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1. Noise and the Frontier of Quantum Supremacy

   https://doi.org/10.1109/FOCS52979.2021.00127  

   Bouland, Adam; Fefferman, Bill; Landau, Zeph; Liu, Yunchao (February 2022, 2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS))
2. Flavors of magnetic noise in quantum materials

   https://doi.org/10.1103/PhysRevB.106.L081122  

   Zhang, Shu; Tserkovnyak, Yaroslav (August 2022, Physical Review B)
3. From Quantum Fuzzing to the Multiverse: Possible Effective Uses of Quantum Noise

   https://doi.org/10.1007/978-3-030-98012-2_30  

   Rosch-Grace, Dominic; Straub, Jeremy (March 2022, Advances in Information and Communication. FICC 2022)

   Arai, Kohei (Ed.)

   Quantum noise is seen by many researchers as a problem to be resolved. Current solutions increase quantum computing system costs significantly by requiring numerous hardware qubits to represent a logical qubit to average the noise away. However, despite its deleterious effects on system performance and the increased costs it creates, it may have some potential uses. This paper evaluates those. Specifically, it considers how quantum noise could be used to support the fuzzing cybersecurity and testing technique and AI techniques such as certain swarm artificial intelligence algorithms. Fuzzing is used to identify vulnerabilities in software by generating massive amounts of input cases for a program. Quantum noise provides an effective built-in fuzzing capability that is centered around the actual answer to a computation. These same phenomena, of clustered and centered fuzz-noise around the answer of an operation, could be similarly useful to AI techniques that can make effective use of lots of point values for optimization. Effectively, by concurrently considering the ‘multiverse’ of possible results to an operation, created by compounding noise, more beneficial solutions that are proximal to the actual result of an operation can be identified via testing quantum noise points with an effectiveness algorithm. Both of these potential uses for quantum noise are considered herein. 

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4. Squeezing the quantum noise of a gravitational-wave detector below the standard quantum limit

   https://doi.org/10.1126/science.ado8069  

   Jia, Wenxuan; Xu, Victoria; Kuns, Kevin; Nakano, Masayuki; Barsotti, Lisa; Evans, Matthew; Mavalvala, Nergis; Abbott, R; Abouelfettouh, I; Adhikari, R X; et al (September 2024, Science)

   The Heisenberg uncertainty principle dictates that the position and momentum of an object cannot be simultaneously measured with arbitrary precision, giving rise to an apparent limitation known as the standard quantum limit (SQL). Gravitational-wave detectors use photons to continuously measure the positions of freely falling mirrors and so are affected by the SQL. We investigated the performance of the Laser Interferometer Gravitational-Wave Observatory (LIGO) after the experimental realization of frequency-dependent squeezing designed to surpass the SQL. For the LIGO Livingston detector, we found that the upgrade reduces quantum noise below the SQL by a maximum of three decibels between 35 and 75 hertz while achieving a broadband sensitivity improvement, increasing the overall detector sensitivity during astrophysical observations. 

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5. Noise-tolerant quantum speedups in quantum annealing without fine tuning

   https://doi.org/10.1088/2058-9565/abd59a  

   Kapit, Eliot; Oganesyan, Vadim (February 2021, Quantum Science and Technology)

   Abstract Quantum annealing is a powerful alternative model of quantum computing, which can succeed in the presence of environmental noise even without error correction. However, despite great effort, no conclusive demonstration of a quantum speedup (relative to state of the art classical algorithms) has been shown for these systems, and rigorous theoretical proofs of a quantum advantage (such as the adiabatic formulation of Grover’s search problem) generally rely on exponential precision in at least some aspects of the system, an unphysical resource guaranteed to be scrambled by experimental uncertainties and random noise. In this work, we propose a new variant of quantum annealing, called RFQA, which can maintain a scalable quantum speedup in the face of noise and modest control precision. Specifically, we consider a modification of flux qubit-based quantum annealing which includes low-frequency oscillations in the directions of the transverse field terms as the system evolves. We show that this method produces a quantum speedup for finding ground states in the Grover problem and quantum random energy model, and thus should be widely applicable to other hard optimization problems which can be formulated as quantum spin glasses. Further, we explore three realistic noise channels and show that the speedup from RFQA is resilient to 1/f-like local potential fluctuations and local heating from interaction with a sufficiently low temperature bath. Another noise channel, bath-assisted quantum cooling transitions, actually accelerates the algorithm and may outweigh the negative effects of the others. We also detail how RFQA may be implemented experimentally with current technology. 

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