Quantum circuit compression using qubit logic on qudits
- URL: http://arxiv.org/abs/2411.03878v1
- Date: Wed, 06 Nov 2024 12:49:32 GMT
- Title: Quantum circuit compression using qubit logic on qudits
- Authors: Liam Lysaght, Timothée Goubault, Patrick Sinnott, Shane Mansfield, Pierre-Emmanuel Emeriau,
- Abstract summary: We present qubit logic on qudits (QLOQ), a compression scheme in which the qubits from a hardware agnostic circuit are divided into groups of various sizes.
We show that arbitrary qubit-logic unitaries can in principle be implemented with significantly fewer two-level (qubit) physical entangling gates in QLOQ than in qubit encoding.
- Score: 0.0
- License:
- Abstract: We present qubit logic on qudits (QLOQ), a compression scheme in which the qubits from a hardware agnostic circuit are divided into groups of various sizes, and each group is mapped to a physical qudit for computation. QLOQ circuits have qubit-logic inputs, outputs, and gates, making them compatible with existing qubit-based algorithms and Hamiltonians. We show that arbitrary qubit-logic unitaries can in principle be implemented with significantly fewer two-level (qubit) physical entangling gates in QLOQ than in qubit encoding. We achieve this advantage in practice for two applications: variational quantum algorithms, and unitary decomposition. The variational quantum eigensolver (VQE) for LiH took 5 hours using QLOQ on one of Quandela's cloud-accessible photonic quantum computers, whereas it would have taken 4.39 years in qubit encoding. We also provide a QLOQ version of the Quantum Shannon Decomposition, which not only outperforms previous qudit-based proposals, but also beats the theoretical lower bound on the CNOT cost of unitary decomposition in qubit encoding.
Related papers
- Extending Quantum Perceptrons: Rydberg Devices, Multi-Class Classification, and Error Tolerance [67.77677387243135]
Quantum Neuromorphic Computing (QNC) merges quantum computation with neural computation to create scalable, noise-resilient algorithms for quantum machine learning (QML)
At the core of QNC is the quantum perceptron (QP), which leverages the analog dynamics of interacting qubits to enable universal quantum computation.
arXiv Detail & Related papers (2024-11-13T23:56:20Z) - Elementary Quantum Arithmetic Logic Units for Near-Term Quantum Computers [0.0]
We propose feasible quantum arithmetic logic units (QALUs) for near-term quantum computers with qubits arranged in two-dimensional arrays.
We introduce a feasible quantum arithmetic operation to compute the two's complement representation of signed integers.
Our work demonstrates a viable implementation of QALUs on near-term quantum computers, advancing towards scalable and resource-efficient quantum arithmetic operations.
arXiv Detail & Related papers (2024-08-13T01:49:58Z) - Supervised binary classification of small-scale digits images with a trapped-ion quantum processor [56.089799129458875]
We show that a quantum processor can correctly solve the basic classification task considered.
With the increase of the capabilities quantum processors, they can become a useful tool for machine learning.
arXiv Detail & Related papers (2024-06-17T18:20:51Z) - An Improved QFT-Based Quantum Comparator and Extended Modular Arithmetic
Using One Ancilla Qubit [4.314578336989336]
We propose a quantum-classical comparator based on the quantum Fourier transform (QFT)
Proposed operators only require one ancilla qubit, which is optimal for qubit resources.
The proposed algorithms reduce computing resources and make them valuable for Noisy Intermediate-Scale Quantum (NISQ) computers.
arXiv Detail & Related papers (2023-05-16T02:09:41Z) - Simple Tests of Quantumness Also Certify Qubits [69.96668065491183]
A test of quantumness is a protocol that allows a classical verifier to certify (only) that a prover is not classical.
We show that tests of quantumness that follow a certain template, which captures recent proposals such as (Kalai et al., 2022) can in fact do much more.
Namely, the same protocols can be used for certifying a qubit, a building-block that stands at the heart of applications such as certifiable randomness and classical delegation of quantum computation.
arXiv Detail & Related papers (2023-03-02T14:18:17Z) - Iterative Qubits Management for Quantum Index Searching in a Hybrid
System [56.39703478198019]
IQuCS aims at index searching and counting in a quantum-classical hybrid system.
We implement IQuCS with Qiskit and conduct intensive experiments.
Results demonstrate that it reduces qubits consumption by up to 66.2%.
arXiv Detail & Related papers (2022-09-22T21:54:28Z) - Entanglement and coherence in Bernstein-Vazirani algorithm [58.720142291102135]
Bernstein-Vazirani algorithm allows one to determine a bit string encoded into an oracle.
We analyze in detail the quantum resources in the Bernstein-Vazirani algorithm.
We show that in the absence of entanglement, the performance of the algorithm is directly related to the amount of quantum coherence in the initial state.
arXiv Detail & Related papers (2022-05-26T20:32:36Z) - Multiqubit state learning with entangling quantum generative adversarial
networks [0.0]
We investigate the entangling quantum GAN (EQ-GAN) for multiqubit learning.
We show that the EQ-GAN can learn a circuit more efficiently compared with a SWAP test.
We also consider random state learning with the EQ-GAN for up to six qubits, using different two-qubit gates.
arXiv Detail & Related papers (2022-04-20T18:00:01Z) - Quantum Neuron with Separable-State Encoding [0.0]
It is not yet possible to test advanced quantum neuron models on a large scale in currently available quantum processors.
We propose a quantum perceptron (QP) model that uses a reduced number of multi-qubit gates.
We demonstrate the performance of the proposed model by implementing a few qubits version of the QP in a simulated quantum computer.
arXiv Detail & Related papers (2022-02-16T19:26:23Z) - QuantumCircuitOpt: An Open-source Framework for Provably Optimal Quantum
Circuit Design [0.0]
We propose QuantumCircuitOpt, a novel open-source framework which implements mathematical optimization formulations and algorithms for decomposing arbitrary unitary gates into a sequence of hardware-native gates.
We show that QCOpt can find up to 57% reduction in the number of necessary gates on circuits with up to four qubits, and in run times less than a few minutes on commodity computing hardware.
We also show how the QCOpt package can be adapted to various built-in types of native gate sets, based on different hardware platforms like those produced by IBM, Rigetti and Google.
arXiv Detail & Related papers (2021-11-23T06:45:40Z) - QUANTIFY: A framework for resource analysis and design verification of
quantum circuits [69.43216268165402]
QUANTIFY is an open-source framework for the quantitative analysis of quantum circuits.
It is based on Google Cirq and is developed with Clifford+T circuits in mind.
For benchmarking purposes QUANTIFY includes quantum memory and quantum arithmetic circuits.
arXiv Detail & Related papers (2020-07-21T15:36:25Z)
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.