387 citations · 2.2k across the 41 of their papers we have counts for
44 papers
Dealing with low-weight errors that lead BP OSD to fail
Giacomo Fregona, Matthias C. Löbl, Benjamin J. Brown +2
Belief Propagation combined with Ordered Statistics Decoding (BP OSD) is an open-source decoding tool used widely within the community due to its versatility and ease of applicatio…
Variational Tensor Network Simulation of Gaussian Boson Sampling and Beyond
Jonas Vinther, Michael James Kastoryano
The continuous variable quantum computing platform constitutes a promising candidate for realizing quantum advantage, as exemplified in Gaussian Boson Sampling. While noise in the…
A little bit of self-correction
Michael J. Kastoryano, Lasse B. Kristensen, Chi-Fang Chen +1
We investigate the emergence of stable subspaces in the low-temperature quantum thermal dynamics of finite spin chains. Our analysis reveals the existence of effective decoherence-…
Coarse-To-Fine Tensor Trains for Compact Visual Representations
Sebastian Loeschcke, Dan Wang, Christian Leth-Espensen +3
The ability to learn compact, high-quality, and easy-to-optimize representations for visual data is paramount to many applications such as novel view synthesis and 3D reconstructio…
Quantum generalizations of Glauber and Metropolis dynamics
András Gilyén, Chi-Fang Chen, Joao F. Doriguello +1
Classical Markov Chain Monte Carlo methods have been essential for simulating statistical physical systems and have proven well applicable to other systems with many degrees of fre…
LoQT: Low-Rank Adapters for Quantized Pretraining
Sebastian Loeschcke, Mads Toftrup, Michael J. Kastoryano +2
Despite advances using low-rank adapters and quantization, pretraining of large models on consumer hardware has not been possible without model sharding, offloading during training…
An efficient and exact noncommutative quantum Gibbs sampler
Chi-Fang Chen, Michael J. Kastoryano, András Gilyén
Preparing thermal and ground states is an essential quantum algorithmic task for quantum simulation. In this work, we construct the first efficiently implementable and exactly deta…
Quantum algorithms: A survey of applications and end-to-end complexities
Alexander M. Dalzell, Sam McArdle, Mario Berta +10
The anticipated applications of quantum computers span across science and industry, ranging from quantum chemistry and many-body physics to optimization, finance, and machine learn…
Quantum Thermal State Preparation
Chi-Fang Chen, Michael J. Kastoryano, Fernando G. S. L. Brandão +1
Preparing ground states and thermal states is essential for simulating quantum systems on quantum computers. Despite the hope for practical quantum advantage in quantum simulation,…
Is there evidence for exponential quantum advantage in quantum chemistry?
Seunghoon Lee, Joonho Lee, Huanchen Zhai +15
The idea to use quantum mechanical devices to simulate other quantum systems is commonly ascribed to Feynman. Since the original suggestion, concrete proposals have appeared for si…
Complexity phase transitions in instantaneous quantum polynomial-time circuits
Chae-Yeun Park, Michael J. Kastoryano
We study a subclass of the Instantaneous Quantum Polynomial-time (IQP) circuit with a varying density of two-qubit gates. In addition to a known anticoncentration regime, we identi…
A highly efficient tensor network algorithm for multi-asset Fourier options pricing
Michael Kastoryano, Nicola Pancotti
Risk assessment and in particular derivatives pricing is one of the core areas in computational finance and accounts for a sizeable fraction of the global computing resources of th…
Learning Neural Network Quantum States with the Linear Method
J. Thorben Frank, Michael J. Kastoryano
Due to the strong correlations present in quantum systems, classical machine learning algorithms like stochastic gradient descent are often insufficient for the training of neural…
Expressive power of complex-valued restricted Boltzmann machines for solving non-stoquastic Hamiltonians
Chae-Yeun Park, Michael J. Kastoryano
Variational Monte Carlo with neural network quantum states has proven to be a promising avenue for evaluating the ground state energy of spin Hamiltonians. However, despite continu…
Classical restrictions of generic matrix product states are quasi-locally Gibbsian
Yaiza Aragonés-Soria, Johan Åberg, Chae-Yeun Park +1
We show that the norm squared amplitudes with respect to a local orthonormal basis (the classical restriction) of finite quantum systems on one-dimensional lattices can be exponent…
Avoiding local minima in variational quantum eigensolvers with the natural gradient optimizer
David Wierichs, Christian Gogolin, Michael Kastoryano
We compare the BFGS optimizer, ADAM and Natural Gradient Descent (NatGrad) in the context of Variational Quantum Eigensolvers (VQEs). We systematically analyze their performance on…
Geometry of learning neural quantum states
Chae-Yeun Park, Michael J. Kastoryano
Combining insights from machine learning and quantum Monte Carlo, the stochastic reconfiguration method with neural network Ansatz states is a promising new direction for high-prec…
The role of entropy in topological quantum error correction
Michael E. Beverland, Benjamin J. Brown, Michael J. Kastoryano +1
The performance of a quantum error-correction process is determined by the likelihood that a random configuration of errors introduced to the system will lead to the corruption of…
Topological transport in the steady state of a quantum particle with dissipation
Michael J. Kastoryano, Mark S. Rudner
We study topological transport in the steady state of a quantum particle hopping on a one-dimensional lattice in the presence of dissipation. The model exhibits a rich phase struct…
Locality at the boundary implies gap in the bulk for 2D PEPS
Michael J. Kastoryano, Angelo Lucia, David Perez-Garcia
Proving that the parent Hamiltonian of a Projected Entangled Pair State (PEPS) is gapped remains an important open problem. We take a step forward in solving this problem by showin…
Divide and conquer method for proving gaps of frustration free Hamiltonians
Michael J. Kastoryano, Angelo Lucia
Providing system-size independent lower bounds on the spectral gap of local Hamiltonian is in general a hard problem. For the case of finite-range, frustration free Hamiltonians on…
Driven quantum dynamics: will it blend?
Leonardo Banchi, Daniel Burgarth, Michael J. Kastoryano
Randomness is an essential tool in many disciplines of modern sciences, such as cryptography, black hole physics, random matrix theory and Monte Carlo sampling. In quantum systems,…
Entanglement renormalization, quantum error correction, and bulk causality
Isaac H. Kim, Michael J. Kastoryano
Entanglement renormalization can be viewed as an encoding circuit for a family of approximate quantum error correcting codes. The logical information becomes progressively more wel…
Limits on the storage of quantum information in a volume of space
Steven T. Flammia, Jeongwan Haah, Michael J. Kastoryano +1
We study the fundamental limits on the reliable storage of quantum information in lattices of qubits by deriving tradeoff bounds for approximate quantum error correcting codes. We…