Quantum Information-Assisted Complete Active Space Optimization (QICAS)
- URL: http://arxiv.org/abs/2309.01676v3
- Date: Wed, 18 Sep 2024 11:05:11 GMT
- Title: Quantum Information-Assisted Complete Active Space Optimization (QICAS)
- Authors: Lexin Ding, Stefan Knecht, Christian Schilling,
- Abstract summary: We propose an information-assisted complete correlation space selection scheme (QICAS)
What sets QICAS apart is the use of unique measures in quantum information that assess the correlation in an unambiguous manner and an orbital optimization step.
Our study validates a profound empirical conjecture: the optimal non-active spaces predominantly those that contain the entanglement entanglement.
- Score: 1.2289361708127877
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Automated active space selection is arguably one of the most challenging and essential aspects of multiconfigurational methods. In this work we propose an effective quantum information-assisted complete active space optimization (QICAS) scheme. What sets QICAS apart from other correlation-based selection schemes is (i) the use of unique measures from quantum information that assess the correlation in electronic structures in an unambiguous and predictive manner, and (ii) an orbital optimization step that minimizes the correlation discarded by the active space approximation. Equipped with these features QICAS yields for smaller correlated molecules sets of optimized orbitals with respect to which the CASCI energy reaches the corresponding CASSCF energy within chemical accuracy. For more challenging systems such as the Chromium dimer, QICAS offers an excellent starting point for CASSCF by greatly reducing the number of iterations required for numerical convergence. Accordingly, our study validates a profound empirical conjecture: the energetically optimal non-active spaces are predominantly those that contain the least entanglement.
Related papers
- Correction scheme for total energy obtained on fault-tolerant quantum computer via quantum dominant orbital selection and subspace dynamical correlation methods [0.0]
We propose a practical method for accurately evaluating molecular energies using a hybrid approach that integrates fault-tolerant quantum computers with classical computing.<n>Our scheme does not suffer from massive task to read out quantum data readout and demonstrates the potential to efficiently compute large, complex molecular systems.
arXiv Detail & Related papers (2026-03-03T08:10:31Z) - Chemically decisive benchmarks on the path to quantum utility [5.186218508509959]
We introduce a curated hierarchy of benchmark systems designed to probe distinct regimes of electronic correlation relevant to molecular, bioinorganic, and heavy-element chemistry.<n>Our benchmark set spans multireference bond breaking (N$$), high-spin transition-metal chemistry (FeS), biologically relevant iron-sulfur clusters ([2Fe-2S]), and actinideactinide bonding (U$$$)<n>As a concrete realization, we benchmark a recently developed automated and adaptive quantum algorithm based on generator-inspired subspace expansion.
arXiv Detail & Related papers (2026-01-15T19:24:51Z) - Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing [68.35481158940401]
CL-QAS is a continual quantum architecture search framework.<n>It mitigates challenges of costly encoding amplitude and forgetting in variational quantum circuits.<n>It achieves controllable robustness expressivity, sample-efficient generalization, and smooth convergence without barren plateaus.
arXiv Detail & Related papers (2026-01-10T02:36:03Z) - Benchmarking VQE Configurations: Architectures, Initializations, and Optimizers for Silicon Ground State Energy [0.0]
This work investigates the performance of the Variational Quantumsolver (VQE) in estimating the ground-state energy of the silicon atom.<n>Within a hybrid quantum-classical optimization framework, we implement VQE using a range of ansatz, including Double Excitation Gates, ParticleConservingU2, UCCSD, and k-UpCCGSD.<n>The main contribution of this work lies in a systematic exploration of how these configuration choices interact to influence VQE performance.
arXiv Detail & Related papers (2025-10-27T09:57:26Z) - AEGISS -- Atomic orbital and Entropy-based Guided Inference for Space Selection -- A novel semi-automated active space selection workflow for quantum chemistry and quantum computing applications [0.0]
We present a novel approach inspired by both the AVAS (Atomic Valence Active Space) and AutoCAS methods.<n>Our method unifies orbital entropy analysis with atomic orbital projections to guide the construction of chemically and physically meaningful active spaces.<n>We validate our approach on a set of molecular systems relevant to photodynamic therapy, in particular a set of Ru(II)-complexes.
arXiv Detail & Related papers (2025-08-14T14:11:26Z) - Molecular Properties in Quantum-Classical Auxiliary-Field Quantum Monte Carlo: Correlated Sampling with Application to Accurate Nuclear Forces [1.2189422792863451]
We extend correlated sampling from classical auxiliary-field quantum Monte Carlo to the quantum-classical (QCAFQMC) framework.<n>We demonstrate significant improvements over single-reference methods in force evaluations for N$ wave$ and stretched linear H$_4$, particularly in strongly correlated regions.<n>We also apply our methodology to the MEA-CO$$ carbon capture reaction, employing quantum information metrics for active space selection and matchgate shadows.
arXiv Detail & Related papers (2025-07-23T23:51:10Z) - Quantum Annealing for Machine Learning: Applications in Feature Selection, Instance Selection, and Clustering [41.94295877935867]
We implement both quantum and classical solvers to compare their effectiveness.<n>For feature selection, we propose several QUBO configurations that balance feature importance and redundancy.<n>In instance selection, we propose a few novels for instance-level importance measures that extend existing methods.<n>For clustering, we embed a classical-to-quantum pipeline, using classical clustering followed by QUBO-based medoid refinement.
arXiv Detail & Related papers (2025-07-20T17:59:14Z) - Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems.
In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems.
We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
arXiv Detail & Related papers (2024-04-17T11:00:12Z) - Projective Quantum Eigensolver via Adiabatically Decoupled Subsystem Evolution: a Resource Efficient Approach to Molecular Energetics in Noisy Quantum Computers [0.0]
We develop a projective formalism that aims to compute ground-state energies of molecular systems accurately using Noisy Intermediate Scale Quantum (NISQ) hardware.
We demonstrate the method's superior performance under noise while concurrently ensuring requisite accuracy in future fault-tolerant systems.
arXiv Detail & Related papers (2024-03-13T13:27:40Z) - A self-consistent field approach for the variational quantum
eigensolver: orbital optimization goes adaptive [52.77024349608834]
We present a self consistent field approach (SCF) within the Adaptive Derivative-Assembled Problem-Assembled Ansatz Variational Eigensolver (ADAPTVQE)
This framework is used for efficient quantum simulations of chemical systems on nearterm quantum computers.
arXiv Detail & Related papers (2022-12-21T23:15:17Z) - Orbital-optimized pair-correlated electron simulations on trapped-ion
quantum computers [0.471876092032107]
Variational quantum eigensolvers (VQE) are among the most promising approaches for solving electronic structure problems on quantum computers.
A critical challenge for VQE in practice is that one needs to strike a balance between the expressivity of the VQE ansatz versus the number of quantum gates required to implement the ansatz.
We run end-to-end VQE algorithms with up to 12 qubits and 72 variational parameters - the largest full VQE simulation with a correlated wave function on quantum hardware.
arXiv Detail & Related papers (2022-12-05T18:40:54Z) - Say NO to Optimization: A Non-Orthogonal Quantum Eigensolver [0.0]
A balanced description of both static and dynamic correlations in electronic systems with nearly degenerate low-lying states presents a challenge for multi-configurational methods on classical computers.
We present here a quantum algorithm utilizing the action of correlating cluster operators to provide high-quality wavefunction ans"atze.
arXiv Detail & Related papers (2022-05-18T16:20:36Z) - Adiabatic Quantum Computing for Multi Object Tracking [170.8716555363907]
Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time.
As these optimization problems are often NP-hard, they can only be solved exactly for small instances on current hardware.
We show that our approach is competitive compared with state-of-the-art optimization-based approaches, even when using of-the-shelf integer programming solvers.
arXiv Detail & Related papers (2022-02-17T18:59:20Z) - Quantum-Classical Hybrid Algorithm for the Simulation of All-Electron
Correlation [58.720142291102135]
We present a novel hybrid-classical algorithm that computes a molecule's all-electron energy and properties on the classical computer.
We demonstrate the ability of the quantum-classical hybrid algorithms to achieve chemically relevant results and accuracy on currently available quantum computers.
arXiv Detail & Related papers (2021-06-22T18:00:00Z) - Benchmarking adaptive variational quantum eigensolvers [63.277656713454284]
We benchmark the accuracy of VQE and ADAPT-VQE to calculate the electronic ground states and potential energy curves.
We find both methods provide good estimates of the energy and ground state.
gradient-based optimization is more economical and delivers superior performance than analogous simulations carried out with gradient-frees.
arXiv Detail & Related papers (2020-11-02T19:52:04Z) - 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) - The impacts of optimization algorithm and basis size on the accuracy and
efficiency of variational quantum eigensolver [8.94838505400535]
Variational quantum eigensolver (VQE) is demonstrated to be the promising methodology for quantum chemistry based on near-term quantum devices.
In this work, five molecules (H2, LiH, HF, N2 and F2) are studied based on the VQE method using unitary coupled cluster (UCC) ansatz.
The performance of the gradient optimization L-BFGS-B is compared with that of the direct search method COBYLA.
For practical applications of VQE, complete active space (CAS) is suggested based on limited quantum resources.
arXiv Detail & Related papers (2020-06-29T07:50:24Z)
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.