Quantum fluctuations and interactions give rise to exotic phases of matter with remarkable properties, pushing the boundaries of our understanding of many-body quantum systems. Solving these problems is notoriously difficult on classical computers due to the exponential complexity of quantum many-body physics. Quantum processors, on the other hand, provide a powerful new way to explore these...
The best-known example of quantum gravity theory is the holographic principle (the AdS/CFT correspondence), in which a quantum gravitational geometry is equivalent to a lower dimensional non-gravitating quantum system. To find a consistent gravity counterpart starting from the latter is an important problem in quantum gravity and string theory. This is called “Bulk reconstruction,” and is a...
Driven by groundbreaking experimental advances, quantum matter is currently entering the era of quantum error correction - where elementary computations can be demonstrated in a fault-tolerant manner. From a many-body theory viewpoint, these developments motivate the question: what are states that are challenging to realize in the presence of error correction? Entanglement alone is not...
We propose a scheme to extract the conformal data of critical two-dimensional classical models from tensor network renormalization based finite-size scaling. The key point is to identify the length scale below which the system is in the finite-size scaling regime. The scheme can work with any tensor network renormalization method that preserves the translation invariance. In particular, we...
Computational physics —the third pillar of physics exploration next to theory and experiment — has, for the past seven decades, evolved alongside tremendous progress in computing hardware. Today the field is on the verge of leaving behind classical computing resources, and pivoting towards quantum hardware, allowing for “quantum on quantum” simulations. In this talk will discuss the...
I overview recent progress in explorations on efficient algorithms and their applications of neural network for strongly correlated electron systems. For correlated electron systems, restricted Boltzmann machines combined with the pair-product wavefunctions have shown a state-of-the-art accuracy among other quantum many-body solvers [1,2]. They have contributed to understanding of quantum...
We explore the emergence of exotic non-equilibrium phases such as prethermal discrete time crystals (DTCs) and discrete time quasicrystals (DTQCs) in two-dimensional quantum many-body systems simulated on IBM’s digital quantum computers. Using superconducting qubit architectures arranged in programmable geometries, including heavy-hex, Kagome, and Lieb lattices, we implement kicked Ising...
Neural canonical transformation leverage modern generative models to parametrize variational density matrix of many-particle systems and optimize them via the variational free energy principle. The approach finds applications in studying the equations of state of electron gas, dense hydrogen, and quantum solids. In this talk, I will present physical motivation behind the design of the method...
Recent breakthroughs in generative machine learning, powered by massive computational resources, have demonstrated unprecedented human-like capabilities. While beyond-classical quantum experiments have generated samples from classically intractable distributions, their complexity has, to-date, thwarted all efforts in efficient learning. This challenge has hindered demonstrations of generative...
Tensor network decompositions are a famous way to compress tensors of coefficients of multivariate wavefunctions in quantum physics and chemistry. Their efficiency hinges on the approximate separability of the variables. This separability can be underpinned by different assumptions. In addition to the area laws of the wavefunctions governed by the Schroedinger equation, a similar separation of...
Abstract:
Quantum chemistry has long been considered one of the most promising applications of quantum computing. However, due to the limitations of current NISQ (Noisy Intermediate-Scale Quantum) devices—particularly in terms of circuit depth and noise—these devices have only been applied to systems of around 20 qubits, where classical computers can still provide exact solutions.
In this...
Dynamical quantum phase transitions (DQPTs) are characterized by nonanalytic behavior in the return rate, a dynamical analogue of the equilibrium free energy. For a translationally invariant one-dimensional system (or 2D cylinder) we define the return rate per site as , which can be expressed via the dominant eigenvalue of the transfer operator. Analytically continuing to complex , each...
Tensor networks offer a powerful and versatile framework for addressing the complexity of quantum many-body systems and the challenges in quantum computing. This talk presents recent advances in applying tensor network algorithms within quantum-classical hybrid computing frameworks, particularly their integration with high-performance computing (HPC) environments. We explore how tensor...
In this talk I will present an efficient quantum algorithmic primitive that can accelerate some quantum algorithms.
The applications include machine learning, such as the problem of constructing a unitary emulator from a given
set of input and output quantum states.
This talk presents methods for simulating both imaginary and real-time dynamics on noisy intermediate-scale quantum (NISQ) hardware to investigate complex physical phenomena.
First, we introduce a quantum circuit-based algorithm for imaginary-time evolution to determine the ground state of 1D infinite-size systems. This approach, based on the time-dependent variational principle (TDVP), is...
We introduce a machine learning model based on flow matching to overcome the limitations of Monte Carlo (MC) sampling methods. We demonstrate its capability in the 2D XY model, where a single network, trained in configurations from a small (32X32) lattice at only sparse temperature points, can generate high-fidelity samples for both a much larger system (>128X128) and a continuous temperature...
