98 citations · 305 across the 21 of their papers we have counts for
21 papers
Scientific discovery as meta-optimization: a combinatorial optimization case study
Yuan-Hang Zhang, Chesson Sipling, Massimiliano Di Ventra
Scientific discovery is fundamentally an optimization problem, defined by a vast ``state space'' of theories and experiments, and an evaluation criterion based on quality, novelty,…
A Critical Assessment of the Brain Criticality Hypothesis
Chesson Sipling, Yuan-Hang Zhang, Massimiliano Di Ventra
A major unresolved question in Neuroscience is: What is the origin of the observed scale-invariant correlations in neural activity? Many researchers support the ``criticality hypot…
Memory-induced long-range order drag
Yuan-Hang Zhang, Chesson Sipling, Massimiliano Di Ventra
Recent research has shown that memory, in the form of slow degrees of freedom, can induce a phase of long-range order (LRO) in locally-coupled fast degrees of freedom, producing po…
Artificial intelligence for representing and characterizing quantum systems
Yuxuan Du, Yan Zhu, Yuan-Hang Zhang +8
Efficient characterization of large-scale quantum systems, especially those produced by quantum analog simulators and megaquop quantum computers, poses a central challenge in quant…
On the solvable-unsolvable transition due to noise-induced chaos in digital memcomputing
Dyk Chung Nguyen, Thomas Chetaille, Yuan-Hang Zhang +2
Digital memcomputing machines (DMMs) have been designed to solve complex combinatorial optimization problems. Since DMMs are fundamentally classical dynamical systems, their ordina…
Phase-Space Engineering and Collective Dynamics in Memcomputing
Chesson Sipling, Yuan-Hang Zhang, Massimiliano Di Ventra
Digital Memcomputing machines (DMMs) are dynamical systems with memory (time non-locality) that have been designed to solve combinatorial optimization problems. Their corresponding…
A Generative Neural Annealer for Black-Box Combinatorial Optimization
Yuan-Hang Zhang, Massimiliano Di Ventra
We propose a generative, end-to-end solver for black-box combinatorial optimization that emphasizes both sample efficiency and solution quality on NP problems. Drawing inspiration…
Memory in neural activity: long-range order without criticality
Jay Sun, Chesson Sipling, Yuan-Hang Zhang +1
The "criticality hypothesis", based on observed scale-free correlations in neural activity, posits that the brain operates at a critical point of transition between two phases. How…
Memory-induced long-range order in dynamical systems
C. Sipling, Y. -H. Zhang, M. Di Ventra
Time non-locality, or memory, is a non-equilibrium property shared by all physical systems. Here, we show that memory is sufficient to induce a phase of spatial long-range order (L…
Collective dynamics and long-range order in thermal neuristor networks
Yuan-Hang Zhang, Chesson Sipling, Erbin Qiu +2
In the pursuit of scalable and energy-efficient neuromorphic devices, recent research has unveiled a novel category of spiking oscillators, termed "thermal neuristors." These devic…
Implementation of digital MemComputing using standard electronic components
Yuan-Hang Zhang, Massimiliano Di Ventra
Digital MemComputing machines (DMMs), which employ nonlinear dynamical systems with memory (time non-locality), have proven to be a robust and scalable unconventional computing app…
Reconfigurable cascaded thermal neuristors for neuromorphic computing
Erbin Qiu, Yuan-Hang Zhang, Massimiliano Di Ventra +1
While the complementary metal-oxide semiconductor (CMOS) technology is the mainstream for the hardware implementation of neural networks, we explore an alternative route based on a…
Hardware implementation of digital memcomputing on small-size FPGAs
Dyk Chung Nguyen, Yuan-Hang Zhang, Massimiliano Di Ventra +1
Memcomputing is a novel computing paradigm beyond the von-Neumann one. Its digital version is designed for the efficient solution of combinatorial optimization problems, which emer…
Self-averaging of digital memcomputing machines
Daniel Primosch, Yuan-Hang Zhang, Massimiliano Di Ventra
Digital memcomputing machines (DMMs) are a new class of computing machines that employ non-quantum dynamical systems with memory to solve combinatorial optimization problems. Here,…
Transformer Quantum State: A Multi-Purpose Model for Quantum Many-Body Problems
Yuan-Hang Zhang, Massimiliano Di Ventra
Inspired by the advancements in large language models based on transformers, we introduce the transformer quantum state (TQS): a versatile machine learning model for quantum many-b…
Efficient Quantum State Tomography with Mode-assisted Training
Yuan-Hang Zhang, Massimiliano Di Ventra
Neural networks (NNs) representing quantum states are typically trained using Markov chain Monte Carlo based methods. However, unless specifically designed, such samplers only cons…
Directed percolation and numerical stability of simulations of digital memcomputing machines
Yuan-Hang Zhang, Massimiliano Di Ventra
Digital memcomputing machines (DMMs) are a novel, non-Turing class of machines designed to solve combinatorial optimization problems. They can be physically realized with continuou…
Topological Quantum Compiling with Reinforcement Learning
Yuan-Hang Zhang, Pei-Lin Zheng, Yi Zhang +1
Quantum compiling, a process that decomposes the quantum algorithm into a series of hardware-compatible commands or elementary gates, is of fundamental importance for quantum compu…
Building quantum neural networks based on swap test
Jian Zhao, Yuan-Hang Zhang, Chang-Peng Shao +3
Artificial neural network, consisting of many neurons in different layers, is an important method to simulate humain brain. Usually, one neuron has two operations: one is linear, t…
An Efficient Algorithmic Way to Construct Boltzmann Machine Representations for Arbitrary Stabilizer Code
Yuan-Hang Zhang, Zhian Jia, Yu-Chun Wu +1
Restricted Boltzmann machines (RBMs) have demonstrated considerable success as variational quantum states; however, their representational power remains incompletely understood. In…
Efficient Machine Learning Representations of Surface Code with Boundaries, Defects, Domain Walls and Twists
Zhih-Ahn Jia, Yuan-Hang Zhang, Yu-Chun Wu +3
Machine learning representations of many-body quantum states have recently been introduced as an ansatz to describe the ground states and unitary evolutions of many-body quantum sy…