11 papers
Out-of-control Hamiltonian Learning
Weiyuan Gong, Muzhou Ma, Sitan Chen +2
Learning the Hamiltonian of a many-body system from its dynamics is a central task in quantum science, yet the algorithms with the strongest provable guarantees assume some level o…
Proper Agnostic Learning of Matrix Product States and Tree Tensor Networks
Constantin Cedillo Vayson de Pradenne, Jordan Cotler
We establish proper agnostic learning of matrix product states and tree tensor networks. Given copies of an arbitrary quantum state , our algorithms return a state o…
Exponential quantum advantage for learning signals with a single qubit
Ishaan Kannan, Sridhar Prabhu, Saeed A. Khan +7
Quantum technology has the potential to transform scientific discovery, but quantum advantages often require processing capabilities well beyond the reach of experimental platforms…
When quantum thermal states look classical
Harald Putterman, Alexander Zlokapa, Jordan Cotler
At high temperature, quantum Gibbs states retain several classical features of the maximally mixed state: the absence of entanglement, the absence of magic, analyticity of the part…
Restrictions on non-Clifford fault tolerance and ruling out beyond-SQL quantum metrology
Constantin Cedillo Vayson de Pradenne, Ishaan Kannan, Harald Putterman +1
Quantum metrology promises a quadratic speedup over the standard quantum limit (SQL), but signal-aligned noise is expected to preclude this advantage in realistic settings. A poten…
Learning Hamiltonians at Long Times
Constantin Cedillo Vayson de Pradenne, Jordan Cotler, Hsin-Yuan Huang
We study the problem of learning an unknown -qubit Hamiltonian from for a single time , where may be arbitrarily large. For broad families of local Ham…
Fault-tolerant quantum processing of physical experiments
Ishaan Kannan, Harald Putterman, Jordan Cotler
Quantum computers may reveal features of Nature inaccessible to conventional experiments, but manipulating raw quantum data introduces noise that degrades inference even when the p…
Quantum Advantage for Sensing Properties of Classical Fields
Jordan Cotler, Daine L. Danielson, Ishaan Kannan
Modern precision experiments often probe unknown classical fields with bosonic sensors in quantum-noise-limited regimes where vacuum fluctuations limit conventional readout. We int…
Noisy Quantum Learning Theory
Jordan Cotler, Weiyuan Gong, Ishaan Kannan
We develop a framework for learning from noisy quantum experiments in which fault-tolerant devices access uncharacterized systems through noisy couplings. Introducing the complexit…
Local arrows of time in quantum many-body systems
Andrew G. Yates, Jordan Cotler, Nishad Maskara +1
We demonstrate that in quantum many-body systems, local arrows of time can differ from the global time induced by Hamiltonian evolution. That is, within a quantum many-body sys…
Dynamics of learning to integrate in linear recurrent neural networks
Blake Bordelon, Jordan Cotler, Cengiz Pehlevan +1
Learning recurrent connectivity that supports memory over long intrinsic timescales is a basic problem in the theory of dynamical computation. While continuous attractor and integr…