30 research outputs found

    Characterizing the geometry of the Kirkwood-Dirac positive states

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    The Kirkwood-Dirac (KD) quasiprobability distribution can describe any quantum state with respect to the eigenbases of two observables AA and BB. KD distributions behave similarly to classical joint probability distributions but can assume negative and nonreal values. In recent years, KD distributions have proven instrumental in mapping out nonclassical phenomena and quantum advantages. These quantum features have been connected to nonpositive entries of KD distributions. Consequently, it is important to understand the geometry of the KD-positive and -nonpositive states. Until now, there has been no thorough analysis of the KD positivity of mixed states. Here, we characterize how the full convex set of states with positive KD distributions depends on the eigenbases of AA and BB. In particular, we identify three regimes where convex combinations of the eigenprojectors of AA and BB constitute the only KD-positive states: (i)(i) any system in dimension 22; (ii)(ii) an open and dense set of bases in dimension 33; and (iii)(iii) the discrete-Fourier-transform bases in prime dimension. Finally, we investigate if there can exist mixed KD-positive states that cannot be written as convex combinations of pure KD-positive states. We show that for some choices of observables AA and BB this phenomenon does indeed occur. We explicitly construct such states for a spin-11 system.Comment: 35 pages, 2 figure

    Quantum simulations of time travel can power nonclassical metrology

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    Gambling agencies forbid late bets, placed after the winning horse crosses the finish line. A time-traveling gambler could cheat the system. We construct a gamble that one can win by simulating time travel with experimentally feasible entanglement manipulation. Our gamble echoes a common metrology protocol: A gambler must prepare probes to input into a metrology experiment. The goal is to infer as much information per probe as possible about a parameter's value. If the input is optimal, the information gained per probe can exceed any value achievable classically. The gambler chooses the input state analogously to choosing a horse. However, only after the probes are measured does the gambler learn which input would have been optimal. The gambler can "place a late bet" by effectively teleporting the optimal input back in time, via entanglement manipulation. Our Gedankenexperiment demonstrates that not only true time travel, but even a simulation offers a quantum advantage in metrology.Comment: 5+1 pages. 2 figures. Comments are welcomed

    Dynamic-ADAPT-QAOA: An algorithm with shallow and noise-resilient circuits

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    The quantum approximate optimization algorithm (QAOA) is an appealing proposal to solve NP problems on noisy intermediate-scale quantum (NISQ) hardware. Making NISQ implementations of the QAOA resilient to noise requires short ansatz circuits with as few CNOT gates as possible. Here, we present Dynamic-ADAPT-QAOA. Our algorithm significantly reduces the circuit depth and the CNOT count of standard ADAPT-QAOA, a leading proposal for near-term implementations of the QAOA. Throughout our algorithm, the decision to apply CNOT-intensive operations is made dynamically, based on algorithmic benefits. Using density-matrix simulations, we benchmark the noise resilience of ADAPT-QAOA and Dynamic-ADAPT-QAOA. We compute the gate-error probability pgate⋆p_\text{gate}^\star below which these algorithms provide, on average, more accurate solutions than the classical, polynomial-time approximation algorithm by Goemans and Williamson. For small systems with 6−106-10 qubits, we show that pgate⋆>10−3p_{\text{gate}}^\star>10^{-3} for Dynamic-ADAPT-QAOA. Compared to standard ADAPT-QAOA, this constitutes an order-of-magnitude improvement in noise resilience. This improvement should make Dynamic-ADAPT-QAOA viable for implementations on superconducting NISQ hardware, even in the absence of error mitigation.Comment: 15 pages, 9 figure
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