arXiv subjects

Johnnie Gray

Publications and source records attributed to Johnnie Gray.

29 records · Page 2Linked to original sources

arXiv2022

Arithmetic circuit tensor networks, multivariable function representation, and high-dimensional integration

Many computational problems can be formulated in terms of high-dimensional functions. Simple representations of such functions and resulting computations with them typically suffer from the "curse of dimensionality", an exponential cost dependence on dimension. Tensor networks provide a way to represent certain classes of high-dimensional functions with polynomial memory. This results in computations where the exponential cost is ameliorated or in some cases, removed, if the tensor network representation can be obtained. Here, we introduce a direct mapping from the arithmetic circuit of a function to arithmetic circuit tensor networks, avoiding the need to perform any optimization or functional fit. We demonstrate the power of the circuit construction in examples of multivariable integration on the unit hypercube in up to 50 dimensions, where the complexity of integration can be understood from the circuit structure. We find very favorable cost scaling compared to quasi-Monte-Carlo integration for these cases, and further give an example where efficient quasi-Monte-Carlo cannot be theoretically performed without knowledge of the underlying tensor network circuit structure.

math.NA↗

arXiv2022

Using hyper-optimized tensor networks and first-principles electronic structure to simulate experimental properties of the giant {Mn84} torus

The single-molecule magnet {Mn84} is a challenge to theory due to its high nuclearity. Building on our prior work which characterized the structure of the spectrum of this magnet, we directly compute two experimentally accessible observables, the field-dependent magnetization up to 75 T and the temperature-dependent heat capacity, using parameter free theory. In particular, we use first principles calculations to derive short- and long-range exchange interactions, while we compute the exact partition function of the resulting classical Potts and Ising spin models for all 84 Mn $S=2$ spins to obtain the observables. The latter computation is possible because of a simulation methodology that uses hyper-optimized tensor network contraction, borrowing from recent techniques developed to simulate quantum supremacy circuits. We also synthesize the magnet and measure its heat capacity and field-dependent magnetization. We observe good qualitative agreement between theory and experiment, identifying an unusual peak in the heat capacity in both, as well as a plateau in the magnetization. Our work also identifies some limitations of current theoretical modeling in large magnets, such as the sensitivity to small, long-range, exchange couplings.

physics.chem-ph↗

arXiv2021

The Variational Power of Quantum Circuit Tensor Networks

We characterize the variational power of quantum circuit tensor networks in the representation of physical many-body ground-states. Such tensor networks are formed by replacing the dense block unitaries and isometries in standard tensor networks by local quantum circuits. We explore both quantum circuit matrix product states and the quantum circuit multi-scale entanglement renormalization ansatz, and introduce an adaptive method to optimize the resulting circuits to high fidelity with more than $10^4$ parameters. We benchmark their expressiveness against standard tensor networks, as well as other common circuit architectures, for the 1D/2D Heisenberg and 1D Fermi-Hubbard models. We find quantum circuit tensor networks to be substantially more expressive than other quantum circuits for these problems, and that they can even be more compact than standard tensor networks. Extrapolating to circuit depths which can no longer be emulated classically, this suggests a region of advantage in quantum expressiveness in the representation of physical ground-states.

quant-ph↗

arXiv2021

Efficient Quantum State Sample Tomography with Basis-dependent Neural-networks

We use a meta-learning neural-network approach to analyse data from a measured quantum state. Once our neural network has been trained it can be used to efficiently sample measurements of the state in measurement bases not contained in the training data. These samples can be used calculate expectation values and other useful quantities. We refer to this process as "state sample tomography". We encode the state's measurement outcome distributions using an efficiently parameterized generative neural network. This allows each stage in the tomography process to be performed efficiently even for large systems. Our scheme is demonstrated on recent IBM Quantum devices, producing a model for a 6-qubit state's measurement outcomes with a predictive accuracy (classical fidelity) > 95% for all test cases using only 100 random measurement settings as opposed to the 729 settings required for standard full tomography using local measurements. This reduction in the required number of measurements scales favourably, with training data in 200 measurement settings yielding a predictive accuracy > 92% for a 10 qubit state where 59,049 settings are typically required for full local measurement-based quantum state tomography. A reduction in number of measurements by a factor, in this case, of almost 600 could allow for estimations of expectation values and state fidelities in practicable times on current quantum devices.

quant-ph↗

arXiv2021

Hyper-optimized tensor network contraction

Tensor networks represent the state-of-the-art in computational methods across many disciplines, including the classical simulation of quantum many-body systems and quantum circuits. Several applications of current interest give rise to tensor networks with irregular geometries. Finding the best possible contraction path for such networks is a central problem, with an exponential effect on computation time and memory footprint. In this work, we implement new randomized protocols that find very high quality contraction paths for arbitrary and large tensor networks. We test our methods on a variety of benchmarks, including the random quantum circuit instances recently implemented on Google quantum chips. We find that the paths obtained can be very close to optimal, and often many orders or magnitude better than the most established approaches. As different underlying geometries suit different methods, we also introduce a hyper-optimization approach, where both the method applied and its algorithmic parameters are tuned during the path finding. The increase in quality of contraction schemes found has significant practical implications for the simulation of quantum many-body systems and particularly for the benchmarking of new quantum chips. Concretely, we estimate a speed-up of over 10,000$\times$ compared to the original expectation for the classical simulation of the Sycamore `supremacy' circuits.

quant-ph↗

arXiv2020

Quantum Delocalised-Interactions

Classical mechanics obeys the intuitive logic that a physical event happens at a definite spatial point. Entanglement however, breaks this logic by enabling interactions without a specific location. In this work we study these delocalised-interactions. These are quantum interactions that create less locational information than would be possible classically, as captured by the disturbance induced on some spatial superposition state. We introduce quantum games to capture the effect and demonstrate a direct operational use for quantum concurrence in that it bounds the non-classical performance gain. We also find a connection with quantum teleportation, and demonstrate the games using an IBM quantum processor.

quant-ph↗

arXiv2019

Scale Invariant Entanglement Negativity at the Many-Body Localization Transition

The exact nature of the many-body localization transition remains an open question. An aspect which has been posited in various studies is the emergence of scale invariance around this point, however the direct observation of this phenomenon is still absent. Here we achieve this by studying the logarithmic negativity and mutual information between disjoint blocks of varying size across the many-body localization transition. The two length scales, block sizes and the distance between them, provide a clear quantitative probe of scale invariance across different length scales. We find that at the transition point, the logarithmic negativity obeys a scale invariant exponential decay with respect to the ratio of block separation to size, whereas the mutual information obeys a polynomial decay. The observed scale invariance of the quantum correlations in a microscopic model opens the direction to probe the fractal structure in critical eigenstates using tensor network techniques and provide constraints on the theory of the many-body localization transition.

cond-mat.dis-nn↗

arXiv2018

Machine Learning Assisted Many-Body Entanglement Measurement

Entanglement not only plays a crucial role in quantum technologies, but is key to our understanding of quantum correlations in many-body systems. However, in an experiment, the only way of measuring entanglement in a generic mixed state is through reconstructive quantum tomography, requiring an exponential number of measurements in the system size. Here, we propose a machine learning assisted scheme to measure the entanglement between arbitrary subsystems of size $N_A$ and $N_B$, with $\mathcal{O}(N_A + N_B)$ measurements, and without any prior knowledge of the state. The method exploits a neural network to learn the unknown, non-linear function relating certain measurable moments and the logarithmic negativity. Our procedure will allow entanglement measurements in a wide variety of systems, including strongly interacting many body systems in both equilibrium and non-equilibrium regimes.

quant-ph↗

arXiv2018

Fast Computation of Many-Body Entanglement

Mixed state entanglement measures can act as a versatile probes of many-body systems. However, they are generally hard to compute, often relying on tricky optimizations. One measure that is straightforward to compute is the logarithmic negativity, yet done naively even this is still limited to small system sizes. Here, we introduce a method to compute the logarithmic negativity for arbitrary subsystems of a densely represented state, as well as block subsystems of matrix product states. The method combines lazily evaluated, tensor network representations of the partially transposed density matrix with stochastic Lanczos quadrature, and is easily extendible to other quantities and classes of many-body states. As examples, we compute the entanglement within random pure states for density matrices of up to 30 qubits, explore scrambling in a many-body quench, and match the results of conformal field theory in the ground-state of the Heisenberg model for density matrices of up to 1000 spins. An implementation of the algorithm has been made available in the open-source library \textit{quimb}.

quant-ph↗

arXiv2018

Many-body Localization Transition: Schmidt Gap, Entanglement Length & Scaling

Many-body localization has become an important phenomenon for illuminating a potential rift between non-equilibrium quantum systems and statistical mechanics. However, the nature of the transition between ergodic and localized phases in models displaying many-body localization is not yet well understood. Assuming that this is a continuous transition, analytic results show that the length scale should diverge with a critical exponent $ν\ge 2$ in one dimensional systems. Interestingly, this is in stark contrast with all exact numerical studies which find $ν\sim 1$. We introduce the Schmidt gap, new in this context, which scales near the transition with a exponent $ν> 2$ compatible with the analytical bound. We attribute this to an insensitivity to certain finite size fluctuations, which remain significant in other quantities at the sizes accessible to exact numerical methods. Additionally, we find that a physical manifestation of the diverging length scale is apparent in the entanglement length computed using the logarithmic negativity between disjoint blocks.

cond-mat.dis-nn↗

arXiv2016

Unravelling Quantum Dot Array Simulators via Singlet-Triplet Measurements

Recently, singlet-triplet measurements in double dots have emerged as a powerful tool in quantum information processing. In parallel, quantum dot arrays are being envisaged as analog quantum simulators of many-body models. Thus motivated, we explore the potential of the above singlet-triplet measurements for probing and exploiting the ground-state of a Heisenberg spin chain in such a quantum simulator. We formulate an efficient protocol to discriminate the achieved many-body ground-state with other likely states. Moreover, the transition between quantum phases, arising from the addition of frustrations in a $J_1-J_2$ model, can be systematically explored using the same set of measurements. We show that the proposed measurements have an application in producing long distance heralded entanglement between well separated quantum dots. Relevant noise sources, such as non-zero temperatures and nuclear spin interactions, are considered.

quant-ph↗