Papers

A central promise of useful quantum advantage is the ability to compute ground states of Hamiltonian systems beyond the reach of classical simulation methods. Here we demonstrate that this problem can be effectively amortized across an arbitrary and universal set of Hamiltonians by a foundation model with $\sim0.5$B variational parameters, trained with contemporary techniques from large language models and deep reinforcement learning. To do this, we formulate $\text{spin-}1/2$ quantum ground-state learning as manifold variational optimisation over centrally odd scalar functions on $\mathrm{SU}(2)^N$. This replaces explicit Hilbert-space vector amplitudes with manifold functions on which the Hamiltonian acts through Lie derivatives, evaluated by custom automatic differentiation primitives. We prove that the resulting variational principle on this manifold preserves the $\text{spin-}1/2$ sector's ground-state upper bound using the Peter-Weyl theorem and justify the choice of such a representation with a no-go theorem for pure state foundation NQS. We then pre-train our foundation model on a dataset of hundreds of thousands of different Hamiltonian systems, varying the connection topology, system size, interaction types and strengths, bringing together a century of many-body literature. Using a novel $\mathrm{SU}(2)$ replica-exchange Langevin sampler and sharded natural-gradient optimisation, we train our model with our own extension of the Kronecker-Factored Approximate Curvature (KFAC) optimiser on system sizes up to 64 qubits. On a held-out generalisation dataset, we fine-tune our model on system sizes of up to 1024 qubits, and evaluate on systems up to 8100 qubits.

Peaked circuits are random quantum circuits whose measurement returns one bitstring far more often than chance. The probability of that string is the peakedness, and a verifier who knows the string checks the device in a few shots. Peaked circuits are a candidate route to verifiable quantum advantage, and classical generation is the bottleneck. Aaronson and Zhang train a variational circuit by gradient descent, and the peakedness they reach decays exponentially with the number of qubits. They state that either their optimizer stalls or no efficient generation method exists. Here we show that no bare fixed base fits the decay we measure and that neither the barren plateau nor fragmentation of the solution set explains it. We measure the optimization landscape on eighteen instances per size, from eight to sixteen qubits, under a protocol whose falsifiers were fixed in advance. The decay steepens as the qubit number grows, leaving extrapolations to larger devices unsupported. A better optimizer exists, but it gains a few percent and the peakedness it reaches decays at nearly the same rate. Aaronson and Zhang attribute the difficulty to a barren plateau. The plateau is present, but the second-order amplitude data are depth-independent while the attained peakedness is not. Independent runs land on uncorrelated solutions, yet paths connecting them stay well above the scale of a random state, so fragmentation fails at that scale. In the deep limit we take the scrambled state Haar-random. There we prove that no search over a polynomial-parameter Lipschitz family, exhaustive search included, beats the scale of a random state by more than a poly(n) factor on average. Both branches of the Aaronson-Zhang alternative therefore stay open. A generation method must beat a preregistered baseline, and a hardness argument must accommodate a solution set connected at the scale of a random state.

Quantum generative adversarial networks (QGANs) have attracted increasing attention for image generation using parameterized quantum circuits. Existing amplitude-based approaches face two key limitations: pixel locations are typically encoded by computational-basis indices or address qubits, causing quantum resources to grow with image resolution; meanwhile, jointly decoding many pixels from normalized quantum states introduces probability competition among pixels and limits precise pixel-wise control. To address these issues, we reformulate quantum image generation as coordinate-conditioned implicit function learning. Our method takes spatial coordinates and latent variables as inputs, uses a classical embedding network to generate input-dependent circuit parameters, and evaluates a variational quantum circuit at each coordinate. Pixel intensities are directly obtained from the expectation value of a dedicated color qubit, and a complete image is generated by querying all spatial coordinates. This design decouples image resolution from address-qubit requirements and avoids shared probability-normalization constraints across pixels. We further design a specialized variational quantum circuit to provide structural inductive bias for coordinate-conditioned generation. Simulated experiments on two benchmark datasets show that our method outperforms FRQI-based generation and PQWGAN in visual and quantitative quality while using fewer qubits, and also achieves better generation quality than the corresponding classical baseline.

Coherences between different energy levels are strongly constrained by thermodynamics. Here we ask a related question: if several coherence transfers can be optimized separately, can they also be optimized simultaneously by the same thermodynamic process? We show that this is in general a compatibility problem. Using an energy-resolved Stinespring representation of thermal operations, we express elementary coherence-transfer amplitudes as inner products of bath-weighted vectors associated with total-energy shells. Saturation of the corresponding Cauchy–Schwarz bounds requires these vectors to be collinear. Since all transfers have to arise from the same energy-preserving system–bath unitary, such saturation conditions cannot in general be imposed independently. Trace and Gibbs preservation lead to additional phase-closure conditions and to polygon inequalities that can rule out simultaneous saturation for fixed population-transfer data. We complement these analytic results with a rigorous computer-assisted semidefinite-program analysis. For the mixed-degenerate Hamiltonian \(H=\operatorname{diag}(0,1,1,3)\), machine-certified primal and dual bounds show that no covariant Gibbs-preserving channel can simultaneously attain three individually optimal directional coherence transfers. Since every thermal operation is covariant and Gibbs preserving, the same triple of \(\CGP\)-optimal values cannot be attained by a thermal operation. We also analyze auxiliary Choi-sector Gram matrices for mixed-degenerate ladders. Under specified directional rank assumptions, positivity imposes range-inclusion conditions, from which we obtain conditional non-genericity statements for physical joint saturation.

Informational completeness (IC) guarantees that an inverse exists, not that it is statistically well conditioned. For minimal rank-one Weyl–Heisenberg (WH) measurements, covariance makes the nonidentity projector-Gram spectrum proportional to the fiducial's ambiguity intensities, with eigenvalues \(d|\chi_\phi(u)|^2\), turning stability into an explicit worst-direction design problem; write \(\lambda\) for its smallest nonidentity eigenvalue. Haar fiducials are IC almost surely while \(\mathbb E[\lambda^{-1}]=\infty\), and an explicit geometric family used to establish balanced informationally complete measurements in every dimension has a normalized spectral floor bounded above by an exponentially decaying envelope. We then construct a hierarchy of minimal measurements. A cyclic family with exactly \(d^2\) outcomes in every integer dimension has floors \(\Theta(d^{-3})\) for odd \(d\) and \(\Theta(d^{-5})\) for even \(d\); a finite-field family for \(q=2^m\) obeys the uniform bound \(\lambda\ge4/9\). Our main result treats every prime-power dimension of characteristic \(p\ge5\). A balanced one-coordinate perturbation repairs the zero ambiguity axis of a cubic Alltop state, gives an attained floor uniformly bounded below by a positive constant, and confines the entire nonidentity spectrum to \([L_q,U_q]\) with \(U_q/L_q\to1\). Its SIC-normalized minimum tends to one, and \(\lambda(\phi_q)/\Lambda_q^\star\to1\) for the global finite-field WH max–min optimum \(\Lambda_q^\star\), without assuming SIC existence. The complete spectrum determines the exact finite-sample Hilbert–Schmidt error of canonical linear inversion at \(I/d\), while its lower edge controls local Fisher efficiency and canonical-shadow bounds.

We present a rigorous strong-coupling Birman-Schwinger analysis of the three-boson lattice Schroedinger operator on Z^2 at the exceptional quasimomentum K = pi. First, we provide an exact closed-form benchmark for the fiber Fredholm determinant, valid for every quasimomentum K, obtained via a uniform elliptic reduction. Second, we formulate and prove a general order-matching criterion determining whether a leading-order Fredholm determinant asymptotic, with relative error O(1/mu), suffices to fix the constant-order additive energy correction, or if the next-order refinement is required. Applying the criterion to the formal branch z = -2mu + d, we identify an algebraic crossing -2mu + 6 + 8/mu + O(mu^{-2}), but demonstrate that this crossing does not correspond to a true eigenvalue of the full Hamiltonian. The actual ground state obeys the rigorous variational bounds -3mu <= z_1^{pi,s}(mu) <= -3mu + 6, so that z_1^{pi,s}(mu) = -3mu + O(1), the same leading branch as at K = 0; direct finite-volume diagonalisation confirms the refined asymptotic -3mu + 6 + O(mu^{-1}) and spectral gap 2mu - 2 + O(mu^{-1}). We independently confirm that the known K = 0 constant C approximately 3.96458 requires no analogous refinement. Finally, we compare the asymptotic precision levels achieved across recent lattice few-body models and draw a structural parallel with the parity-based classification of topological band insulators at time-reversal-invariant momenta.

Quantum field theory allows local violations of the classical energy conditions, such as regions of negative energy density. The Ford-Roman quantum inequalities quantify how negative these regions can be and for how long. We present a derivation of these inequalities from the second law of thermodynamics, in an operational framework that couples the field to a small thermal detector. The detector acts as a physical probe of the field's energy density and, by the non-negativity of the entanglement entropy between the two systems, one can show that its heat loss is bounded from below by a state-independent quantity.

Continuous-variable (CV) quantum states are naturally characterized by their moments, defined as expectation values of products of single- or multimode ladder operators. Many CV Hamiltonians and quantum algorithms are formulated directly in terms of these moments, and therefore an efficient procedure to estimate moments with limited state measurements is necessary. In this paper, we present a protocol for shadow tomography of moment-generating functions (MGFs) of CV states based on randomized homodyne and heterodyne measurements. The resulting shadows enable an efficient and concurrent estimation of many multimode moments. Our complexity analysis shows that the number of measurements required to estimate a certain moment to a given precision grows exponentially in the order of the moments. Finally, we assess the precision of these moment estimators by applying them to two tasks: detecting entanglement in both Gaussian and non-Gaussian states via the Shchukin-Vogel protocol, and characterizing optical loss in a photonic chip. We demonstrate that both tasks can be accomplished with only a few thousand randomized measurements. The small number of required measurements, combined with efficient sample processing, makes our protocol applicable to a wide range of CV simulation tasks.

Unconventional photon blockade (UPB) provides an attractive route for generating antibunched light through quantum interference without requiring strong nonlinear coupling. In conventional Markovian systems, irreversible dissipation progressively destroys the phase coherence, even limiting the achievable single-photon purity. Here we propose a scheme for imposing non-Markovian effects on a microwave cavity where a Er$^{3+}$:Y$_2$SiO$_5$ crystal loaded into a hybrid optical-microwave system. Based on the time-convolutionless non-Markovian framework, we derive the time-dependent non-Markovian decay rate and the renormalized microwave drive in the weak-coupling regime. We show that the second-order correlation function is significantly suppressed, even to the order of $10^{-7}$, since the backflow of environmental information enhances photon blocking. In addition, this structured reservoir engineering not only provides an additional degree of freedom, but also further optimizes the blockade via optical detuning. Importantly, the delayed second-order correlation exhibits a broadened antibunching profile, thereby relaxing the timing requirements for single-photon synchronization. Our results demonstrate that environmental non-Markovianity can be transformed from a source of decoherence into a controllable quantum resource, opening new opportunities for high-purity single-photon generation.

Effective implementation of high-level quantum gates is essential for practical quantum computing. In this work, we presented, to the best of our knowledge, the first reported 12-CNOT decomposition of the double qubit excitation operator. We compared our new circuit with the previous SOTA 13-CNOT circuits in 4 different metrics. Our new circuit has the lowest CNOT count (12), lowest CNOT depth (8, roughly 27% reduction), and lowest total circuit depth (15, 25% reduction) among all the previous SOTA circuits. With output qubit relabeling, the CNOT depth can be further reduced to 7 (roughly 36% reduction from 11). Further, we only added 2 extra 1q gates (from 11 to 13) compared to the best of the SOTA circuits. As the double qubit excitation operator can be used as a building block hundreds or thousands of times in practical quantum algorithms, any reduction in such primitives compounds over the full circuit, resulting in significant overall resource savings.

We formulate a finite-proper-time (FPT) construction for real scalar $\lambda\phi^4$ theory in which a non-zero lower endpoint $s_0$ is retained as physical spectral data for complete virtual histories. Functional differentiation partitions a pre-existing history, while interaction sewing creates closed momentum circulations. Local momentum conservation identifies the primitive closed circulations with graph circuits; applying the retained endpoint condition gives \[ s_e\ge0, \qquad \sum_{e\in c}s_e\ge s_0 \quad(c\in\mathcal{C}(G)). \] An individual edge or bridge may be arbitrarily short, but no complete closed circulation may collapse below $s_0$. This routing-independent prescription differs from damping every propagator. We prove that it gives a positive loop quadratic form and ultraviolet-finite massive amplitudes, including overlapping short-distance regions. Under a physical cut, precisely the circuit conditions crossed by the cut disappear, leaving the independently constructed daughter domains together with the ordinary pole residues and positive scalar phase space. At fixed $s_0$ we give an all-order local construction; for $\lambda\ge0$, $\mathcal{K}_{\bar{\phi}}\ge-\partial^2+m^2$ gives a background-uniform heat-kernel bound. The open Euclidean history kernel is virtual spectral machinery rather than the physical propagator, and physical positivity is imposed on complete boundary amplitudes. After local matching, the first non-constant one-loop on-shell correction is proportional to $s_0^2(s^2+t^2+u^2)$, providing a correlated observable test of the retained scale.

Transversal CNOT (TCNOT) gates can accelerate fault-tolerant quantum computation (FTQC) in the surface code by reducing the number of syndrome extraction rounds required between logical operations from $O(d)$ to $O(1)$. This is particularly attractive for quantum platforms with long-range connectivity, such as neutral atoms. However, dense TCNOT schedules substantially increase the classical decoding workload. TCNOTs propagate errors across multiple surface-code patches, enlarging the spatiotemporal region that must be decoded jointly. Consequently, denser TCNOT schedules increase decoding latency and memory requirements and potentially exceed available decoder capacity. Moreover, because the detector error model (DEM) of each decoding window depends on the TCNOT schedule, exhaustively precomputing all possible window-level DEMs is infeasible, requiring just-in-time (JIT) DEM compilation. Thus, the practical benefit of TCNOT gates is limited not only by quantum hardware performance but also by classical decoding and DEM-compilation capacity. We introduce PACE, a decoder-aware scheduling framework for TCNOT-based FTQC. PACE first mitigates the decoder-side costs of aggressive TCNOT scheduling through three complementary techniques. Hybrid Window Decoding assigns different decoders for each decoding window according to its DEM structure. DEM Stitch generates schedule-specific window-level DEMs just in time by assembling reusable precompiled fragments. Sub-window Parallel Decoding decomposes large windows into smaller sub-windows with graph-coloring formulation. Building on these techniques, PACE then performs decoder-aware scheduling to maximize TCNOT concurrency within the available decoder resources. Our evaluation shows the trade-off between quantum acceleration and classical decoding cost, revealing the limitations of current decoding systems for TCNOT-based FTQC.

Telecloning – the combination of quantum teleportation and cloning – offers a powerful mechanism to disseminate unknown quantum states to multiple spatially separated recipients with optimal fidelity. Despite its conceptual importance for quantum networks, an experimental demonstration of symmetric qubit quantum telecloning remains elusive, particularly due to the challenges of generating multipartite entangled resource states and implementing stable multi-photon interference across distributed nodes. Here, we realize the optimal 1 to 2 symmetric quantum telecloning using a scalable silicon photonic platform. We implement a six-photon protocol using two independent, fiber-linked photonic chips: one generating a heralded input state and the other preparing a four-photon entangled resource state. By performing an interchip Bell-state measurement, we successfully distribute the input state into two optimal clones at remote nodes. We observe an interchip cloning fidelity of 78.45 $\pm$ 1.39%, exceeding the classical limit of 2/3 by 8 standard deviations. Our results demonstrate the robust generation and manipulation of complex multi-photon states between integrated chips, providing a foundational building block for large-scale multi-party quantum networks.

We show that a cyclically controlled non-coherence-generating channel supplies a two-parameter family of closed Bloch trajectories, absent in the single-parameter (temperature) cycles of the thermal Uhlmann literature, whose associated Uhlmann phase develops a genuine vortex–antivortex structure on the channel-parameter torus. A qubit prepared in a pure state, which carries no geometric phase of its own, acquires a nontrivial Uhlmann phase, obtained here in closed form, purely from a closed loop in the space of the native quantum channel parameters. The net topological charge of these defects is constrained to zero by the Poincaré–Hopf theorem. As the input-state orientation approaches its critical values, the defects merge and annihilate in pairs following a square-root coalescence law. This defect dynamics drives local geometric transitions, a mechanism that contrasts with single-parameter thermal Uhlmann transitions, where a global quantized winding jumps as a bulk invariant.

Irreversibility and time-translation symmetry are both fundamental in open quantum dynamics, and it is of great importance to study the interplay between them. In this paper, we study tabletop reversibility (TTR) for phase-covariant quantum operations and obtain sharply different conclusions for finite-dimensional and Gaussian systems. For finite-dimensional systems, we prove that any phase-covariant operation that admits a Petz recovery map can be implemented by a dilation which is both time-translational symmetric and tabletop time reversible. In contrast, for phase-covariant Gaussian operations we exhibit a concrete obstruction: any Gaussian dilation of a single-mode Gaussian amplification channel is not tabletop time reversible. As a byproduct, we find that almost every completely positive and trace preserving (CPTP) map admits a dilation that realizes TTR. These results clarify whether and how TTR can be realized under phase-covariant symmetry constraints and reveal a qualitative difference between finite-dimensional and Gaussian systems.

Consider two PAC learning algorithms, both having access to quantum computation, but differing in the types of examples they obtain: one is provided with classical samples, while the other is given quantum samples. Are there any learning tasks that can be efficiently performed by the latter, but not by the former? This question, the focus of our work, is surprisingly still open. Our main result is to show that relative to an oracle, there are distributions that can be efficiently generated by a quantum learner with access to quantum samples, but not by a quantum learner with access to only classical samples, making progress to answering this question in the affirmative.

Quantum computation has traditionally been formulated by postulating abstract information carriers and subsequently identifying physical systems that realize them. We adopt the opposite viewpoint and ask what computational structure is supplied by a fundamental relativistic quantum system itself. Focusing on the simplest nontrivial massive spin carrier, spin-1/2, we show that its relativistic description supplies a native four-dimensional information carrier through the Dirac equation. The resulting Dirac information carrier possesses an intrinsic positive- and negative-energy decomposition that induces a physics-constrained computational structure comprising sector-preserving and sector-coupling quantum logic. Restricting the relativistic computational structure to the positive-energy sector and taking the nonrelativistic regime recovers the familiar Pauli qubit description. We distinguish mathematical unitary transformations from physically admissible gates and show how charge-conjugation structure, charge superselection, and associated reference-frame resources can constrain quantum logic beyond the sector-preserving structure. Finally, under appropriate physical assumptions, we establish the conditions under which this structure supports full single-carrier controllability. Our work establishes the Dirac information carrier as the massive spin-1/2 instance of a broader programme in which computational structures are derived from the relativistic representation carried by the underlying physical system.

Solid state quantum emitters are a leading platform for photonic quantum networking with memory nodes. However, the inhomogeneous distribution of quantum emitters, as well as several environmental factors (i.e. strain and electric fields) spread the frequency spectrum of the qubits, making them distinguishable and therefore not a reliable resource for distributed quantum entanglement. In this paper, we demonstrate a full-stack approach to integrating nearly indistinguishable tin vacancy (SnV$^-$) quantum emitters on a frequency-tunable photonic interposer that overcomes the native distribution and static variation of quantum emitters for an indistinguishable photonic quantum networking platform. We demonstrate a silicon nitride-on-insulator photonic integrated circuit (PIC) with accompanying multiphysics digital twin (MPhDT) that guides discovery of SnV$^-$ strain-tuning parameters and informs construction of a multi-channel quantum repeater node. On this node, we achieve the first simultaneous demonstration of spectral tuning of the zero phonon line (ZPL) at GHz scale; coherent electron spin control with gate times of $<80$ ns; strongly- and weakly-coupled nuclear spin detection; and commercial fiber array-coupled readout of a SnV$^-$ center. Finally, we propose and simulate improvements to the architecture that achieve 99.96% connectivity of $ N \sim 1000$ emitters spanning the inhomogeneous distribution of SnV$^-$ centers in strained diamond, where distributed quantum entanglement may be realized.

Understanding how common dielectric substrates influence the spin transport properties of graphene is essential for advancing graphene-based spintronic technologies. Here we use a comprehensive set of numerical simulations to reveal how a SiO$_2$ substrate modifies the spin texture and governs spin relaxation in graphene. Using first-principles density matrix dynamics simulations, as well as tight-binding (TB) transport simulations, we quantify the effects of electron-phonon scattering, impurity scattering, and electrostatic disorder on the spin relaxation process. We find that a 2D SiO$_2$ substrate induces a predominantly Rashba-type helical spin texture in graphene, leading to a spin lifetime anisotropy of 1/2. Meanwhile, bulk SiO$_2$ breaks in-plane symmetry in graphene, leading to anisotropic in-plane and out-of-plane components in the spin texture, which we capture with a newly-developed TB model of graphene. Transport simulations under realistic disorder conditions reveal a spin lifetime anisotropy between 0.5 and 1, similar to what is seen in measurements of graphene spin valves on a SiO$_2$ substrate. Our results reveal a more complex picture of spin relaxation at the ubiquitous graphene/SiO$_2$ interface, beyond the standard Rashba model, providing critical insight for interpreting experiments and guiding substrate engineering for graphene spintronics.

Hamiltonian simulation is a core quantum workload, underpinning variational quantum algorithms and Trotterized time evolution. Such programs are expressed as Pauli exponential sequences, exhibiting structural patterns that are highly amenable to high-level synthesis and optimization. Existing compilers, however, fail to fully unlock the optimization potential of their global algebraic structure, even when employing advanced graph- or tableau-based methods. We present Symphony, a holistic compilation approach built on the binary symplectic form (BSF) representation of Pauli strings. Unlike prior group-wise BSF simplification and path-based Pauli network synthesis, Symphony applies generalized controlled-Pauli Clifford transformations directly to a global BSF tableau, adaptively reducing active Pauli rows and emitting eligible two-qubit blocks other than single-qubit rotations in a forward Clifford frame. Following algebraic simplification, Symphony performs a causality-preserving block rescheduling heuristic that respects frame-induced dependencies while exposing extensive two-qubit block parallelism opportunities. This streamlined compilation style comprehensively exploits simultaneous simplification and commutativity opportunities, achieving efficient global optimization without relying on computationally expensive heuristics or long-horizon searches. Across the generic Hamiltonian simulation benchmarks in HamLib, Symphony achieves average reductions of 59% in two-qubit gate count and 91% in circuit depth. It strictly Pareto-dominates prior state-of-the-art compilers, requiring 1.14–1.58$\times$ fewer two-qubit gates and especially shrinking two-qubit circuit depth by a substantial factor of 1.87–5.67$\times$ on average.

Quantum-selected configuration interaction (QSCI) methods use a quantum computer to identify dominant electronic configurations in the molecular ground state, while a classical computer diagonalizes the Hamiltonian within the reduced subspace spanned by those configurations. Sample-based quantum diagonalization (SQD), a leading QSCI approach, uses iterative classical post-processing to correct noisy quantum measurement to ensure that the corresponding configurations remain physically sensible. In this work, we show that SQD performance can be strongly influenced by uncontrolled growth of the classical diagonalization subspace. When classical resources are not explicitly constrained, classical uniform random sampling can reproduce SQD benchmarks as noise increases the diversity of sampled configurations. We show any fair benchmarking protocol of SQD must explicitly control diagonalization size over unique samples. We then address the problem of efficiently discovering physically relevant, energy-lowering configurations by introducing a measurement protocol based on non-orthogonal configuration interaction (NOCI). By distributing measurements across orbital bases optimized with respect to the molecular Hamiltonian, we obtain improved sample efficiency relative to measurements performed solely in the Hartree–Fock basis. Importantly, these improvements persist even under fixed classical resource budgets, demonstrating that the resulting configurations are of higher quality rather than being more numerous. Under our proposed benchmarking procedure, we establish measurement-basis engineering as a promising route to improving quantum sampling methods for electronic structure.

Modular arithmetic is central to quantum algorithms for cryptographic problems, including Shor's algorithm and Grover-based cryptanalysis, with modular reduction contributing substantially to circuit cost. Pseudo-Mersenne moduli $q=2^n-c$ allow classical Crandall reduction to replace division with folding and constant arithmetic, providing a structural opportunity for more efficient quantum modular reduction than Barrett reduction. We translate this advantage into a reversible quantum setting by deriving explicit folding and normalization conditions for $2n$-bit inputs. To the best of our knowledge, this constitutes the first exact reversible quantum circuit formulation of Crandall reduction. Based on this formulation, we develop two variants: Crandall reduction-1 is designed to minimize execution cost through one-step normalization, whereas Crandall reduction-2 uses two-step normalization to support a wider range of $c$ with limited overhead. Logical resource estimates show that both variants require fewer qubits and lower T-count and T-depth than optimized folding Barrett reduction. At $n=10$, Crandall reduction-1 reduces both T-count and T-depth by approximately 46.9% relative to optimized folding Barrett reduction. Surface-code analysis further shows that, at $n=20$ under the Sparse Blossom decoder, the estimated runtimes of the two variants are 30.05 ms and 35.39 ms, respectively, compared with 53.77 ms for optimized folding Barrett reduction. These results demonstrate the practical value of exploiting modulus-specific arithmetic structure in fault-tolerant quantum circuit design.

We investigate the exact dynamics of first-order system–bath coherence induced by excitation exchange in bilinear bosonic models. By solving the linear Heisenberg equations, we obtain the exact evolution of system and bath operators and evaluate the first-order coherence between the system mode and the collective bath mode directly coupled to it. We analyze how this coherence depends on initial occupations, coupling strength, spectral width, and detuning, showing that its buildup and oscillatory behavior are closely related to excitation exchange and reservoir memory. We further examine controlled coherence dynamics under leakage-elimination-operator inspired modulation of the system frequency. The results show that random modulation can suppress excitation leakage and maintain finite and relatively stable coherence fluctuations at long times. These results clarify the evolution and control of first-order system–bath coherence in settings where a localized bosonic mode is coupled to a structured bosonic reservoir.

The spontaneous emission factor - often known as the e̱ta} factor - is an important quantity in the description of quantum well lasers, influencing both the threshold power as well as the general shape of the light in-light out (L-L) curve. Past work on modelling multiple quantum well (MQW) nanowire laser devices has typically assumed that the e̱ta} factor is a constant parameter that can either be estimated or fit in a post-hoc manner. However, the e̱ta} factor can be derived from the transitions between valence and conduction bands in semiconductor quantum wells, together with knowledge of the cavity modes. Here we investigate the dynamic nature of the e̱ta} factor for MQW nanowire lasers, and show how it can be computed. We also examine the dependence of the spontaneous emission rate and spontaneous emission factor e̱ta} on the charge carrier density, quantum well thickness, and composition, and discuss the impact on laser threshold and operation.

Message-passing decoders are among the most promising candidates for scalable quantum error correction, yet their behavior in the low-error-rate regime remains poorly understood under realistic circuit-level noise. In this work, we introduce a systematic framework for identifying the graph structures that govern decoder failures and for using them to predict the resulting error floor. We apply exhaustive trapping-set enumeration directly to the detector error model of a bivariate bicycle code and test all low-weight fault configurations supported on the resulting structures. This converts the analysis of extremely rare logical failures into a finite structural search, avoiding the prohibitive cost of direct Monte Carlo simulation. We evaluate the framework on three iterative decoders with substantially different architectures and decoding heuristics. Remarkably, for RelayBP, the resulting prediction accurately reproduces the simulated error floor; for the others, it remains within the same order of magnitude. Despite their differences, leafless elementary trapping sets capture a substantial part of the low-weight error-floor contribution for all three decoders. Moreover, each decoder admits failures caused by fault configurations well below the correction capability implied by the circuit-level distance, revealing a substantial gap between code distance and practical iterative-decoding performance. These results establish trapping-set analysis as a practical framework for predicting error floors, exposing the structural weaknesses of iterative decoders, and guiding the joint design of decoding algorithms.

The proposed formulation establishes a formal correspondence between the Heisenberg representation and the Bayesian formulation of inverse problems. The likelihood function is first expressed in Hilbert space through a quadratic norm of the difference between the noisy observations and the response of a forward model depending on the unknown parameters, weighted by the inverse of the observational noise covariance. Based on the common mathematical structure of this quadratic form and the observable operator in the Heisenberg representation, the inverse observational noise covariance operator is identified with the initial observable. To establish this correspondence, a stochastic field with Matérn covariance is considered and represented through the Karhunen-Loève expansion. The eigenvalues and eigenfunctions of this expansion are then used to define the state $|\Psi\rangle$ within the proposed formalism. The main results are summarized by four theorems: the unitarity of the evolution operator, the self-adjointness of the Hamiltonian, the conservation of the trace of the observable, and the formal correspondence between the Bayesian likelihood and the Heisenberg observable structure. Limiting cases of the Matérn covariance are analyzed. Finally, numerical examples illustrate the analytical results.

Portability in high-performance quantum-circuit simulation need not begin at the kernel. We present QSimAdv, which makes late binding, rather than a common kernel, the basis of vendor independence. Representation, operator lowering, and data placement are bound only when their required inputs become available. Before full-state allocation, circuit, noise, and output inspection can route eligible generic sampled-count requests to a stabiliser tableau; explicitly requested representations remain fixed. For full-state execution, backend constraints shape fusion; an ordered fused operator binds to a native lowering only after its physical targets are known. A first-class logical-to-physical layout map records non-canonical order across local and rank-address bits, so the dispatcher moves nonlocal targets only on demand. GPU, CPU, and Message Passing Interface (MPI) backends share these semantics while retaining native execution paths. We realize this design on NVIDIA GH200 and AMD MI250X/EPYC systems across local and distributed execution. With matched complex 32-bit floating-point state storage, QSimAdv leads both Aer Hopper configurations at $N=32$ and Aer's HIP backend at four shared MI250X sizes from $N=24$ to 30. Strong scaling exposes platform dependence: on setonix, QSimAdv leads both GPU and CPU comparisons at every measured rank, achieving $3.4\times$ and $2.8\times$ speedups, respectively, from one to eight ranks; neither the GH200 path nor the CPU path speeds up at eight ranks. Weak scaling reaches 256 ranks with 2 TiB GPU and 1 TiB CPU states. Together, these results support that portability can reside above the kernel boundary while execution remains native and extends across distributed memory.

Training variational quantum models requires choosing between parameter-shift gradients, which are exact but cost $O(P)$ forward evaluations, and simultaneous perturbation stochastic approximation (SPSA), which uses only two samples but produces high-variance estimates that can degrade optimisation on small supervised tasks. Whether the cheap gradient is usable depends on the variance that results from different choices of the SPSA perturbation scale, learning rate, and gain-decay schedule. We varied those quantities across a broad grid on a 6-qubit, 60-parameter QNLI classifier and compared the best configurations to parameter-shift AdamW and BuresQNG. AdamW-style SPSA with $c_0=0.01$, $\eta=0.10$, $\gamma=0.10$ reached $55\% \pm 11\%$ test accuracy, improving over the default configuration ($49\% \pm 6\%$) but remaining 16-19 percentage points below the parameter-shift baselines because the two-sample SPSA gradient estimate has too much variance for reliable optimisation of 60 parameters in 40 epochs. Classical-gain SPSA and Bures-preconditioned SPSA performed worse, at $51\%$ and $46\%$ respectively. Bures-preconditioning a noisy two-sample SPSA gradient amplifies perturbation noise.

Quantum-geometric speed and dynamical instability are two natural rates for a driven quantum system, and their relation is unsettled even for exactly solvable dynamics. Here we show that for any multimode quadratic bosonic system referred to the bare-mode vacuum the Fubini-Study speed v_FS is the Frobenius norm of the symmetric, stretching part of the flow: the rate at which the vacuum becomes distinguishable from itself measures instantaneous symplectic stretching. That identity turns a classical stability estimate into a quantum-geometric bound, lambda_max <= sqrt(2) v_FS, sharp at every mode number and saturated by a resonant pure squeezer. The bound is not invertible: in a detuned parametric amplifier we hold either rate fixed while varying the other.

Which part of a Hamiltonian spectrum is relevant when the initial state and interpolation preserve multiple symmetries? We study this question for a multi-register encoding of fixed-cardinality Set Cover. Register permutations and incidence automorphisms define a joint action whose orbits are unlabelled candidate covers, with an exact hopping Hamiltonian on the orbit quotient. We distinguish the symmetry-fixed space from the generally smaller common invariant cyclic envelope generated by the initial state and endpoint Hamiltonians; a prescribed trajectory may span less still. We prove a sector-resolved Schur-complement bound on cover-measurement probability that includes the kinetic floor induced by sector projection. For a retained-walk linear path, a general hopping-cancellation point makes the spectrum exactly orbit-resolved. Multiplicity outside the fixed sector is then dynamically dark, while transverse dark crossings satisfy a separate stability theorem. On even cycles the fixed-sector excitation gap at cancellation equals $\lambda(n-1)/(2n-1)$ despite global multiplicity closure. Separately, for the same instances, we construct a distinct Johnson–Metropolis parent path on the injective hard-core space, from a Dicke state to a Gibbs-amplitude state. Its cover-failure probability is $O(n^{-5})$ and its cyclic-envelope gap is at least $1024\,n^{-13}$ uniformly along the path, yielding a conditional polynomial adiabatic runtime in an abstract Hamiltonian-access model. We claim no quantum speedup; the result is a protocol-dependent separation of global, symmetry-fixed, cyclic-envelope, and tracked spectra.