quant-ph digest — 2026-05-06

Generated 2026-05-06 · 117 entries scored · 15 relevant

Scored against Yuan's research programme (Y1–Y6):

Source

arXiv listing: https://arxiv.org/list/quant-ph/new (86 new + 31 cross = 117 entries)

Coverage: all 117 entries scored. 15 relevant (score ≥ 1); 102 SKIP (score 0, omitted).

Scoring rubric

0–10 on method/scope/conclusion overlap — max wins. HIGH 8–10 · MED 5–7 · LOW 1–4 · SKIP 0.

Highly relevant (score 8–10) — 3 papers

A Quantum Approach to Stochastic Optimization in Insurance Underwriting

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The presence of stochastic elements in combinatorial optimization problems makes them particularly challenging, as such problems quickly become intractable for classical computers even at relatively small sizes. In this work, we propose a novel quantum-classical hybrid scheme for solving a class of stochastic optimization problems known as chance-constrained knapsack problems, in which item weights follow probability distributions and constraints may be violated within a specified risk tolerance. Our method employs knapsack-specific QAOA-based circuits to generate samples which, when combined with a self-consistent classical recovery scheme introduced in this work, produce high-quality solutions. Experiments carried out on IBM Heron processors, using circuits with depths up to 177 and comprising 3443 gates acting on as many as 150 qubits, yield solutions that indicate improvement over classical optimization schemes.

Constraint-Preserving XY-Mixers under Trotterized Adiabatic Evolution

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Constraint handling is a central challenge for quantum algorithms applied to combinatorial optimization. Standard penalty-based approaches increase problem size, distort energy landscapes, and often degrade performance. Constraint-preserving mixers, such as XY-mixers, restrict quantum evolution to feasible subspaces, but their implementation on gate-based hardware requires Trotterization, which introduces approximation errors. In this work, we systematically investigate the interplay between constraint-preserving XY-mixers and Trotterized Adiabatic Evolution (TAE)… For problems with a single global equality constraint spanning all variables, Trotter errors significantly impair XY-mixer performance, making standard Pauli-X mixers more robust under realistic implementations. In contrast, for problems whose constraints decompose into multiple disjoint local blocks, XY-mixers outperform X-mixers by several orders of magnitude even under Trotterized evolution.

Structured Parameterization and Non-Stabilizerness in Hypergraph QAOA

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The quantum approximate optimization algorithm (QAOA) has emerged as a promising candidate for demonstrating quantum advantage on noisy intermediate-scale quantum (NISQ) devices. While various QAOA parameterization schemes exist, ranging from the original single-angle approach to the more expressive multi-angle quantum approximate optimization algorithm (MA-QAOA) and automorphic-angle quantum approximate optimization algorithm (AA-QAOA), each presents distinct trade-offs between expressiveness and classical optimization complexity. In this work, we introduce the k-interaction-angle quantum approximate optimization algorithm (kA-QAOA), a parameterization scheme that groups cost function terms by their k-body interaction order, providing a natural middle ground between parameter efficiency and solution quality.

Moderately relevant (score 5–7) — 6 papers

Quantum Tilted Loss in Variational Optimization: Theory and Applications

Variational quantum algorithms (VQAs) are leading strategies for using near-term quantum devices, with a well-studied bottleneck being their trainability. Standard expectation-value objectives with expressive circuits frequently encounter barren plateaus in the optimization landscape during training. To address this challenge, we introduce the Quantum Tilted Loss (QTL), an operator-level generalization of classical exponential tilting designed to systematically reshape the optimization landscape. By tuning a single continuous parameter, QTL can amplify gradient signals in structured settings while preserving the problem's true global minima. We provide a theoretical foundation that unifies standard expectation minimization with popular tunable heuristics, such as Conditional Value-at-Risk (CVaR) and Gibbs formulations.

Many Hamiltonians Are Sparsifiable

We study the problem of Hamiltonian sparsification: given a parameter ε ∈ (0,1) and an n-qubit Hamiltonian H which is the sum of r-local positive semi-definite (PSD) terms H_1, …, H_m, our goal is to compute a sparse set L ⊆ [m], along with weights w: L → ℝ_{≥0} such that for every state |ψ⟩, Σ w(i) ⟨ψ|H_i|ψ⟩ ∈ (1 ± ε) Σ ⟨ψ|H_i|ψ⟩. We show that many Hamiltonians indeed are sparsifiable to a number of terms much smaller than n^r, including: (a) Hamiltonians where each term is an r-local Pauli string, (b) Hamiltonians where each term is an r-local random operator of rank R, for R ≥ 2^{r-1}+1, and (c) Hamiltonians where each term is an arbitrary r-local operator of rank ≥ 2^r −1 (Quantum SAT). Our results find applications to better (semi-)streaming algorithms for quantum Max-Cut.

Sample-Based Quantum Diagonalization with Amplitude Amplification

Sample-based quantum diagonalization (SQD) has emerged as a promising approach to compute ground and excited states. The method classically diagonalizes a Hamiltonian in a subspace spanned by samples obtained from a quantum computer. However, SQD suffers from a fundamental sampling problem, as some basis states required for a targeted accuracy may only be sampled extremely rarely. To alleviate this, we introduce SQD-AA, which combines SQD with amplitude amplification. SQD-AA uses AA to sequentially reduce probabilities of already measured bitstrings, making the observation of new ones more likely. We observe a reduction in total query complexity of more than a factor 100 for algebraically and exponentially decaying model distributions, and analytically show a quadratic advantage for the latter.

Accelerating Noisy Variational Quantum Algorithms with Physics-Informed Denoising Networks

Variational quantum algorithms are promising for near-term quantum computing, but are severely limited by hardware noise and the substantial circuit overhead required for error mitigation methods such as Zero-Noise Extrapolation (ZNE). We propose a Physics-Informed Denoising Network (PIDN) that reduces the cost of ZNE by learning a surrogate model of its optimization dynamics… We benchmark on QAOA for 3-regular graphs, Sherrington-Kirkpatrick, and transverse-field Ising models, as well as VQE for LiH, BeH₂ and H₂O. Across all tasks, PIDN attains performance comparable to ZNE, while reducing the number of circuit executions by a factor of approximately 4 to 6.

Impact-Driven Quantum Decomposition for Traffic Zone Partitioning: A Hybrid Gate-Model Framework

Partitioning transportation networks into balanced and spatially coherent traffic zones is a fundamental yet computationally challenging task. The resulting optimization problem can be formulated as a Quadratic Unconstrained Binary Optimization (QUBO) model… This paper proposes an impact-driven hybrid quantum-classical optimization framework that bridges transportation-scale optimization models and practical gate-based quantum processors. Instead of static geographic decomposition, the method estimates the energy impact of decision variables and selectively assigns quantum computation to influential subproblems while a classical coordination loop maintains global feasibility. The framework is implemented using the Iskay optimizer and evaluated on the IBM Quantum System One backend.

Universality of Quantum Gates in Particle and Symmetry Constrained Subspaces

Simulating physical systems on near-term quantum computers often requires preparing states within constrained subspaces, like those with fixed particle number or spin. We use Lie algebraic techniques to prove that hardware-efficient gates are universal for state preparation in these subspaces. The key mechanism is Pauli Z dressing: commutators of overlapping gates produce Pauli Z operators on shared qubits, acting as spectator projectors that decompose multi-plane rotations into single-plane generators spanning the full so(w) algebra, where w is the dimension of the constrained subspace, thereby guaranteeing universality for real state preparation. Adding independent complex phases extends this to su(w).

Tangential (score 1–4) — 6 papers

Summary table

ScorearXiv IDShort titleOverlapsarXiv
92605.01169QAOA chance-constrained knapsack on IBM Heron (150 qubits)Y2, Y3, Y4, Y6link
92605.02465Constraint-preserving XY-mixers under TAEY2, Y3, Y4link
82605.01620kA-QAOA: interaction-order parameterization on hypergraphsY1, Y2, Y3link
62605.02850Quantum Tilted Loss (CVaR/Gibbs generalization)Y2, Y3link
62605.02211Many Hamiltonians Are SparsifiableY5link
62605.02066PIDN denoising for VQA (QAOA on 3-regular, SK, TFIM)Y1, Y3link
62605.01127Impact-driven QUBO decomposition for traffic zonesY3, Y4link
52605.02565Sample-Based Quantum Diagonalization with Amplitude AmplificationY4link
52605.00979Universality of gates in constrained subspacesY2link
32605.01438Spectral minimax SDP for direct fidelity estimationY5link
32605.01319Barren plateaus as destructive interference (TFIM)Y2, Y3link
32605.02494Critical assessment of SQD on Heisenberg/HubbardY4link
22605.02051Sub-cubic Solovay-Kitaev with Gibbs-sampled commutatorsY5link
22605.00981Minimal triangle-network Bell nonlocalityY6link
12605.02877Strong quantum Markov properties of Gibbs statesY5link

Source: arXiv quant-ph new submissions · scored against Y1–Y6