Multivariate DQI uses N-variable polynomials for weighted Max-LINSAT, derives closed-form asymptotics for expectation and concentration, provides a single-decoder preparation circuit, and shows outperformance over weighted Prange for some OPI cases while extending to Hamiltonian DQI.
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Typical trace-distance relaxation concentrates around a mean in open quantum systems, producing typical mixing times separated from worst-case by rare-state bottlenecks that scale logarithmically, linearly, or exponentially depending on the slow modes.
Local tensor-train surrogates approximate quantum machine learning models via Taylor polynomials and tensor networks, delivering polynomial parameter scaling and explicit generalization bounds controlled by patch radius.
A polylog-sized quantum computer achieves exponential advantage over classical machines in classification and dimension reduction of massive classical data using quantum oracle sketching combined with classical shadows.
AutoVerifier decomposes technical claims into triples and uses layered LLM verification to assess validity, demonstrated on a quantum computing paper by finding overclaims and conflicts.
A modular atomic processor with 500,000 qubits factors 2048-bit RSA numbers in roughly the same time as a single large module when inter-module Bell-pair communication runs at 10^5 per second.
HQRN creates an exact functional match to classical residual networks on basis inputs while using quantum correlations for better performance on mixed states in digit recognition and entanglement classification.
A witness-based framework quantifies continuous-variable resources and activates them into discrete-variable entanglement or EPR steering via measure-and-prepare channels that produce Werner states.
QIML uses a quantum-trained Q-Prior to enhance classical autoregressive predictions of spatiotemporal chaos, improving accuracy by up to 17.25% and full-spectrum fidelity by up to 29.36% while enabling stable forecasts for 3D turbulent channel flow.
A tunable mixing parameter p in random quantum circuits controls the transition from classically simulable to expressive quantum reservoir dynamics via entanglement and nonstabilizer content.
End-to-end runtime definitions and strong classical baselines show that three recent quantum advantage claims in annealing, Simon's problem, and hybrid algorithms do not hold on NISQ hardware.
Engineered non-Gaussian coherence serves as a thermodynamic resource that optimizes quantum battery performance beyond Gaussian states for Gaussian charger profiles under unitary dynamics.
The paper identifies four key hurdles in the transition from NISQ to FASQ quantum computers and argues that targeting them will accelerate progress toward useful quantum advantage.
A review summarizing superconducting qubit types, DiVincenzo criteria implementations, coherence limits from defects, and large-scale integration strategies for quantum computing.
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Recent quantum runtime (dis)advantages
End-to-end runtime definitions and strong classical baselines show that three recent quantum advantage claims in annealing, Simon's problem, and hybrid algorithms do not hold on NISQ hardware.