quant-ph digest — 2026-04-24
Scored against Yuan's research programme (Y1–Y6):
- Y1 — arXiv:2502.09704 — iterative warm-started QAOA
- Y2 — arXiv:2304.06915 — quasi-binary portfolio QAOA
- Y3 — arXiv:2410.16265 — QAOA DGMVP portfolio (QST 2026)
- Y4 — arXiv:2603.14744 — Grover + ADMM cardinality-constrained BO
- Y5 — arXiv:2510.08292 — GW speed-ups via Gibbs states + Pauli sparsity
- Y6 — arXiv:2510.11213 — PBR test on IBM Heron2
Source
arXiv listing: https://arxiv.org/list/quant-ph/new (51 new + 19 cross = 70 entries)
Coverage: all 70 entries scored. 16 relevant (score ≥ 1); 54 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) — 4 papers
CVaR-Assisted Custom Penalty Function for Constrained Optimization
- Authors: Xin Wei Lee, Hoong Chuin Lau (Singapore Management University)
- arXiv: 2604.20088
- Category: new submission — Quantum Physics (quant-ph)
- Score: 9/10 (HIGH)
- Overlaps with: Y2 (CVaR-QAOA + hard-constraint philosophy, method), Y4 (constrained binary optimisation, scope), Y3 (landscape-design lever for NISQ-era VQE, method)
- Why it matters: A direct answer to “how to handle inequality constraints in QAOA/VQE without slack-blown QUBOs” — replaces exponential exact-diagonalisation with Monte-Carlo + CVaR, benchmarked at up to 50 qubits on MDKP. This is the most Y2-adjacent paper of the day.
Constrained combinatorial optimization problems are frequently reformulated as quadratic unconstrained binary optimization (QUBO) models in order to leverage emerging quantum optimization algorithms such as the Variational Quantum Eigensolver (VQE) and the Quantum Approximate Optimization Algorithm (QAOA). However, standard QUBO formulations enforce inequality constraints through slack variables and quadratic penalties, which can significantly increase the problem size and distort the optimization landscape. In this work, we propose a slack-free penalty formulation for constrained binary optimization that eliminates auxiliary slack variables and preserves the feasibility structure of the original problem.
Divide-and-Conquer Neural Network Surrogates for Quantum Sampling: Accelerating Markov Chain Monte Carlo in Large-Scale Constrained Optimization Problems
- Authors: Yuya Kawamata, Yuichiro Nakano, Keisuke Fujii (Osaka / Kyoto / RIKEN)
- arXiv: 2604.20701
- Category: new submission — Quantum Physics (quant-ph)
- Score: 9/10 (HIGH)
- Overlaps with: Y1 (QAOA on 3-regular graphs, method), Y2 (XY hard mixer + CVaR-style sample post-processing, method), Y4 (cardinality / Hamming-weight constraint, scope), Y3 (large-scale hybrid classical-quantum MCMC, scope)
- Why it matters: Sits at the triple-intersection of Y1/Y2/Y4. XY-mixer QAOA over small blocks + conditional MADE surrogate + MCMC on 3-regular graphs and MNIST feature-mask (N=784). Delivers 20.3× / 7.6× mixing-rate speedups over Kawasaki dynamics that persist with N.
Sampling problems are promising candidates for demonstrating quantum advantage, and one approach known as quantum-enhanced Markov chain Monte Carlo [Layden, D. et al., Nature 619, 282-287 (2023)] uses quantum samples as a proposal distribution to accelerate convergence to a target distribution. On the other hand, many practical problems are large-scale and constrained, making it difficult to construct efficient proposal distributions in classical methods and slowing down MCMC mixing. In this work, we propose a divide-and-conquer neural network surrogate framework for quantum sampling to accelerate MCMC under fixed Hamming weight constraints. Our method divides the interaction graph for an Ising problem into subgraphs, generates samples using QAOA for those subproblems with an XY mixer.
Tensor network surrogate models for variational quantum computation
- Authors: Ryo Watanabe, Dries Sels, Joseph Tindall (Osaka / Boston / Flatiron)
- arXiv: 2604.20180
- Category: new submission — Quantum Physics (quant-ph)
- Score: 8/10 (HIGH)
- Overlaps with: Y1 (parameter concentration/transfer for QAOA, method), Y3 (classical surrogate for QAOA on IBM hardware topology, method), scope overlap with NISQ-era heavy-hex benchmarks
- Why it matters: Characterises exactly where parameter concentration breaks down on 127-qubit IBM heavy-hex QAOA at depth p=100 — directly informs Y1's warm-start scaling claims and Y3's hardware-vs-simulator story. Demonstrates TN as a training surrogate beyond state-vector reach.
We adopt a two-dimensional tensor-network (TN) ansatz to simulate variational quantum algorithms on two-dimensional qubit architectures, demonstrating its capability to accurately simulate deep circuits through the Quantum Approximate Optimization Algorithm (QAOA) applied to Ising spin-glass problems on heavy-hexagonal and square lattices. For heavy-hexagonal problems with up to three-body interactions, parameters trained on small instances and transferred to systems an order of magnitude larger improve the sampled energy distribution only up to intermediate depths, indicating a fundamental limit of parameter concentration as a transfer strategy. By extending the training itself with TN simulations on larger system sizes, we avoid local minima and obtain lower-energy samples.
Distributed Quantum Optimization for Large-Scale Higher-Order Problems with Dense Interactions
- Authors: Seongmin Kim, Vincent R. Pascuzzi, Travis S. Humble, Thomas Beck, Sanghyo Hwang, Tengfei Luo, Eungkyu Lee, In-Saeng Suh (ORNL / IBM / Notre Dame / Kyung Hee)
- arXiv: 2604.20599
- Category: new submission — Quantum Physics (quant-ph); Computational Engineering (cs.CE); Distributed Computing (cs.DC)
- Score: 8/10 (HIGH)
- Overlaps with: Y3 (HPC-orchestrated QAOA with classical outer loop, method+scope), Y4 (classical-quantum decomposition a la ADMM, method), Y2 (HUBO-direct vs quadratisation philosophy, scope)
- Why it matters: End-to-end demonstration of distributed QAOA on IBM Heron r2 for HUBO up to N=500 with clustering that shows hardware time-to-solution is width-flat while simulator time is exponential — empirical quantum utility argument at scales beyond Y3's current regime.
Many real-world problems are naturally formulated as higher-order optimization (HUBO) tasks involving dense, multi-variable interactions, which are challenging to solve with classical methods. Quantum optimization offers a promising route, but hardware constraints and limitations to quadratic formulations have hampered their practicality. Here, we develop a distributed quantum optimization framework (DQOF) for dense, large-scale HUBO problems. DQOF assigns quantum circuits a central role in directly capturing higher-order interactions, while high-performance computing orchestrates large-scale parallelism and coordination. A clustering strategy enables wide quantum circuits without increasing depth, allowing efficient execution on near-term quantum hardware.
Moderately relevant (score 5–7) — 4 papers
Distributed Quantum-Enhanced Optimization: A Topographical Preconditioning Approach for High-Dimensional Search
- Authors: Dominik Soós, Marc Paterno, John Stenger, Nikos Chrisochoides
- arXiv: 2604.20639
- Category: new submission — Quantum Physics (quant-ph); Distributed Computing (cs.DC)
- Score: 7/10 (MED)
- Overlaps with: Y1 (quantum warm-start concept, method), Y3 (GPU + QPU classical-quantum orchestration, method), Y5 (250-variable search framing echoes Y5's SDP scale)
- Why it matters: Uses a QPU as a topographical preconditioner to seed a classical GPU BFGS solver on 10D Rastrigin/Ackley; the “quantum warm-start” terminology is directly Y1's. Continuous-optimisation flavour differs from Yuan's discrete focus but the seeding philosophy translates.
Optimization problems become fundamentally challenging as the number of variables increases. Because the volume of the search space grows exponentially, classical algorithms frequently fail to locate the global minimum of non-convex functions. While quantum optimization offers a potential alternative, mapping continuous problems onto near-term quantum hardware introduces severe scaling limits and barren plateaus. To bridge this gap, we propose the Distributed Quantum-Enhanced Optimization (D-QEO) framework. Instead of forcing the quantum processor to find the exact minimum, we use it simply as a topographical preconditioner. The QPU maps the landscape to locate the most promising basin of attraction, generating high-quality seed points for a classical GPU-accelerated solver to refine.
Cutting-plane methodology via quantum optimization for solving the Traveling Salesman Problem
- Authors: Alessia Ciacco, Luigi Di Puglia Pugliese, Francesca Guerriero
- arXiv: 2604.20321
- Category: new submission — Quantum Physics (quant-ph)
- Score: 6/10 (MED)
- Overlaps with: Y4 (OR-style classical-quantum hybrid for constrained binary optimisation, method), Y3 (hybrid D-Wave + classical preprocessing, method), scope overlap with portfolio/NP-hard combinatorial problems
- Why it matters: Classical OR cutting-plane scaffold wrapping a D-Wave QPU / hybrid solver inner loop for TSP, reducing subtour-elimination constraint count dynamically. Parallel to Y4's ADMM + Grover structure at a software-engineering level.
The Traveling Salesman Problem is a classical NP-hard combinatorial optimization problem that has been extensively studied in operations research. A major challenge in Traveling Salesman Problem formulations is the large number of subtour elimination constraints required to ensure a valid tour. To address this issue, we adopt an iterative approach grounded in well-established operations research techniques, in which subtour elimination constraints are generated dynamically. In addition, we integrate a preprocessing phase to reduce the number of candidate arcs. In this work, we investigate both classical and quantum optimization approaches for solving the problem using the proposed framework.
Quantum hardware noise learning via differentiable Kraus representation on tensor networks
- Authors: Ryo Sakai, Yu Yamashiro
- arXiv: 2604.20804
- Category: new submission — Quantum Physics (quant-ph)
- Score: 6/10 (MED)
- Overlaps with: Y3 (noise modelling regimes / thermal relaxation impact on QAOA, scope), Y6 (IBM Heron processor family, scope), Y1 (QAOA-with-error-detection demonstration, method)
- Why it matters: Learns device-specific noise model on ibm_fez (same Heron generation as Y6's Heron2) via differentiable Kraus operators on MPDO forward model; generalises across circuits without retraining. Directly feeds noise-aware QAOA feasibility analysis — the key Y3 “noise regime crossover” question.
We present a method for learning quantum hardware noise from a measurement distribution of a single device experiment. Each noise channel is represented by automatically differentiable Kraus operators obtained from a Stinespring-based parameterization that is completely positive and trace preserving by construction, and circuits are simulated with a matrix product density operator forward model. Independent channels are attached to each native gate type, to each nearest-neighbor crosstalk interaction, and to state preparation and measurement, and all channels are optimized end-to-end against a distance between the simulated and observed measurement distributions. On ibm_fez, a Heron-generation superconducting processor, training on a ripple-carry adder circuit reproduces the device output distribution.
Dissipative microcanonical ensemble preparation from KMS-detailed balance
- Authors: Anirban N. Chowdhury, Samuel O. Scalet, Kunal Sharma
- arXiv: 2604.19973
- Category: new submission — Quantum Physics (quant-ph); Statistical Mechanics (cond-mat.stat-mech)
- Score: 5/10 (MED)
- Overlaps with: Y5 (Gibbs state preparation via Lindblad/KMS-detailed-balance constructions, method)
- Why it matters: Extends recent KMS-detailed-balance Gibbs-state preparation techniques to microcanonical and general stationary states. Y5's Pauli-sparse Gibbs construction is a closely related technology at a different level of the stack — cross-check efficiency criteria and small-temperature limit.
Stationary states of quantum many-body Hamiltonians are invariant under the Hamiltonian evolution. Besides ground and thermal states, this class includes microcanonical ensembles that are of fundamental importance in statistical physics. We consider the preparation of general stationary states by leveraging recent advances in the field of open-system dynamics. In particular, constructions based on exact KMS-detailed balance with respect to Gibbs states of noncommuting Hamiltonians have only recently been proposed as a tool for their efficient preparation and, by extension to small temperatures, for ground state preparation. We extend these constructions to the problem of stationary state preparation.
Tangential (score 1–4) — 8 papers
- 2604.19832 · score 3/10 · Option Pricing on Noisy Intermediate-Scale Quantum Computers: A Quantum Neural Network Approach — NISQ finance application, but uses QNN not QAOA — scope overlap with Y3 only.
- 2604.20647 · score 3/10 · Quantum Advantage for Coordinated Frequency Selection Against Distributed Jammers — quantum advantage claim (Wehner), but on a communication/coordination task, not optimisation.
- 2604.19947 · score 3/10 · SAT + NAUTY: Orderly Generation of Small Kochen-Specker Sets — contextuality foundations, adjacent to Y6's PBR theme.
- 2604.20513 · score 3/10 · Constrained Optimal Polynomials for Quantum Linear System Solvers — Krylov-framework polynomial design; algorithm-design adjacency to Y4.
- 2604.19814 · score 2/10 · Quantum Integrated High-Performance Computing: Foundations, Architectural Elements and Future Directions — QPU-HPC vision paper; only scope adjacency via combinatorial-optimisation mentions.
- 2604.19911 · score 2/10 · Semi-device-independent self-testing of unitary operations — foundations-adjacent to Y6, but via SDI rather than PBR.
- 2604.20338 · score 2/10 · Column Generation for the Optimization of Switching in Repeaterless Quantum Networks — OR-style LP + column generation on a quantum-networking problem.
- 2604.20384 · score 2/10 · Hessian-vector products for tensor networks via recursive tangent-state propagation — second-order optimiser for tensor networks; potential relevance to VQA landscape analysis.
Summary table
| Score | arXiv ID | Short title | Overlaps | arXiv |
|---|---|---|---|---|
| 9 | 2604.20088 | CVaR-Assisted Custom Penalty for Constrained Optimization | Y2, Y4, Y3 | link |
| 9 | 2604.20701 | Divide-and-Conquer NN Surrogates for Quantum Sampling MCMC | Y1, Y2, Y4, Y3 | link |
| 8 | 2604.20180 | Tensor network surrogate models for variational quantum computation | Y1, Y3 | link |
| 8 | 2604.20599 | Distributed Quantum Optimization for Large-Scale HUBO | Y3, Y4, Y2 | link |
| 7 | 2604.20639 | Distributed Quantum-Enhanced Optimization (D-QEO) preconditioner | Y1, Y3, Y5 | link |
| 6 | 2604.20321 | Cutting-plane quantum optimization for TSP | Y4, Y3 | link |
| 6 | 2604.20804 | Quantum hardware noise learning via differentiable Kraus / MPDO | Y3, Y6, Y1 | link |
| 5 | 2604.19973 | Dissipative microcanonical ensemble via KMS-detailed balance | Y5 | link |
| 3 | 2604.19832 | Option Pricing on NISQ via Quantum Neural Networks | Y3 | link |
| 3 | 2604.19947 | SAT + NAUTY: Small Kochen-Specker sets | Y6 | link |
| 3 | 2604.20513 | Constrained Optimal Polynomials for Quantum Linear System Solvers | Y4 | link |
| 3 | 2604.20647 | Quantum Advantage for Coordinated Frequency Selection | Y6 (quantum-advantage framing) | link |
| 2 | 2604.19814 | Quantum Integrated HPC: Architectural Elements | Y3 (scope) | link |
| 2 | 2604.19911 | SDI self-testing of unitary operations | Y6 | link |
| 2 | 2604.20338 | Column Generation for Switching in Repeaterless Quantum Networks | Y4 (OR framing) | link |
| 2 | 2604.20384 | Hessian-vector products for tensor networks | Y3 (VQA landscape) | link |