Quantum 5, 538 (2021). https://doi.org/10.22331/q-2021-09-09-538 Guided by the intuition of coherent superposition of causal relations, recent works presented quantum processes without classical common-cause and direct-cause explanation, that is, processes which cannot be written as probabilistic […]
Quantum 5, 537 (2021). https://doi.org/10.22331/q-2021-09-07-537 Distributing entanglement over long distances is one of the central tasks in quantum networks. An important problem, especially for near-term quantum networks, is to develop optimal entanglement distribution protocols that […]
Quantum 5, 536 (2021). https://doi.org/10.22331/q-2021-09-07-536 In its standard formulation, quantum backflow is a classically impossible phenomenon in which a free quantum particle in a positive-momentum state exhibits a negative probability current. Recently, Miller et al. […]
Quantum 5, 535 (2021). https://doi.org/10.22331/q-2021-09-02-535 One strategy to fit larger problems on NISQ devices is to exploit a tradeoff between circuit width and circuit depth. Unfortunately, this tradeoff still limits the size of tractable problems […]
Quantum 5, 534 (2021). https://doi.org/10.22331/q-2021-08-31-534 Quantum computing can efficiently simulate Hamiltonian dynamics of many-body quantum physics, a task that is generally intractable with classical computers. The hardness lies at the ubiquitous anti-commutative relations of quantum […]
Quantum 5, 533 (2021). https://doi.org/10.22331/q-2021-08-31-533 We present methods for implementing arbitrary permutations of qubits under interaction constraints. Our protocols make use of previous methods for rapidly reversing the order of qubits along a path. Given […]
Quantum 5, 532 (2021). https://doi.org/10.22331/q-2021-08-30-532 The quantum approximate optimization algorithm (QAOA) has become a cornerstone of contemporary quantum applications development. Here we show that the $density$ of problem constraints versus problem variables acts as a […]
Quantum 5, 531 (2021). https://doi.org/10.22331/q-2021-08-30-531 A key problem in the field of quantum computing is understanding whether quantum machine learning (QML) models implemented on noisy intermediate-scale quantum (NISQ) machines can achieve quantum advantages. Recently, Huang […]
Much of the recent progress in AI has come from building ever-larger neural networks. A new chip powerful enough to handle “brain-scale” models could turbo-charge this approach. Chip startup Cerebras leaped into the limelight in […]
Quantum 5, 530 (2021). https://doi.org/10.22331/q-2021-08-27-530 In a quantum world, reference frames are ultimately quantum systems too – but what does it mean to “jump into the perspective of a quantum particle”? In this work, we […]
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