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20202026
most citedWhen can classical neural networks represent quantum states?

3 citations · 4 across the 7 of their papers we have counts for

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quant-ph2026★ 1 cited

Securing Elliptic Curve Cryptocurrencies against Quantum Vulnerabilities: Resource Estimates and Mitigations

Ryan Babbush, Adam Zalcman, Craig Gidney +6

This whitepaper seeks to elucidate implications that the capabilities of developing quantum architectures have on blockchain vulnerabilities and mitigation strategies. First, we pr…

quant-ph2025

A log-depth in-place quantum Fourier transform that rarely needs ancillas

Gregory D. Kahanamoku-Meyer, John Blue, Thiago Bergamaschi +2

When designing quantum circuits for a given unitary, it can be much cheaper to achieve a good approximation on most inputs than on all inputs. In this work we formalize this idea,…

quant-ph2025

A Constant Rate Quantum Computer on a Line

Craig Gidney, Thiago Bergamaschi

We prove by construction that the Bravyi-Poulin-Terhal bound on the spatial density of stabilizer codes does not generalize to stabilizer circuits. To do so, we construct a fault t…

quant-ph2024★ 3 cited

When can classical neural networks represent quantum states?

Tai-Hsuan Yang, Mehdi Soleimanifar, Thiago Bergamaschi +1

A naive classical representation of an n-qubit state requires specifying exponentially many amplitudes in the computational basis. Past works have demonstrated that classical neura…

quant-ph2020

Simulated Quantum Annealing is Efficient on the Spike Hamiltonian

Thiago Bergamaschi

In this work we study the convergence of a classical algorithm called Simulated Quantum Annealing (SQA) on the Spike Hamiltonian, a specific toy model Hamiltonian for quantum-mecha…