Quantum 6, 641 (2022). https://doi.org/10.22331/q-2022-02-02-641 Negative quantum conditional entropy states are key ingredients for information theoretic tasks such as superdense coding, state merging and one-way entanglement distillation. In this work, we ask: how does one […]
Quantum 6, 640 (2022). https://doi.org/10.22331/q-2022-02-02-640 We consider the problem of learning stabilizer states with noise in the Probably Approximately Correct (PAC) framework of Aaronson (2007) for learning quantum states. In the noiseless setting, an algorithm […]
Quantum 6, 639 (2022). https://doi.org/10.22331/q-2022-02-02-639 Landau-Zener physics is often exploited to generate quantum logic gates and to perform state initialization and readout. The quality of these operations can be degraded by noise fluctuations in the […]
Quantum 6, 638 (2022). https://doi.org/10.22331/q-2022-02-02-638 Quantum systems evolving unitarily and subject to quantum measurements exhibit various types of non-equilibrium phase transitions, arising from the competition between unitary evolution and measurements. Dissipative phase transitions in steady […]
Quantum 6, 637 (2022). https://doi.org/10.22331/q-2022-02-02-637 A highly anticipated use of quantum computers is the simulation of complex quantum systems including molecules and other many-body systems. One promising method involves directly applying a linear combination of […]
Quantum 6, 636 (2022). https://doi.org/10.22331/q-2022-01-31-636 Quantum Darwinism posits that the emergence of a classical reality relies on the spreading of classical information from a quantum system to many parts of its environment. But what are […]
Quantum 6, 635 (2022). https://doi.org/10.22331/q-2022-01-27-635 The quantum approximate optimization algorithm (QAOA) is a near-term hybrid algorithm intended to solve combinatorial optimization problems, such as MaxCut. QAOA can be made to mimic an adiabatic schedule, and […]
Quantum 6, 634 (2022). https://doi.org/10.22331/q-2022-01-27-634 Parallel operations in conventional computing have proven to be an essential tool for efficient and practical computation, and the story is not different for quantum computing. Indeed, there exists a […]
Quantum 6, 633 (2022). https://doi.org/10.22331/q-2022-01-26-633 Reinforcement learning with neural networks (RLNN) has recently demonstrated great promise for many problems, including some problems in quantum information theory. In this work, we apply RLNN to quantum hypothesis […]
Quantum 6, 632 (2022). https://doi.org/10.22331/q-2022-01-24-632 Quantum error correction procedures have the potential to enable faithful operation of large-scale quantum computers. They protect information from environmental decoherence by storing it in logical qubits, built from ensembles […]
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