Quantum Rare-Event Estimation for Ising Graphical Models with Belief-Propagation State Preparation

Convergence monitoring of quantum Gibbs samplers

This work proposes a low-cost criterion for convergence monitoring that exploits the weak measurements inherent in quantum Gibbs samplers and their qubit-efficient variants and constructs a Hamiltonian-agnostic stopping criterion based solely on data already generated by the sampler.

Robust Hamiltonian estimation with partial prior information via random unsharp Pauli measurements

Approximate sampling from decoded quantum interferometry via Markov chain Monte Carlo methods

Optimization problems are among the leading candidates for industrially relevant quantum advantage. Decoded quantum interferometry (DQI) has been proposed to tackle approximate optimization, establishing a connection to classical decoding problems. While previous work has primarily focused on the theoretical complexity of DQI, comparatively little is known about its empirical performance relative to classical algorithms. In this work, we shed further light on the complexity of DQI and investigate numerically whether classical sampling methods can emulate the optimization capabilities of DQI. We first present a simplified analytical characterization of DQI that connects its expected performance to binomial statistics, and we identify concrete obstacles in further studying the complexity of DQI. Exploiting the fact that DQI output probabilities are efficiently computable, we apply Markov chain Monte Carlo (MCMC) techniques, particularly block-Gibbs sampling, to sample from the induced distribution. We study the runtime scaling of these methods for two optimization problems called max-XORSAT, where we reach beyond $1000$ effective qubits; and OPI, where we reach beyond $150$ effective qubits. Our results show that MCMC algorithms can reliably attain the approximation ratios expected from DQI across a broad range of problem sizes. In OPI, in the regime where a super-polynomial advantage is claimed for DQI, we observe an empirical runtime for MCMC that scales approximately as $1.1^{n}$, indicating exponential growth with a comparatively small base. Our findings do not refute existing quantum advantage claims but provide new empirical evidence that classical sampling algorithms can closely match DQI's optimization performance, offering a more nuanced perspective on the practical advantage of DQI.

Reliable Sample-Level Quantum Error Mitigation via Dominance-Aware Clustering

Many quantum algorithms for classically difficult optimization tasks must return high-quality bitstrings from finitely many circuit executions, whereas most quantum error-mitigation methods target expectation values. We study sample-level recovery when measured probability mass is distributed around multiple latent bitstrings, called centers. Each component of the measured probability mass is called a source and we assume that each center is associated with one source. We identify dominance-at every coordinate, more than half of a retained region's probability mass comes from one source and agrees with its center-as a sufficient condition under which majority voting recovers that center with exponentially decreasing error probability. We show that nearest-center assignment, as used in clustering algorithms such as the $k$-modes algorithm, can fail to produce dominated regions even when the true centers are known. This failure motivates responsibility thresholding and a local dominance screen, whose combination we call dominance-aware (DA) refinement. Synthetic and simulated MaxCut-QAOA experiments show that DA refinement favors precision, while $k$-modes with DA refinement improves overall center recovery. All procedures are classical post-processing and require no additional quantum-circuit executions.

Quantum Error Mitigation with Diffusion-Like Models

Coupling between a quantum system and its environment causes decoherence by transferring information from the system to environmental degrees of freedom. When discretized in time, such interactions can be interpreted as sequences of weak measurements that provide an effective model of noisy quantum dynamics. Motivated by this picture, we propose an AI-assisted error-mitigation framework for quantum diffusion processes generated by sequential local weak measurements. The forward process progressively erases information from the input state through weak measurements performed in randomly selected Pauli bases, producing basis-dependent local dephasing and locally depolarizing dynamics on average. Machine-learning models are trained on exact synthetic density matrices to learn a channel- and distribution-specific denoising map and estimate the corresponding pre-noise state. We benchmark the approach on single-qubit states and separable and entangled multi-qubit registers. We also study distribution-dependent local-to-global reconstruction, in which local reduced density matrices are used to reconstruct the global state. This experimentally motivated setting relies on locally accessible information and is therefore compatible with noisy and distributed quantum systems. More broadly, the framework provides a hybrid classical-quantum approach for approximating non-unitary dynamics and mitigating coherence loss.

An Architecture-Conditional Framework for Relative-Entropy Event Timing, Quantum Records, and Modular Recovery

The quantum measurement problem separates into operational questions: which observables are stable records, how outcome probabilities are represented, how conditional post-event states are updated, and how a detector event time is assigned. We give a compact architecture-conditional framework. In a finite-dimensional detector model, the detector-side relative-entropy flux is differentiated with the exact Fréchet derivative of the matrix logarithm. A noise-regularised timing distribution is defined and, under an explicitly assumed isolated non-degenerate maximum of the calibrated flux, Laplace asymptotics proves concentration at that maximum. Under stated fixed-point and detailed-balance hypotheses, the centre of the fixed-point algebra gives a canonical commutative record algebra. Outcome probabilities admit a POVM representation, and conditional updates use a completely positive (CP) instrument in its standard sense: CP maps whose traces give probabilities and whose normalised outputs give post-event states, summing to a trace-preserving map. Separately, an assumed modular-invariant inclusion of von Neumann algebras admits a state-preserving conditional expectation and CP retraction. A conditional quantum-error-correction lemma bounds accumulated record failure. These results do not derive unique outcomes from unitarity, construct a black-hole algebra inclusion, or resolve the black-hole information problem; they give a conditional framework, a worked illustration, and testable timing and record-stability criteria.