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Probabilistic circuits compute multilinear polynomials that represent multivariate probability distributions. They are tractable models that support efficient marginal inference. However, various polynomial semantics have been considered in…

Artificial Intelligence · Computer Science 2024-08-09 Oliver Broadrick , Honghua Zhang , Guy Van den Broeck

By the Gottesman-Knill Theorem, the outcome probabilities of Clifford circuits can be computed efficiently. We present an alternative proof of this result for quopit Clifford circuits (i.e., Clifford circuits on collections of $p$-level…

Quantum Physics · Physics 2021-04-13 Dax Enshan Koh , Mark D. Penney , Robert W. Spekkens

We define syntax and semantics of quantum circuits, allowing measurement gates and classical channels. We define circuit-based quantum algorithms and prove that, semantically, any such algorithm is equivalent to a single measurement that…

Quantum Physics · Physics 2021-07-20 Yuri Gurevich , Andreas Blass

Quantum computers are on the brink of surpassing the capabilities of even the most powerful classical computers. This naturally raises the question of how one can trust the results of a quantum computer when they cannot be compared to…

Diagonal quantum circuits are quantum circuits comprising only diagonal gates in the computational basis. In spite of a classical feature of diagonal quantum circuits in the sense of commutativity of all gates, their computational power is…

Quantum Physics · Physics 2014-08-05 Yoshifumi Nakata , Mio Murao

Superconducting quantum circuits are typically housed in conducting enclosures in order to control their electromagnetic environment. As devices grow in physical size, the electromagnetic modes of the enclosure come down in frequency and…

Quantum Physics · Physics 2020-08-26 P. A. Spring , T. Tsunoda , B. Vlastakis , P. J. Leek

Parameterized quantum circuits (PQCs) have been broadly used as a hybrid quantum-classical machine learning scheme to accomplish generative tasks. However, whether PQCs have better expressive power than classical generative neural networks,…

Quantum Physics · Physics 2020-07-29 Yuxuan Du , Min-Hsiu Hsieh , Tongliang Liu , Dacheng Tao

This paper proves the polynomial equivalence of a broad class of definitions of quantum computational complexity. We study right-invariant metrics on the unitary group -- often called `complexity geometries' following the definition of…

Quantum Physics · Physics 2024-07-03 Adam R. Brown

We say that a circuit $C$ over a field $F$ functionally computes an $n$-variate polynomial $P$ if for every $x \in \{0,1\}^n$ we have that $C(x) = P(x)$. This is in contrast to syntactically computing $P$, when $C \equiv P$ as formal…

Computational Complexity · Computer Science 2016-05-16 Michael A. Forbes , Mrinal Kumar , Ramprasad Saptharishi

This short note proposes a symbolic approach for representing and reasoning about quantum circuits using complex, vector or matrix-valued Boolean expressions. A major benefit of this approach is that it allows us to directly borrow the…

Quantum Physics · Physics 2020-10-08 Mingsheng Ying , Zhengfeng Ji

We show how to efficiently generate pseudo-random states suitable for quantum information processing via cluster-state quantum computation. By reformulating pseudo-random algorithms in the cluster-state picture, we identify a strategy for…

Quantum Physics · Physics 2009-11-13 Winton G. Brown , Yaakov S. Weinstein , Lorenza Viola

An $n$-qubit quantum circuit is said to be peaked if it has an output probability that is at least inverse-polynomially large as a function of $n$. We describe a classical algorithm with quasipolynomial runtime $n^{O(\log{n})}$ that…

Quantum Physics · Physics 2023-09-18 Sergey Bravyi , David Gosset , Yinchen Liu

Quantum pseudorandomness, also known as unitary designs, comprise a powerful resource for quantum computation and quantum engineering. While it is known in theory that pseudorandom unitary operators can be constructed efficiently, realizing…

Quantum Physics · Physics 2019-07-24 Jun Li , Zhihuang Luo , Tao Xin , Hengyan Wang , David Kribs , Dawei Lu , Bei Zeng , Raymond Laflamme

The quantum capacity of a noisy quantum channel determines the maximal rate at which we can code reliably over asymptotically many uses of the channel, and it characterizes the channel's ultimate ability to transmit quantum information…

Quantum Physics · Physics 2021-10-26 Xin Wang

We introduce the polynomial coefficient matrix and identify maximum rank of this matrix under variable substitution as a complexity measure for multivariate polynomials. We use our techniques to prove super-polynomial lower bounds against…

Computational Complexity · Computer Science 2013-02-15 Mrinal Kumar , Gaurav Maheshwari , Jayalal Sarma M. N

We provide an $\Omega(log(n))$ lower bound for the depth of any quantum circuit generating the unique groundstate of Kitaev's spherical code. No circuit-depth lower bound was known before on this code in the general case where the gates can…

Quantum Physics · Physics 2018-10-10 Dorit Aharonov , Yonathan Touati

A finite-dimensional pseudo-unitary framework is set up for describing the dynamics of free elementary particles in a purely relativistic quantum mechanical way. States of any individual particles or antiparticles are defined as suitably…

Mathematical Physics · Physics 2015-02-03 Jorge G. Cardoso

We introduce a single-number metric, quantum volume, that can be measured using a concrete protocol on near-term quantum computers of modest size ($n\lesssim 50$), and measure it on several state-of-the-art transmon devices, finding values…

Quantum Physics · Physics 2019-10-14 Andrew W. Cross , Lev S. Bishop , Sarah Sheldon , Paul D. Nation , Jay M. Gambetta

While scalable error correction schemes and fault tolerant quantum computing seem not to be universally accessible in the near sight, the efforts of many researchers have been directed to the exploration of the contemporary available…

In this work, we highlight an unforeseen behavior of the expressivity of Parameterized Quantum Circuits (PQCs) for machine learning. A large class of these models, seen as Fourier Series which frequencies are derived from the encoding…

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