Measurable Krylov Spaces and Eigenenergy Count in Quantum State Dynamics
arXiv:2404.13089 · doi:10.1007/JHEP10(2024)083
Abstract
In this work, we propose a quantum-mechanically measurable basis for the computation of spread complexity. Current literature focuses on computing different powers of the Hamiltonian to construct a basis for the Krylov state space and the computation of the spread complexity. We show, through a series of proofs, that time-evolved states with different evolution times can be used to construct an equivalent space to the Krylov state space used in the computation of the spread complexity. Afterwards, we introduce the effective dimension, which is upper-bounded by the number of pairwise distinct eigenvalues of the Hamiltonian. The computation of the spread complexity requires knowledge of the Hamiltonian and a classical computation of the different powers of the Hamiltonian. The computation of large powers of the Hamiltonian becomes increasingly difficult for large systems. The first part of our work addresses these issues by defining an equivalent space, where the original basis consists of quantum-mechanically measurable states. We demonstrate that a set of different time-evolved states can be used to construct a basis. We subsequently verify the results through numerical analysis, demonstrating that every time-evolved state can be reconstructed using the defined vector space. Based on this new space, we define an upper-bounded effective dimension and analyze its influence on finite-dimensional systems. We further show that the Krylov space dimension is equal to the number of pairwise distinct eigenvalues of the Hamiltonian, enabling a method to determine the number of eigenenergies the system has experimentally. Lastly, we compute the spread complexities of both basis representations and observe almost identical behavior, thus enabling the computation of spread complexities through measurements.
References in corpus (41)
- The theory of variational hybrid quantum-classical algorithms
- An introduction to quantum machine learning
- The effect of data encoding on the expressive power of variational quantum machine learning models
- Quantum Computation as Geometry
- Experimental Realization of Quantum Artificial Intelligence
- Quantum chaos and the complexity of spread of states
- Experimental quantum speed-up in reinforcement learning agents
- On The Evolution Of Operator Complexity Beyond Scrambling
- Krylov complexity from integrability to chaos
- Toda chain flow in Krylov space
- Operator growth and Krylov construction in dissipative open quantum systems
- Krylov Localization and suppression of complexity
- The bulk Hilbert space of double scaled SYK
- Universal chaotic dynamics from Krylov space
- Complexity growth of operators in the SYK model and in JT gravity
- Quantum complexity and topological phases of matter
- Probing quantum scars and weak ergodicity-breaking through quantum complexity
- Time Series Quantum Reservoir Computing with Weak and Projective Measurements
- Krylov Complexity in Open Quantum Systems
- Krylov complexity and orthogonal polynomials
- Krylov Complexity in Free and Interacting Scalar Field Theories with Bounded Power Spectrum
- Krylov complexity and chaos in quantum mechanics
- Information Processing Capacity of Spin-Based Quantum Reservoir Computing Systems
- Operator dynamics in Lindbladian SYK: a Krylov complexity perspective
- A statistical mechanism for operator growth
- Krylov complexity of density matrix operators
- Universal relation for operator complexity
- A universal approach to Krylov State and Operator complexities
- Quantum chaos, scrambling and operator growth in deformed SYK models
- Time evolution of spread complexity in quenched Lipkin-Meshkov-Glick model
- Probing the entanglement of operator growth
- Spread complexity in saddle-dominated scrambling
- Spread complexity as classical dilaton solutions
- Quantum state complexity meets many-body scars
- On operator growth and emergent Poincaré symmetries
- Krylov construction and complexity for driven quantum systems
- A relation between Krylov and Nielsen complexity
- Operator growth and Krylov Complexity in Bose-Hubbard Model
- Krylov Complexity in Lifshitz-type Scalar Field Theories
- Spread complexity for measurement-induced non-unitary dynamics and Zeno effect
- Solving the time-complexity problem and tuning the performance of quantum reservoir computing by artificial memory restriction
Cited by in corpus (6)
- Quantum Dynamics in Krylov Space: Methods and Applications
- State estimation with quantum extreme learning machines beyond the scrambling time
- Diagnosing Quantum Many-body Chaos in Non-Hermitian Quantum Spin Chain via Krylov Complexity
- Engineering Quantum Reservoirs through Krylov Complexity, Expressivity and Observability
- Signatures of quantum chaos and complexity in the Ising model on random graphs
- From Krylov Complexity to Observability: Capturing Phase Space Dimension with Applications in Quantum Reservoir Computing