Multi-qutrit exact synthesis
- URL: http://arxiv.org/abs/2405.08147v1
- Date: Mon, 13 May 2024 19:48:10 GMT
- Title: Multi-qutrit exact synthesis
- Authors: Amolak Ratan Kalra, Manimugdha Saikia, Dinesh Valluri, Sam Winnick, Jon Yard,
- Abstract summary: We present an exact synthesis algorithm for qutrit unitaries in $mathcalU_3n(mathbbZ[/3,e2pi i/3])$ over the Clifford$+T$ gate set with at most one ancilla.
This, in particular, gives an exact synthesis algorithm of single-qutrit Clifford$+mathcalD$ over the multi-qutrit Clifford$+T$ gate set with at most two ancillas.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present an exact synthesis algorithm for qutrit unitaries in $\mathcal{U}_{3^n}(\mathbb{Z}[1/3,e^{2\pi i/3}])$ over the Clifford$+T$ gate set with at most one ancilla. This extends the already known result of qutrit metaplectic gates being a subset of Clifford$+T$ gate set with one ancilla. As an intermediary step, we construct an algorithm to convert 3-level unitaries into multiply-controlled gates, analogous to Gray codes converting 2-level unitaries into multiply-controlled gates. Finally, using catalytic embeddings, we present an algorithm to exactly synthesize unitaries $\mathcal{U}_{3^n}(\mathbb{Z}[1/3,e^{2\pi i/9}])$ over the Clifford$+T$ gate set with at most 2 ancillas. This, in particular, gives an exact synthesis algorithm of single-qutrit Clifford$+\mathcal{D}$ over the multi-qutrit Clifford$+T$ gate set with at most two ancillas.
Related papers
- Exact Synthesis of Multiqutrit Clifford-Cyclotomic Circuits [0.0]
We prove that a $3ntimes 3n$ unitary matrix $U$ can be represented by an $n$-qutrit circuit over the Clifford-cyclotomic gate set of degree $3k$.
arXiv Detail & Related papers (2024-05-13T19:27:48Z) - Synthesizing Toffoli-optimal quantum circuits for arbitrary multi-qubit
unitaries [0.0]
We study the Clifford+Toffoli universal fault-tolerant gate set.
We develop Toffoli-count optimal synthesis algorithms for both approximately and exactly implementable multi-qubit unitaries.
arXiv Detail & Related papers (2024-01-17T03:53:37Z) - A simple asymptotically optimal Clifford circuit compilation algorithm [0.0]
We present an algorithm that decomposes any $n$-qubit Clifford operator into a circuit consisting of three subcircuits.
As with other derivationally optimal Clifford compilation algorithms, the resulting circuit contains $O(n2/log n)$ two-qubit gates.
arXiv Detail & Related papers (2023-10-16T23:27:59Z) - Efficiently Learning One-Hidden-Layer ReLU Networks via Schur
Polynomials [50.90125395570797]
We study the problem of PAC learning a linear combination of $k$ ReLU activations under the standard Gaussian distribution on $mathbbRd$ with respect to the square loss.
Our main result is an efficient algorithm for this learning task with sample and computational complexity $(dk/epsilon)O(k)$, whereepsilon>0$ is the target accuracy.
arXiv Detail & Related papers (2023-07-24T14:37:22Z) - An Oblivious Stochastic Composite Optimization Algorithm for Eigenvalue
Optimization Problems [76.2042837251496]
We introduce two oblivious mirror descent algorithms based on a complementary composite setting.
Remarkably, both algorithms work without prior knowledge of the Lipschitz constant or smoothness of the objective function.
We show how to extend our framework to scale and demonstrate the efficiency and robustness of our methods on large scale semidefinite programs.
arXiv Detail & Related papers (2023-06-30T08:34:29Z) - ReSQueing Parallel and Private Stochastic Convex Optimization [59.53297063174519]
We introduce a new tool for BFG convex optimization (SCO): a Reweighted Query (ReSQue) estimator for the gradient of a function convolved with a (Gaussian) probability density.
We develop algorithms achieving state-of-the-art complexities for SCO in parallel and private settings.
arXiv Detail & Related papers (2023-01-01T18:51:29Z) - Learning a Single Neuron with Adversarial Label Noise via Gradient
Descent [50.659479930171585]
We study a function of the form $mathbfxmapstosigma(mathbfwcdotmathbfx)$ for monotone activations.
The goal of the learner is to output a hypothesis vector $mathbfw$ that $F(mathbbw)=C, epsilon$ with high probability.
arXiv Detail & Related papers (2022-06-17T17:55:43Z) - Shorter quantum circuits via single-qubit gate approximation [1.2463824156241843]
We give a novel procedure for approximating general single-qubit unitaries from a finite universal gate set.
We show that taking probabilistic mixtures of channels to solve (arXiv:1409.3552) and magnitude approximation problems saves factor of two in approximation costs.
arXiv Detail & Related papers (2022-03-18T17:14:35Z) - T-count and T-depth of any multi-qubit unitary [1.933681537640272]
We design provable algorithm to determine T-count of any $n$-qubit ($ngeq 1$) unitary $W$ of size $2ntimes 2n$, over the Clifford+T gate set.
Our algorithm can also be used to determine the (minimum possible) T-depth of any multi-qubit unitary.
arXiv Detail & Related papers (2021-10-19T22:16:00Z) - Solving correlation clustering with QAOA and a Rydberg qudit system: a
full-stack approach [94.37521840642141]
We study the correlation clustering problem using the quantum approximate optimization algorithm (QAOA) and qudits.
Specifically, we consider a neutral atom quantum computer and propose a full stack approach for correlation clustering.
We show the qudit implementation is superior to the qubit encoding as quantified by the gate count.
arXiv Detail & Related papers (2021-06-22T11:07:38Z) - Private Learning of Halfspaces: Simplifying the Construction and
Reducing the Sample Complexity [63.29100726064574]
We present a differentially private learner for halfspaces over a finite grid $G$ in $mathbbRd$ with sample complexity $approx d2.5cdot 2log*|G|$.
The building block for our learner is a new differentially private algorithm for approximately solving the linear feasibility problem.
arXiv Detail & Related papers (2020-04-16T16:12:10Z)
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.