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Sarah Sheldon

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Defining Quantum Advantage: A Push for Platform-Agnostic, Empirically Verifiable Standards

As quantum hardware matures, the term "quantum advantage" has become inconsistently applied across vendors, architectures, and use cases — creating ambiguity that undermines scientific and commercial credibility. This paper proposes an operational definition of quantum advantage that is both platform-agnostic and empirically verifiable, addressing the lack of consensus in the field. The authors identify the algorithmic families most likely to achieve early advantage and envision quantum computing as a complement to classical HPC infrastructure, with near-term impact expected in chemistry, materials discovery, and optimization.

CSHOREBench: A Benchmark for Quantum Chemistry Hamiltonian Measurement Methods

The CSHOREBench benchmark evaluates randomized measurement methods for quantum chemistry Hamiltonians. It considers both quantum (measurement count) and classical (runtime for measurement proposal and post-processing) resource utilization across common molecular Hamiltonians and quantum states. The study demonstrates that decision diagrams and derandomization are preferred, with decision diagrams significantly reducing measurements compared to classical shadows and locally biased classical shadows.

Measurement-Based Quantum State Preparation Excels in Noise Stability and Realizes Nishimori Transition

A measurement-based protocol on a 127-qubit superconducting device demonstrates superior fidelity for Greenberger-Horne-Zeilinger (GHZ) states compared to unitary protocols, enabling the study of Ising order on 54 system qubits. This approach effectively prepares quantum states with enhanced stability against noise and gate imperfections. The study experimentally confirms the stability of decoded long-range order in two spatial dimensions up to a critical point, identifying a Nishimori universality class transition. The Born rule intrinsically drives this Nishimori physics, simplifying experimental realization by locking effective temperature and disorder.

Quantum Acceleration for Markov Chain Monte Carlo

A quantum algorithm for Markov Chain Monte Carlo (MCMC) sampling has been developed and implemented on a superconducting quantum processor. This hybrid quantum-classical approach demonstrably converges in fewer iterations than classical MCMC alternatives, addressing a bottleneck in sampling complicated probability distributions. This advancement suggests a near-term application for quantum computers in solving practical, rather than merely difficult, problems in fields like statistical physics, optimization, and machine learning.