Jarrod McClean

The ability of quantum computers to directly manipulate and analyze quantum states stored in quantum memory allows them to learn about aspects of our physical world that would otherwise be invisible given a modest number of measurements. Here we investigate a new learning resource which could be available to quantum computers in the future – measurements on the unknown state accompanied by its complex conjugate ρ⊗ρ*. For a certain shadow tomography task, we surprisingly find that measurements on only copies of ρ⊗ρ* can be exponentially more powerful than measurements on ρ⊗K, even for large K. This expands the class of exponential advantages using only a constant overhead quantum memory, or minimal quantum memory, and we provide a number of examples where the state ρ* is naturally available in both computational and physical applications. In addition, we precisely quantify the power of classical shadows on single copies under a generalized Clifford ensemble and give a class of quantities that can be efficiently learned. The learning task we study in both the single copy and quantum memory is physically natural and corresponds to real-space observables with a limit of bosonic modes, where it achieves an exponential improvement in detecting certain signals under a noisy background. In addition to quantifying a fundamentally new and powerful resource in quantum learning, we believe the advantage may find applications in improving quantum simulation, learning from quantum sensors, and uncovering new physical phenomena.

Abstract Machine learning (ML) provides the potential to solve challenging quantum many-body problems in physics and chemistry. Yet, this prospect has not been fully justified. In this work, we establish rigorous results to understand the power of classical ML and the potential for quantum advantage in an important example application: predicting outcomes of quantum mechanical processes. We prove that for achieving a small average prediction error, one can always design a classical ML model whose sample complexity is comparable to the best quantum ML model (up to a small polynomial factor). Regarding computational complexity, we show that the class of problems that can be solved by efficient classical ML models with access to sampled data is strictly larger than BPP. Hence, classical ML models may be able to solve some challenging quantum problems after training from data obtained in physical experiments. As a concrete example, we prove that a simple, classical ML model can efficiently learn to predict ground state representations that approximate expectation values of local observables up to a small, constant error. This holds for any smooth family of gapped local Hamiltonians in a finite spatial dimension.

Abstract We show how to achieve the highest efficiency yet for simulations with arbitrary basis sets by using a representation of the Coulomb operator known as tensor hypercontraction (THC). We use THC to express the Coulomb operator in a non-orthogonal basis, which we are able to block encode by separately rotating each term with angles that are obtained via QROM. Our algorithm has the best complexity scaling for an arbitrary basis, as well as the best complexity for the specific case of FeMoCo. By optimising the surface code resources, we show that FeMoCo can be simulated using about 4 million physical qubits and 3.5 days of runtime, assuming 1 s cycle times and physical gate error rates no worse than 0.1%.