Improving Hamiltonian encodings with the Gray code
- URL: http://arxiv.org/abs/2008.05012v3
- Date: Tue, 30 Mar 2021 12:05:40 GMT
- Title: Improving Hamiltonian encodings with the Gray code
- Authors: Olivia Di Matteo, Anna McCoy, Peter Gysbers, Takayuki Miyagi, R. M.
Woloshyn, Petr Navr\'atil
- Abstract summary: We study an efficient encoding that uses the entire set of basis states.
This encoding is applied to the commonly-studied problem of finding the ground state energy of a deuteron.
It is compared to a standard "one-hot" encoding, and various trade-offs that arise are analyzed.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Due to the limitations of present-day quantum hardware, it is especially
critical to design algorithms that make the best possible use of available
resources. When simulating quantum many-body systems on a quantum computer,
straightforward encodings that transform many-body Hamiltonians into qubit
Hamiltonians use $N$ of the available basis states of an $N$-qubit system,
whereas $2^N$ are in theory available. We explore an efficient encoding that
uses the entire set of basis states, where terms in the Hamiltonian are mapped
to qubit operators with a Hamiltonian that acts on the basis states in Gray
code order. This encoding is applied to the commonly-studied problem of finding
the ground state energy of a deuteron with a simulated variational quantum
eigensolver (VQE). It is compared to a standard "one-hot" encoding, and various
trade-offs that arise are analyzed. The energy distribution of VQE solutions
has smaller variance than the one obtained by the one-hot encoding even in the
presence of simulated hardware noise, despite an increase in the number of
measurements. The reduced number of qubits and a shorter-depth variational
ansatz enables the encoding of larger problems on current-generation machines.
This encoding also demonstrates improvements for simulating time evolution of
the same system, producing circuits for the evolution operators with reduced
depth and roughly half the number of gates compared to a one-hot encoding.
Related papers
- An Efficient Quantum Classifier Based on Hamiltonian Representations [50.467930253994155]
Quantum machine learning (QML) is a discipline that seeks to transfer the advantages of quantum computing to data-driven tasks.
We propose an efficient approach that circumvents the costs associated with data encoding by mapping inputs to a finite set of Pauli strings.
We evaluate our approach on text and image classification tasks, against well-established classical and quantum models.
arXiv Detail & Related papers (2025-04-13T11:49:53Z) - High-Entanglement Capabilities for Variational Quantum Algorithms: The Poisson Equation Case [0.07366405857677226]
This research attempts to resolve problems by utilizing the IonQ Aria quantum computer capabilities.
We propose a decomposition of the discretized equation matrix (DPEM) based on 2- or 3-qubit entanglement gates and is shown to have $O(1)$ terms with respect to system size.
We also introduce the Globally-Entangling Ansatz which reduces the parameter space of the quantum ansatz while maintaining enough expressibility to find the solution.
arXiv Detail & Related papers (2024-06-14T16:16:50Z) - Near-optimal decoding algorithm for color codes using Population Annealing [44.99833362998488]
We implement a decoder that finds the recovery operation with the highest success probability.
We study the decoder performance on a 4.8.8 color code lattice under different noise models.
arXiv Detail & Related papers (2024-05-06T18:17:42Z) - The END: An Equivariant Neural Decoder for Quantum Error Correction [73.4384623973809]
We introduce a data efficient neural decoder that exploits the symmetries of the problem.
We propose a novel equivariant architecture that achieves state of the art accuracy compared to previous neural decoders.
arXiv Detail & Related papers (2023-04-14T19:46:39Z) - Deep Quantum Error Correction [73.54643419792453]
Quantum error correction codes (QECC) are a key component for realizing the potential of quantum computing.
In this work, we efficiently train novel emphend-to-end deep quantum error decoders.
The proposed method demonstrates the power of neural decoders for QECC by achieving state-of-the-art accuracy.
arXiv Detail & Related papers (2023-01-27T08:16:26Z) - Efficient Quantum Simulation of Electron-Phonon Systems by Variational
Basis State Encoder [12.497706003633391]
Digital quantum simulation of electron-phonon systems requires truncating infinite phonon levels into $N$ basis states.
We propose a variational basis state encoding algorithm that reduces the scaling of the number of qubits and quantum gates.
arXiv Detail & Related papers (2023-01-04T04:23:53Z) - Towards Neural Variational Monte Carlo That Scales Linearly with System
Size [67.09349921751341]
Quantum many-body problems are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors.
The combination of neural networks (NN) for representing quantum states, and the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems.
We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm.
arXiv Detail & Related papers (2022-12-21T19:00:04Z) - Quantum algorithms for grid-based variational time evolution [36.136619420474766]
We propose a variational quantum algorithm for performing quantum dynamics in first quantization.
Our simulations exhibit the previously observed numerical instabilities of variational time propagation approaches.
arXiv Detail & Related papers (2022-03-04T19:00:45Z) - Deterministic and Entanglement-Efficient Preparation of
Amplitude-Encoded Quantum Registers [0.533024001730262]
A classical vector $mathbfb$ is encoded in the amplitudes of a quantum state.
An arbitrary state of $Q$ qubits generally requires approximately $2Q$ entangling gates.
We present a deterministic (nonvariational) algorithm that allows one to flexibly reduce the quantum resources required for state preparation.
arXiv Detail & Related papers (2021-10-26T07:37:54Z) - Space-efficient binary optimization for variational computing [68.8204255655161]
We show that it is possible to greatly reduce the number of qubits needed for the Traveling Salesman Problem.
We also propose encoding schemes which smoothly interpolate between the qubit-efficient and the circuit depth-efficient models.
arXiv Detail & Related papers (2020-09-15T18:17:27Z) - Electronic structure with direct diagonalization on a D-Wave quantum
annealer [62.997667081978825]
This work implements the general Quantum Annealer Eigensolver (QAE) algorithm to solve the molecular electronic Hamiltonian eigenvalue-eigenvector problem on a D-Wave 2000Q quantum annealer.
We demonstrate the use of D-Wave hardware for obtaining ground and electronically excited states across a variety of small molecular systems.
arXiv Detail & Related papers (2020-09-02T22:46:47Z) - On connectivity-dependent resource requirements for digital quantum
simulation of $d$-level particles [0.703901004178046]
We study the number of SWAP gates required to Trotterize commonly used quantum operators.
Results are applicable in hardware co-design and in choosing efficient qudit encodings for a given set of near-term quantum hardware.
arXiv Detail & Related papers (2020-05-26T22:28:51Z)
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.