Exploiting subspace constraints and ab initio variational methods for
quantum chemistry
- URL: http://arxiv.org/abs/2206.11246v2
- Date: Thu, 17 Nov 2022 18:14:50 GMT
- Title: Exploiting subspace constraints and ab initio variational methods for
quantum chemistry
- Authors: Cica Gustiani, Richard Meister, Simon C. Benjamin
- Abstract summary: We employ methods described in a sister paper to the present report to solve problems using adaptively evolving quantum circuits.
We find that this approach can outperform human-designed circuits such as the coupled-cluster or hardware-efficient ans"atze.
We also introduce a novel approach to constraining the circuit evolution in the physically relevant subspace.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Variational methods offer a highly promising route to exploiting quantum
computers for chemistry tasks. Here we employ methods described in a sister
paper to the present report, entitled ab initio machine synthesis of quantum
circuits, in order to solve problems using adaptively evolving quantum
circuits. Consistent with prior authors we find that this approach can
outperform human-designed circuits such as the coupled-cluster or
hardware-efficient ans\"atze, and we make comparisons for larger instances up
to 14 qubits. Moreover we introduce a novel approach to constraining the
circuit evolution in the physically relevant subspace, finding that this
greatly improves performance and compactness of the circuits. We consider both
static and dynamics properties of molecular systems. The emulation environments
used is QuESTlink; all resources are open source and linked from this paper.
Related papers
- 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) - Adaptive Circuit Learning of Born Machine: Towards Realization of
Amplitude Embedding and Data Loading [7.88657961743755]
We present a novel algorithm "Adaptive Circuit Learning of Born Machine" (ACLBM)
Our algorithm is tailored to selectively integrate two-qubit entangled gates that best capture the complex entanglement present within the target state.
Empirical results underscore the proficiency of our approach in encoding real-world data through amplitude embedding.
arXiv Detail & Related papers (2023-11-29T16:47:31Z) - Peptide Binding Classification on Quantum Computers [3.9540968630765643]
We conduct an extensive study on using near-term quantum computers for a task in the domain of computational biology.
We perform sequence classification on a task relevant to the design of therapeutic proteins, and find competitive performance with classical baselines of similar scale.
This work constitutes the first proof-of-concept application of near-term quantum computing to a task critical to the design of therapeutic proteins.
arXiv Detail & Related papers (2023-11-27T10:32:31Z) - 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) - QNEAT: Natural Evolution of Variational Quantum Circuit Architecture [95.29334926638462]
We focus on variational quantum circuits (VQC), which emerged as the most promising candidates for the quantum counterpart of neural networks.
Although showing promising results, VQCs can be hard to train because of different issues, e.g., barren plateau, periodicity of the weights, or choice of architecture.
We propose a gradient-free algorithm inspired by natural evolution to optimize both the weights and the architecture of the VQC.
arXiv Detail & Related papers (2023-04-14T08:03:20Z) - Large-scale sparse wavefunction circuit simulator for applications with
the variational quantum eigensolver [0.0]
We show that purely classical resources can be used to optimize quantum circuits in an approximate but robust manner.
We demonstrate this with a unitary coupled cluster ansatz on various molecules up to 64 qubits with tens of thousands of variational parameters.
arXiv Detail & Related papers (2023-01-13T19:03:21Z) - Exploring ab initio machine synthesis of quantum circuits [0.0]
Gate-level quantum circuits are often derived manually from higher level algorithms.
Here we explore methods for the ab initio creation of circuits within a machine.
arXiv Detail & Related papers (2022-06-22T17:48:29Z) - Quantum circuit debugging and sensitivity analysis via local inversions [62.997667081978825]
We present a technique that pinpoints the sections of a quantum circuit that affect the circuit output the most.
We demonstrate the practicality and efficacy of the proposed technique by applying it to example algorithmic circuits implemented on IBM quantum machines.
arXiv Detail & Related papers (2022-04-12T19:39:31Z) - Numerical Simulations of Noisy Quantum Circuits for Computational
Chemistry [51.827942608832025]
Near-term quantum computers can calculate the ground-state properties of small molecules.
We show how the structure of the computational ansatz as well as the errors induced by device noise affect the calculation.
arXiv Detail & Related papers (2021-12-31T16:33:10Z) - Quantum-optimal-control-inspired ansatz for variational quantum
algorithms [105.54048699217668]
A central component of variational quantum algorithms (VQA) is the state-preparation circuit, also known as ansatz or variational form.
Here, we show that this approach is not always advantageous by introducing ans"atze that incorporate symmetry-breaking unitaries.
This work constitutes a first step towards the development of a more general class of symmetry-breaking ans"atze with applications to physics and chemistry problems.
arXiv Detail & Related papers (2020-08-03T18:00:05Z)
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.