, and qubit allocation to minimize inter-core state transfers. Evaluated on molecular benchmarks, COSMA achieves up to $2.5\times$ reduction in communication cost compared to state-of-the-art baselines, with a median improvement of $1.7\times$. These results demonstrate that cross-layer co-design is essential for efficient and scalable quantum simulation on multi-core quantum hardware.

We study an adversarial bandit problem for entanglement-based quantum-network routing over a modest graph corpus. Alice selects an end-to-end repeater route for an Ekert-91 protocol (E91) representing her move, while Eve selects an attack surface, either edge intercept–resend or repeater memory degradation. Payoffs are drawn from cached SeQUeNCe-simulated E91 transcripts, and Alice accepts a turn when the finite-sample statistic violates the Clauser-Horne-Shimony-Holt (CHSH) bound. Performing adversarial co-learning across 50 structured topologies, we find that learned retention tracks a full-matrix minimax reference closely (Pearson $r=0.99$): under a one-surface Eve action model, bottleneck families have zero retention, while non-bottleneck families follow a $1-1/N$ coverage principle. We then fit decision-tree explanation models to graph-, attack-, and route-level topology-corpus targets and report their faithfulness. Finally, we construct prompt records for local language models to summarize the tree evidence, resulting in an open-source explanation workflow for quantum-repeater network games.

Photonic graph states are key resource states for measurement based quantum information processing. As semiconductor quantum dots are excellent deterministic photon emitters, several protocols using them for the generation of linear cluster states have been proposed, either based on constant precession of a hole or electron spin in a weak magnetic field, or based on optical spin control, in a stronger magnetic field. We theoretically compare four such schemes, using polarization or time-bin encoding, respectively, for a range of cavity environments and spin coherence times. In particular we study how different error mechanisms affect the different schemes, using a microscopic model of the spin control, the excitation and emission dynamics, and of the phonon bath. We find the spin-precession based schemes to scale well with strong cavity enhancement and to be naturally robust against phonon-induced decoherence, while the schemes using optical spin control can perform well for lower spin coherence times and are strongly dependent on the cooperativity of the cavity induced cycling transition. Our results provide a regime map for choosing between magnetic-field-driven and optically controlled protocols depending on spin coherence time, Purcell enhancement, and suppression of unwanted decay channels.

Progress towards a quantum advantage using known heuristic methods for combinatorial optimization is impeded by hardware noise and limited qubit count. Here, we propose a noise-aware adaptive approach to quantum approximate optimization, Noise-Directed Adaptive Warm-Starting (ND-AWS), that builds on recent concepts such as Warm-Start QAOA and Noise-Directed Adaptive Remapping. By leveraging bitflip gauge transformations, our algorithm exploits amplitude-damping-like noise components. We experimentally implement high-performance quantum optimization ansätze on 100-qubit Ising Hamiltonians, showing that ND-AWS generally improves the performance over a non-gauge-transformed iterative Warm-Starting variant, at no additional circuit cost. This places our results among the highest-quality demonstrations of quantum optimization with similar ansätze at this scale. Crucially, the simplicity of the framework opens the door for future enhancements such as adaptive bias schedules, and integration with classical solvers.

Reliable benchmarking of Quantum Error Mitigation (QEM) requires distinguishing genuine improvements from artefacts of the post-processing arithmetic. In this paper, we expose a failure mode in Richardson Zero-Noise Extrapolation (ZNE), a widely used technique routinely (and often implicitly) relied upon in benchmarks and experiments. When noise amplification operates beyond usable signals - a regime that is quickly reached on current hardware for non-trivial circuits - we show that the extrapolation no longer reflects the underlying physics, but collapses into a fixed rescaling of a single noisy measurement, producing a bogus apparent improvement that is independent of noise amplification. This poses a rarely considered threat to the validity of many empirical evaluations in quantum computing. Measurements on real hardware (IQM Euro-Q-Exa) confirm this collapse with ordinary folding alone: as circuit depth erodes the signal, the reported estimate decouples from the truth and overshoots the ideal by up to 21%. We further introduce a matched-cost "garbage-folding" negative control that carries no usable signal yet reports a larger apparent improvement than genuine folding - showing that the magnitude of an improvement is not evidence of its correctness - alongside a zero-cost check flagging the artefact from data a benchmark already holds. We distil both into a short reporting checklist for ZNE benchmarks.

Here we present PCPOP, a Julia package for polynomial optimization that supports non-commutative optimization, tracial polynomial optimization, trace polynomial optimization and state polynomial optimization. PCPOP fully supports exact arithmetic computations and incorporates convenient functionalities such as algebraic reductions based on Gröbner basis methods, automatized symmetrization via Wedderburn decompositions, and Jordan algebra reductions. As a distinguished feature, PCPOP implements a specialized framework for polynomial computations in partially commutative variables that provides significant computational advantages for problems appearing in quantum information.

At one frozen setting, the probabilities observed in a sharp quantum context are indistinguishable, as detector-click statistics, from ordinary probabilities on the atoms of a classical partition. But an actual analyzer usually comes with a calibrated knob: a tangible handle on the apparatus. If the measurement configuration is co-varied continuously through this physical parameter, the operational object is no longer one point of a simplex but a response curve. Classical linear responses, Malus-type Hilbert-space responses, softmax links, non-homomorphic parameter transcriptions, and discontinuous threshold limits are different maps from settings to probabilities. Continuity, calibration, and preservation of the physical composition law are then part of the experimental meaning of the knob. Such comparisons distinguish specified, calibrated response models; by themselves they do not constitute a classical-versus-quantum impossibility theorem. The static operational coincidence can also persist for two intertwined contexts: if the common outcomes receive the same probabilities, the remaining masses can always be coupled by a classical joint distribution. Genuine multi-context nonclassicality begins when a family of local shadows cannot be glued into one nonnegative global distribution or one simplex factorization. Farkas' lemma gives the exact alternative: either the classical extension exists, or a separating linear inequality certifies its impossibility.

Continuous time crystals (CTCs) represent a nonequilibrium quantum phase that spontaneously breaks time-translation symmetry without periodic external driving, manifesting as persistent, long-lived oscillations under steady pumping. The lifetime is constrained by the instability of the limit cycle phase, balanced between nonlinear feedback and energy dissipation, which have rarely been studied in experiments before. Here, we report an observation of an ultralong-lived Rydberg-atom CTC in a driven-dissipative many-body atomic system. By harnessing long-range interactions and engineering a dissipative environment that stabilizes the limit-cycle dynamics, we suppress heating and decay effects that typically destroy time-crystalline order. The key factor underlying the ultralong-lived CTC is the closing of the Liouvillian gap and the near-zero real part of the system's Liouvillian eigenspectrum. Through systematic optimization, we achieve an oscillatory lifetime exceeding 16.95 hours-orders of magnitude longer than previous CTC realizations. Our work establishes a robust platform for exploring long-lived autonomous nonequilibrium phases and paves the way for applications in quantum sensing and continuous-time quantum information processing.

We build a framework for the thermodynamics of macroscopic quantum systems. In contrast with approaches requiring access to the full density matrix, our framework relies on a coarse-grained description, based on measurement statistics of a few observables. When these observables commute, the outcomes define classical macrostates whose entropy is quantified by observational entropy, accounting for uncertainty about both the macrostate and the microstate within it. We extend this notion to non-commuting observables forming a subalgebra of the operator space, and use Jaynes' principle to define an algebra-dependent entropy interpolating between von Neumann and observational entropies. Given initial and final measurement sets, connected by internal and/or environment-induced dynamics, we derive a second law for the coarse-grained dynamics. Unlike formulations based on von Neumann entropy, our inequality captures irreversibility from both non-unitary environment-induced dynamics and internal equilibration. It takes the usual form of a positive entropy production when the system is initially at internal equilibrium, while correction terms capture nonequilibrium resources ignored by the coarse-graining. We also derive fluctuation theorems for coarse-grained thermodynamic quantities. Along a quasi-static path of measurement schemes, we identify quantum macroscopic notions of work and heat fulfilling the first and second laws, including an additional work contribution from manipulating the algebra to which the system is confined, through external constraints or quantum measurement backaction. Finally, we apply our framework to examples illustrating the impact of varying the coarse-graining scheme. Our approach unifies macroscopic and stochastic thermodynamics in a genuinely quantum framework, laying the basis for a versatile, experimentally friendly toolbox to analyze complex quantum dynamics.

We present a quantum mechanical model of dual parallel Mach Zehnder electro-optic modulators (DPMZMs), derived using the quantum scattering formalism [1,2]. We obtain the complete, unrestricted transformation of single-photon and coherent input states through the DPMZM architecture, including independent radiofrequency drive conditions on each internal modulator, and recover the symmetric push pull operating point as an exact special case. We numerically evaluate the resulting frequency comb structure, quantify the device sensitivity to bias error, modulation-index imbalance, and RF noise, and use the model to design a DP-MZM-based frequency-bin qubit source, reporting its fidelity as a function of these impairments.

Quantum identity authentication (QIA) has emerged as a crucial technology for secure communication systems, particularly in the burgeoning era of quantum communications. This paper proposes a novel QIA protocol based on non-classical characteristics of squeezed light fields. By exploiting quantum noise reduction properties of quadrature squeezed coherent states, the protocol fundamentally thwarts eavesdropping attempts by Heisenberg-limited uncertainty constraints. The fidelity parameter for decoy states is utilized to detect spoofing attacks, and the dynamic key update mechanism fundamentally eliminates vulnerabilities caused by key reuse. Security information ratio analysis shows that the protocol is able to resist Gaussian-cloner attacks and detect eavesdropping. Moreover, the security threshold can be further enhanced with higher squeezing, allowing tunable protection levels adaptable to different threat scenarios. Compared with binary-squeezed protocols, our proposed four-direction (quaternary-dimensional) squeezing halves the eavesdropper's guessing probability and enlarges the fidelity gap by 29% tightening the discrimination threshold without relying on extra hardware, thus facilitating practical implementation.

High Performance Computing (HPC) centers are expanding to integrate quantum resources, enabling hybrid quantum-classical workflows for complex optimization. Integrating quantum processing units (QPUs) into workload managers poses an orchestration challenge: a remote QPU introduces a second queue - a "two-queue problem" - alongside the scheduler's own. We present Fluence, a Kubernetes scheduler plugin backed by the Fluxion graph-based scheduler, enabling gang-scheduled placement for quantum-classical workloads and custom resources. First, under contention, Fluence's atomic gang placement eliminates the node-time a default scheduler wastes on partially placed gangs. Second, a synchronization primitive gates consumers behind a single producer's shared quantum task, cutting worker idle time roughly 1.2-12x under short queues and orders of magnitude under long ones. Third, policy-aware backend selection cuts mean per-run cost roughly 72x and time-to-result from hours to under two minutes. Together, these results show that quantum-awareness can be added to a cloud-native scheduler without modifying user containers.

The Quantum Approximate Optimization Algorithm (QAOA) is a promising framework for combinatorial optimization, but constrained problems are commonly handled using energetic penalty terms that require calibration and allow infeasible configurations to remain dynamically accessible. We develop a constraint-preserving QAOA framework for personnel rostering in which hard scheduling constraints are embedded directly into the mixer Hamiltonian. Using a binary rostering model with daily coverage and no-consecutive-duty constraints, we formulate the dynamics from a transition-graph perspective and introduce a guarded-XY mixer that confines the evolution to the fully feasible scheduling manifold. We further distinguish feasibility preservation from feasible-transition design and propose a tight-pattern extension that introduces collective feasible exchanges in saturated workload segments where local guarded exchanges alone are insufficient. Exact statevector simulations demonstrate that, compared with Penalty-X and Coverage-XY formulations under both expectation-value and Conditional Value-at-Risk optimization, the proposed approach eliminates hard-constraint penalty calibration, guarantees feasible evolution by construction, and consistently yields higher-quality output distributions with stronger concentration on optimal feasible schedules. To the best of our knowledge, this is the first constraint-preserving QAOA formulation for personnel rostering, and the transition-graph framework is readily applicable to a broad class of constrained quantum optimization problems.