Resource-Optimized Grouping Shadow for Efficient Energy Estimation

• URL: http://arxiv.org/abs/2406.17252v1
• Date: Tue, 25 Jun 2024 03:37:35 GMT
• Title: Resource-Optimized Grouping Shadow for Efficient Energy Estimation
• Authors: Min Li, Mao Lin, Matthew J. S. Beach,
• Abstract summary: We introduce a Resource-d Grouping Shadow (ROGS) algorithm, which optimally allocates measurement resources by minimizing the estimation error bound through a novel overlapped grouping strategy and convex optimization. Our numerical experiments demonstrate that ROGS requires significantly fewer quantum circuits for accurate estimation accuracy compared to existing methods given a fixed measurement budget, addressing a major cost factor for compiling and executing circuits on quantum computers.
• Score: 2.5636932629466735
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The accurate and efficient energy estimation of quantum Hamiltonians consisting of Pauli observables is an essential task in modern quantum computing. We introduce a Resource-Optimized Grouping Shadow (ROGS) algorithm, which optimally allocates measurement resources by minimizing the estimation error bound through a novel overlapped grouping strategy and convex optimization. Our numerical experiments demonstrate that ROGS requires significantly fewer unique quantum circuits for accurate estimation accuracy compared to existing methods given a fixed measurement budget, addressing a major cost factor for compiling and executing circuits on quantum computers.

Related papers

• Mitigating crosstalk and residual coupling errors in superconducting quantum processors using many-body localization [21.175964469657803]
  This study introduces a novel calibration scheme harnessing the principles of Many-Body Localization (MBL) Our MBL-based methodology emerges as a stalwart against noise, notably crosstalk and residual coupling errors. Not only does this approach provide a marked improvement in performance, particularly where specific residue couplings are present, but it also presents a more resource-efficient and cost-effective calibration process.
  arXiv  Detail & Related papers  (2023-10-10T13:36:09Z)
• Near-Term Distributed Quantum Computation using Mean-Field Corrections and Auxiliary Qubits [77.04894470683776]
  We propose near-term distributed quantum computing that involve limited information transfer and conservative entanglement production. We build upon these concepts to produce an approximate circuit-cutting technique for the fragmented pre-training of variational quantum algorithms.
  arXiv  Detail & Related papers  (2023-09-11T18:00:00Z)
• Robust ground-state energy estimation under depolarizing noise [4.969229261095783]
  We present a novel ground-state energy estimation algorithm that is robust under global depolarizing error channels. Our research demonstrates the feasibility of ground-state energy estimation in the presence of depolarizing noise.
  arXiv  Detail & Related papers  (2023-07-20T22:30:12Z)
• Applicability of Measurement-based Quantum Computation towards Physically-driven Variational Quantum Eigensolver [17.975555487972166]
  Variational quantum algorithms are considered one of the most promising methods for obtaining near-term quantum advantages. The roadblock to developing quantum algorithms with the measurement-based quantum computation scheme is resource cost. We propose an efficient measurement-based quantum algorithm for quantum many-body system simulation tasks, called measurement-based Hamiltonian variational ansatz (MBHVA)
  arXiv  Detail & Related papers  (2023-07-19T08:07:53Z)
• Fault-tolerant quantum algorithm for symmetry-adapted perturbation theory [0.0]
  We consider the symmetry-adapted perturbation theory components of the interaction energy as a prototypical example of such an observable. We present a quantum algorithm that estimates interaction energies at the first-order SAPT level with a Heisenberg-limited scaling.
  arXiv  Detail & Related papers  (2023-05-11T17:52:44Z)
• Guaranteed efficient energy estimation of quantum many-body Hamiltonians using ShadowGrouping [55.47824411563162]
  Estimation of the energy of quantum many-body systems is a paradigmatic task in various research fields. We aim to find the optimal strategy with single-qubit measurements that yields the highest provable accuracy given a total measurement budget. We develop a practical, efficient estimation strategy, which we call ShadowGrouping.
  arXiv  Detail & Related papers  (2023-01-09T14:41:07Z)
• End-to-end resource analysis for quantum interior point methods and portfolio optimization [63.4863637315163]
  We provide a complete quantum circuit-level description of the algorithm from problem input to problem output. We report the number of logical qubits and the quantity/depth of non-Clifford T-gates needed to run the algorithm.
  arXiv  Detail & Related papers  (2022-11-22T18:54:48Z)
• Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
  tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs) We propose a new protocol for approximating TN states using realistic quantum circuits. Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
  arXiv  Detail & Related papers  (2022-09-01T17:08:41Z)
• Reducing the cost of energy estimation in the variational quantum eigensolver algorithm with robust amplitude estimation [50.591267188664666]
  Quantum chemistry and materials is one of the most promising applications of quantum computing. Much work is still to be done in matching industry-relevant problems in these areas with quantum algorithms that can solve them.
  arXiv  Detail & Related papers  (2022-03-14T16:51:36Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.