Short-Depth Circuits for Dicke State Preparation
- URL: http://arxiv.org/abs/2207.09998v1
- Date: Wed, 20 Jul 2022 16:01:03 GMT
- Title: Short-Depth Circuits for Dicke State Preparation
- Abstract summary: We present short-depth circuits to deterministically prepare any Dicke state.
Dicke states are an important class of entangled quantum states with a large variety of applications.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present short-depth circuits to deterministically prepare any Dicke state
|Dn,k>, which is the equal-amplitude superposition of all n-qubit computational
basis states with Hamming Weight k. Dicke states are an important class of
entangled quantum states with a large variety of applications, and a long
history of experimental creation in physical systems. On the other hand, not
much is known regarding efficient scalable quantum circuits for Dicke state
preparation on realistic quantum computing hardware connectivities.
Here we present preparation circuits for Dicke states |Dn,k> with (i) a depth
of O(k log(n/k)) for All-to-All connectivity (such as on current ion trap
devices); (ii) a depth of O(k sqrt(n/k)) = O(sqrt(nk) for Grid connectivity on
grids of size Omega(sqrt(n/s)) x O(sqrt(ns)) with s<=k (such as on current
superconducting qubit devices).
Both approaches have a total gate count of O(kn), need no ancilla qubits, and
generalize to both the preparation and compression of symmetric pure states in
which all non-zero amplitudes correspond to states with Hamming weight at most
k. Thus our work significantly improves and expands previous state-of-the art
circuits which had depth O(n) on a Linear Nearest Neighbor connectivity for
arbitrary k (Fundamentals of Computation Theory 2019) and depth O(log n) on
All-to-All connectivity for k=1 (Advanced Quantum Technologies 2019).
Related papers
- Constant-depth adaptive preparation of Dicke and symmetric states [8.455444430741668]
Efficient preparation of Dicke states and, more generally, permutation-symmetric states is important for quantum information processing.<n>We introduce an exact constant-depth adaptive preparation protocol for arbitrary Dicke-$(n,k)$ states and further symmetric states.
arXiv Detail & Related papers (2026-08-02T10:46:16Z) - Constant-Depth Unitary Preparation of Dicke States [0.24350063807321942]
unitary preparation of Dicke states is limited to logarithmic depth in standard circuit models.<n>We present the first unitary, constant-depth protocols for exact Dicke state preparation.
arXiv Detail & Related papers (2026-01-15T18:51:09Z) - Performance Guarantees for Quantum Neural Estimation of Entropies [31.955071410400947]
Quantum neural estimators (QNEs) combine classical neural networks with parametrized quantum circuits.<n>We study formal guarantees for QNEs of measured relative entropies in the form of non-asymptotic error risk bounds.<n>Our theory aims to facilitate principled implementation of QNEs for measured relative entropies.
arXiv Detail & Related papers (2025-11-24T16:36:06Z) - Efficient circuits for leaf-separable state preparation [0.0]
We present a state preparation algorithm that combines logarithmic-depth Dicke state circuits with Hamming weight encoders for efficiently preparing leaf-separable" quantum states.<n>We evaluate the performance of the algorithm by numerically simulating it on randomly generated target states with between 4 and 15 qubits.<n>These results contribute to scalable state preparation for quantum algorithms that require structured inputs such as Dicke or near-Dicke states.
arXiv Detail & Related papers (2025-11-14T12:30:08Z) - Depth-Efficient Quantum Circuit Synthesis for Deterministic Dicke State Preparation [5.755460769073285]
Dicke states represent an important class of entangled quantum states with broad applications in quantum computing.<n>We propose deterministic quantum circuits for Dicke state preparation under two commonly seen qubit connectivity constraints.
arXiv Detail & Related papers (2025-05-21T11:55:17Z) - Sublinear Classical-to-Quantum Data Encoding using $n$-Toffoli Gates [3.0711566483997066]
A common strategy is amplitude encoding, which embeds a classical input vector of size N=2textsuperscriptn in the amplitudes of an n-qubit register.<n>We propose a general-purpose procedure with sublinear average depth in N, increasing the window of utility.<n>Our amplitude encoding method encodes arbitrary complex vectors of size N=2textsuperscriptn at any desired binary precision using a register with n qubits plus 2 ancillas and a sublinear number of multi-controlled NOT (MCX) gates.
arXiv Detail & Related papers (2025-05-09T13:49:16Z) - Quantum Algorithms for Stochastic Differential Equations: A Schrödingerisation Approach [29.662683446339194]
We propose quantum algorithms for linear differential equations.
The gate complexity of our algorithms exhibits an $mathcalO(dlog(Nd))$ dependence on the dimensions.
The algorithms are numerically verified for the Ornstein-Uhlenbeck processes, Brownian motions, and one-dimensional L'evy flights.
arXiv Detail & Related papers (2024-12-19T14:04:11Z) - Learning State Preparation Circuits for Quantum Phases of Matter [0.294944680995069]
We introduce a flexible and efficient framework for obtaining a state preparation circuit for a large class of many-body ground states.
We use a variant of the quantum Markov chain condition that remains robust against constant-depth circuits.
arXiv Detail & Related papers (2024-10-31T01:10:46Z) - Realization of Constant-Depth Fan-Out with Real-Time Feedforward on a Superconducting Quantum Processor [33.096693427147535]
We demonstrate a quantum fan-out gate with real-time feedforward on up to four output qubits using a superconducting quantum processor.
Our work highlights the potential of mid-circuit measurements combined with real-time conditional operations to improve the efficiency of complex quantum algorithms.
arXiv Detail & Related papers (2024-09-11T03:40:24Z) - Classically estimating observables of noiseless quantum circuits [36.688706661620905]
We present a classical algorithm for estimating expectation values of arbitrary observables on most quantum circuits.
For non-classically-simulable input states or observables, the expectation values can be estimated by augmenting our algorithm with classical shadows of the relevant state or observable.
arXiv Detail & Related papers (2024-09-03T08:44:33Z) - On the Constant Depth Implementation of Pauli Exponentials [49.48516314472825]
We decompose arbitrary exponentials into circuits of constant depth using $mathcalO(n)$ ancillae and two-body XX and ZZ interactions.
We prove the correctness of our approach, after introducing novel rewrite rules for circuits which benefit from qubit recycling.
arXiv Detail & Related papers (2024-08-15T17:09:08Z) - Linear Circuit Synthesis using Weighted Steiner Trees [45.11082946405984]
CNOT circuits are a common building block of general quantum circuits.
This article presents state-of-the-art algorithms for optimizing the number of CNOT gates.
A simulated evaluation shows that the suggested is almost always beneficial and reduces the number of CNOT gates by up to 10%.
arXiv Detail & Related papers (2024-08-07T19:51:22Z) - Efficient Quantum Circuits based on the Quantum Natural Gradient [0.0]
Efficient preparation of arbitrary entangled quantum states is crucial for quantum computation.
We propose symmetry-conserving modified quantum approximate optimization algorithm(SCom-QAOA) circuits.
The proposed scheme enlarges the set of the initial states accessible for variational quantum algorithms and widens the scope of investigation of non-equilibrium phenomena in quantum simulators.
arXiv Detail & Related papers (2023-10-16T16:08:57Z) - Variational-quantum-eigensolver-inspired optimization for spin-chain work extraction [39.58317527488534]
Energy extraction from quantum sources is a key task to develop new quantum devices such as quantum batteries.
One of the main issues to fully extract energy from the quantum source is the assumption that any unitary operation can be done on the system.
We propose an approach to optimize the extractable energy inspired by the variational quantum eigensolver (VQE) algorithm.
arXiv Detail & Related papers (2023-10-11T15:59:54Z) - Non-Linear Transformations of Quantum Amplitudes: Exponential
Improvement, Generalization, and Applications [0.0]
Quantum algorithms manipulate the amplitudes of quantum states to find solutions to computational problems.
We present a framework for applying a general class of non-linear functions to the amplitudes of quantum states.
Our work provides an important and efficient building block with potentially numerous applications in areas such as optimization, state preparation, quantum chemistry, and machine learning.
arXiv Detail & Related papers (2023-09-18T14:57:21Z) - Quantum State Preparation with Optimal Circuit Depth: Implementations
and Applications [10.436969366019015]
We show that any $Theta(n)$-depth circuit can be prepared with a $Theta(log(nd)) with $O(ndlog d)$ ancillary qubits.
We discuss applications of the results in different quantum computing tasks, such as Hamiltonian simulation, solving linear systems of equations, and realizing quantum random access memories.
arXiv Detail & Related papers (2022-01-27T13:16:30Z) - Tensor Ring Parametrized Variational Quantum Circuits for Large Scale
Quantum Machine Learning [28.026962110693695]
We propose an algorithm that compresses the quantum state within the circuit using a tensor ring representation.
The storage and computational time increases linearly in the number of qubits and number of layers, as compared to the exponential increase with exact simulation algorithms.
We achieve a test accuracy of 83.33% on Iris dataset and a maximum of 99.30% and 76.31% on binary and ternary classification of MNIST dataset.
arXiv Detail & Related papers (2022-01-21T19:54:57Z) - K-sparse Pure State Tomography with Phase Estimation [1.2183405753834557]
Quantum state tomography (QST) for reconstructing pure states requires exponentially increasing resources and measurements with the number of qubits.
QST reconstruction for any pure state composed of the superposition of $K$ different computational basis states of $n$bits in a specific measurement set-up is presented.
arXiv Detail & Related papers (2021-11-08T09:43:12Z) - Asymptotically Optimal Circuit Depth for Quantum State Preparation and
General Unitary Synthesis [24.555887999356646]
The problem is of fundamental importance in quantum algorithm design, Hamiltonian simulation and quantum machine learning, yet its circuit depth and size complexity remain open when ancillary qubits are available.
In this paper, we study efficient constructions of quantum circuits with $m$ ancillary qubits that can prepare $psi_vrangle$ in depth.
Our circuits are deterministic, prepare the state and carry out the unitary precisely, utilize the ancillary qubits tightly and the depths are optimal in a wide range of parameter regime.
arXiv Detail & Related papers (2021-08-13T09:47:11Z) - Quantum Gram-Schmidt Processes and Their Application to Efficient State
Read-out for Quantum Algorithms [87.04438831673063]
We present an efficient read-out protocol that yields the classical vector form of the generated state.
Our protocol suits the case that the output state lies in the row space of the input matrix.
One of our technical tools is an efficient quantum algorithm for performing the Gram-Schmidt orthonormal procedure.
arXiv Detail & Related papers (2020-04-14T11:05:26Z)
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.