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pro vyhledávání: '"Ortiz, Alejandro"'
Autor:
Meiburg, Alex, Chen, Jing, Miller, Jacob, Tihon, Raphaëlle, Rabusseau, Guillaume, Perdomo-Ortiz, Alejandro
Beyond their origin in modeling many-body quantum systems, tensor networks have emerged as a promising class of models for solving machine learning problems, notably in unsupervised generative learning. While possessing many desirable features arisin
Externí odkaz:
http://arxiv.org/abs/2310.20498
Generative modeling has seen a rising interest in both classical and quantum machine learning, and it represents a promising candidate to obtain a practical quantum advantage in the near term. In this study, we build over a proposed framework for eva
Externí odkaz:
http://arxiv.org/abs/2303.15626
With the advent of quantum and quantum-inspired machine learning, adapting the structure of learning models to match the structure of target datasets has been shown to be crucial for obtaining high performance. Probabilistic models based on tensor ne
Externí odkaz:
http://arxiv.org/abs/2211.15978
Constrained combinatorial optimization problems abound in industry, from portfolio optimization to logistics. One of the major roadblocks in solving these problems is the presence of non-trivial hard constraints which limit the valid search space. In
Externí odkaz:
http://arxiv.org/abs/2211.09121
Autor:
Rosales-Ortiz, Alejandro
A step reinforced random walk is a discrete time process with memory such that at each time step, with fixed probability $p \in (0,1)$, it repeats a previously performed step chosen uniformly at random while with complementary probability $1-p$, it p
Externí odkaz:
http://arxiv.org/abs/2210.00564
The rapid pace of recent advancements in numerical computation, notably the rise of GPU and TPU hardware accelerators, have allowed tensor network (TN) algorithms to scale to even larger quantum simulation problems, and to be employed more broadly fo
Externí odkaz:
http://arxiv.org/abs/2209.00595
Autor:
Rudolph, Manuel S., Miller, Jacob, Motlagh, Danial, Chen, Jing, Acharya, Atithi, Perdomo-Ortiz, Alejandro
While recent breakthroughs have proven the ability of noisy intermediate-scale quantum (NISQ) devices to achieve quantum advantage in classically-intractable sampling tasks, the use of these devices for solving more practically relevant computational
Externí odkaz:
http://arxiv.org/abs/2208.13673
In recent proposals of quantum circuit models for generative tasks, the discussion about their performance has been limited to their ability to reproduce a known target distribution. For example, expressive model families such as Quantum Circuit Born
Externí odkaz:
http://arxiv.org/abs/2207.13645
Publikováno v:
Mach. Learn.: Sci. Technol. 4, 015016 (2023)
It is believed that one of the first useful applications for a quantum computer will be the preparation of groundstates of molecular Hamiltonians. A crucial task involving state preparation and readout is obtaining physical observables of such states
Externí odkaz:
http://arxiv.org/abs/2206.15449