Higher-order quantum processes respecting closed labs in classical spacetime are exactly those realizable as quantum circuits with quantum control of causal order.
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Scaling of the quantum approximate optimization algorithm on superconducting qubit based hardware
34 Pith papers cite this work. Polarity classification is still indexing.
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SAFE ma-QAOA achieves 64.3% fewer active parameters and 94.5% lower estimated QPU workload via surrogate pre-training and parameter distillation on Sherrington-Kirkpatrick, 2D spin glass, and Max-Cut instances.
Graphical Algebraic Geometry creates universal diagrammatic languages for commutative algebras and affine varieties that also characterize the qudit ZH calculus for quantum computation.
A randomized linear-time phase-folding algorithm using constant-width bitstring abstraction optimizes T-count in quantum circuits orders of magnitude faster than prior tools while achieving comparable reductions.
A projection-based model reduction enables exponential state-space reduction for constrained quantum optimization applied to random 3-SAT and agent coordination on graphs.
KOVAL-Q uses SAT solving to optimize and verify surface-code logical operations with general encodings, finding d-cycle CNOTs and 2d-cycle rotations that reduce FTQC application runtime by about 10 percent.
Iterative-QAOA solves pangenome assembly instances on current quantum hardware by using a fixed-ramp QAOA schedule with warm-start updates and a new HUBO encoding that cuts variables from O(N^{2}) to O(N log N).
A new framework for spatial quantum sensing constructs non-local estimators for field properties using quantum sensor networks, with algebraic geometry for exact placements, entanglement for maximal precision, and error-free subspaces to cut sensor requirements.
Cobble is a domain-specific language for quantum block encodings that compiles high-level matrix expressions to optimized circuits using analyses and quantum singular value transformation, achieving 2.6x-25.4x speedups over unoptimized baselines on benchmarks.
Co-optimization of flexible Iceberg error-detection gadgets with QAOA via tree search improves success probability and post-selection on Quantinuum H2-1 hardware up to 34 algorithmic qubits.
Search-based approximate diagonalization followed by analytical inversion yields high-precision multi-qubit Clifford+T circuits with 95% fewer non-Clifford gates on real-algorithm benchmarks.
Rapid mixing and frustration-freeness in short- and long-range Lindbladians imply polynomial decay of MI and CMI in fixed points, and long-range non-commuting Gibbs states satisfy local Markov property at any temperature.
Presents a coset ensemble decoder with algorithm-hardware co-design that claims better accuracy-latency trade-off and lower FPGA resource use than MWPM and UF baselines under depolarizing noise.
Gauging the spacetime code produces a lattice gauge theory inheriting circuit fault tolerance, with applications to foliated MBQC, classical memory in mixed topological states, and learnable Pauli noise degrees of freedom.
Identifies conditions and explicit constructions allowing polynomial-size quantum circuits to implement geometry oracles for pseudorandom textured materials, in contrast to Grover-hard unstructured cases.
General derivation of phase sensitivity formulas for SU(1,1) interferometers with arbitrary inputs, homodyne detection, and losses; applied to coherent-state probes to optimize configurations.
Clifford-deformed zero-rate LDPC codes achieve code-capacity thresholds approaching 50% under i.i.d. pure dephasing when the number of biased logical operators scales slower than distance or overlaps satisfy stated conditions, with new examples from tile codes.
DART-Q shows that cached state organization, overload policies, and service capacity determine whether real-time QLDPC decoders can meet deadlines under finite memory and varying load.
Spectral bounds relate graph Laplacian eigenvalues to the congestion of binary-tree embeddings, with an efficient spectral-ordering algorithm and applications to tensor-network contraction complexity.
A quantum autoencoder for multivariate time series anomaly detection achieves competitive performance with neural-network autoencoders using fewer trainable parameters.
A variational quantum autoencoder detects anomalies in brain MRI by scoring resistance to compression, reporting slice-level ROC-AUC of 0.95 and outperforming classical autoencoders and PCA on public datasets.
A sum-of-squares decomposition method systematically derives Tsirelson bounds for high-dimensional quantum systems and recovers known results for qubits and qudits while finding novel bounds.
SPICE-Q is a proposed unified data-chain framework for co-optimizing process, layout, electromagnetic simulation, circuit quantization, noise, and yield in superconducting quantum processors.
Survey of quantum feature encoding families with a cost-expressivity-robustness taxonomy, closed-form NISQ bounds, and a five-regime decision framework that recommends shallow angle encodings when gate error rate p is at or above 10^-3.
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Wasserstein Distances on Quantum Structures: an Overview
A literature review synthesizing developments in quantum Wasserstein distances, their applications, and unresolved questions.