REVIEW 5 cited by
Learning to predict arbitrary quantum processes
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Learning to predict arbitrary quantum processes
read the original abstract
We present an efficient machine learning (ML) algorithm for predicting any unknown quantum process $\mathcal{E}$ over $n$ qubits. For a wide range of distributions $\mathcal{D}$ on arbitrary $n$-qubit states, we show that this ML algorithm can learn to predict any local property of the output from the unknown process~$\mathcal{E}$, with a small average error over input states drawn from $\mathcal{D}$. The ML algorithm is computationally efficient even when the unknown process is a quantum circuit with exponentially many gates. Our algorithm combines efficient procedures for learning properties of an unknown state and for learning a low-degree approximation to an unknown observable. The analysis hinges on proving new norm inequalities, including a quantum analogue of the classical Bohnenblust-Hille inequality, which we derive by giving an improved algorithm for optimizing local Hamiltonians. Numerical experiments on predicting quantum dynamics with evolution time up to $10^6$ and system size up to $50$ qubits corroborate our proof. Overall, our results highlight the potential for ML models to predict the output of complex quantum dynamics much faster than the time needed to run the process itself.
Forward citations
Cited by 5 Pith papers
-
High-rate qLDPC processors
Non-abelian "mitten" qLDPC codes achieve 20% encoding rate with distances 10-24 on 150-975 qubits, and simulations indicate fault-tolerant processors sustaining ~10^10 logical operations at 0.1% physical error rate.
-
Simulating quantum circuits with arbitrary local noise using Pauli Propagation
Pauli propagation with tailored truncation enables efficient classical simulation of expectation values for most quantum circuits under any local noise, with high probability and logarithmic effective depth.
-
Provable learning separation for predicting time-evolution of quantum many-body systems
A provable exponential quantum-classical learning separation is established for predicting expectation values of time-evolved quantum states under unknown low-intersection Hamiltonians, assuming BQP ⊄ P/poly.
-
Pseudorandom Dynamics in the SYK Model and Cryptographic Censorship in JT Gravity
SYK disorder is shown to be an approximate unitary k-design for poly(N) k; under the planted-SYK hardness conjecture this yields gravitationally pseudorandom unitaries, implying cryptographic censorship in JT gravity ...
-
The power and limitations of learning quantum dynamics incoherently
The paper proves sample complexity bounds showing that any efficiently representable unitary can be learned incoherently with arbitrary measurements, but only low-entangling unitaries with shallow-depth measurements, ...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.