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John Preskill

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers
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3

FOCS Conference 2024 Conference Paper

Certifying Almost All Quantum States with Few Single-Qubit Measurements

  • Hsin-Yuan Huang
  • John Preskill
  • Mehdi Soleimanifar

A fundamental challenge in quantum information science is certifying that an n-qubit state $\rho$ prepared in the lab closely matches a target state $\vert \psi\rangle$. Previous approaches to this problem often require deep quantum circuits, exponentially many single-qubit measurements, or are limited to specific state families. In this work, we introduce a new method that leverages a connection between state certification and the mixing time of a random walk, allowing almost all n-qubit target states, including those with exponential circuit complexity, to be certified with only $\mathrm{O}(n^{2})$ single-qubit measurements. Our protocol is broadly compatible with various experimental platforms and has applications in benchmarking quantum systems, optimizing quantum circuits, and efficiently learning and verifying representations of quantum states—such as neural networks and tensor networks—using only single-qubit measurements. Moreover, these verified representations enable the efficient prediction of highly non-local properties of $\rho$ that would otherwise require an exponential number of measurements.

STOC Conference 2024 Conference Paper

Local Minima in Quantum Systems

  • Chi-Fang Chen
  • Hsin-Yuan Huang
  • John Preskill
  • Leo Zhou

Finding ground states of quantum many-body systems is known to be hard for both classical and quantum computers. As a result, when Nature cools a quantum system in a low-temperature thermal bath, the ground state cannot always be found efficiently. Instead, Nature finds a local minimum of the energy. In this work, we study the problem of finding local minima in quantum systems under thermal perturbations. While local minima are much easier to find than ground states, we show that finding a local minimum is computationally hard for classical computers, even when the task is to output a single-qubit observable at any local minimum. In contrast, we prove that a quantum computer can always find a local minimum efficiently using a thermal gradient descent algorithm that mimics the cooling process in Nature. To establish the classical hardness of finding local minima, we consider a family of two-dimensional Hamiltonians such that any problem solvable by polynomial-time quantum algorithms can be reduced to finding local minima of these Hamiltonians. Therefore, cooling systems to local minima is universal for quantum computation, and, assuming quantum computation is more powerful than classical computation, finding local minima is classically hard and quantumly easy.