Quantum learning theory is a new and very active area of research at the intersection of
quantum computing and machine learning. Important breakthroughs in the past two years …

Quantum technology promises to revolutionize how we learn about the physical world. An
experiment that processes quantum data with a quantum computer could have substantial …

The success of modern deep learning hinges on the ability to train neural networks at scale.
Through clever reuse of intermediate information, backpropagation facilitates training …

Pseudorandom states, introduced by Ji, Liu and Song (Crypto'18), are efficiently-computable
quantum states that are computationally indistinguishable from Haar-random states. One …

We prove that random quantum circuits on any geometry, including a 1D line, can form
approximate unitary designs over $ n $ qubits in $\log n $ depth. In a similar manner, we …

While quantum state tomography is notoriously hard, most states hold little interest to
practically minded tomographers. Given that states and unitaries appearing in nature are of …

In the classical world, the existence of commitments is equivalent to the existence of one-
way functions. In the quantum setting, on the other hand, commitments are not known to …

We construct a classical oracle relative to which P= NP yet single-copy secure
pseudorandom quantum states exist. In the language of Impagliazzo's five worlds, this is a …

A fundamental challenge in quantum information science is certifying that an n-qubit state ρ
prepared in the lab closely matches a target state |ψ⟩. Previous approaches to this problem …

In the classical model of computation, it is well established that one-way functions (OWF) are
minimal for computational cryptography: They are essential for almost any cryptographic …