PUBLICATIONS

Cambridge Quantum’s publications encompass select representative papers from Cambridge Quantum’s team of scientists and global network of affiliates. Dating back to Cambridge Quantum’s inception in 2014, our publications archive is fully searchable and features lengthy and in-depth coverage of our core technologies. Quickly filter, download and read each paper directly online or on desktop, with subjects spanning quantum chemistry, quantum artificial intelligence, quantum cybersecurity and quantum algorithms.

OUR TECHNOLOGY

Publications Archive

05
06.11.2021
Relaxations of Graph Isomorphism
Laura Mancinska, David Roberson, Robert Samal, Simone Severini, Antonios Varvitsiotis

We introduce a nonlocal game that captures and extends the notion of graph isomorphism. This game can be won in the classical case if and only if the two input graphs are isomorphic. Thus, by considering quantum strategies we are able to define the notion of quantum isomorphism. We also consider the case of more general non-signalling strategies, and show that such a strategy exists only if the graphs are fractionally isomorphic.

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06
08.10.2021
lambeq – An Efficient High-Level Python Library for Quantum NLP
Dimitri Kartsaklis, Ian Fan, Richie Yeung, Dr Anna Pearson, Robin Lorenz, Alexis Toumi, Giovanni de Felice, Dr Konstantinos Meichanetzidis, Professor Stephen Clark, Professor Bob Coecke

lambeq, the first high-level Python library for Quantum Natural Language Processing (QNLP), is presented in this paper. The open-source toolkit offers a detailed hierarchy of modules and classes implementing all stages of a pipeline for converting sentences to string diagrams, tensor networks, and quantum circuits ready to be used on a quantum computer.

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07
23.09.2021
A Non-Anyonic Qudit ZW-calculus
Quanlong Wang

We give a new type of qudit ZW-calculus which has generators and rewriting rules similar to that of the qubit ZW-calculus. Furthermore, we establish a translation between this qudit ZW-calculus and the qudit ZX-calculus which is universal. Therefore, this qudit ZW-calculus is also universal for pure qudit quantum computing.

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08
20.09.2021
Quantum Hardware Calculations of Periodic Systems: Hydrogen Chain and Iron Crystals
Kentaro Yamamoto, David Zsolt Manrique, Irfan Khan, Hideaki Sawada, and Dr David Muñoz Ramo

We focus on the practical aspect of quantum computational calculations of solid-state crystalline materials based on a theory developed in our group by using real quantum hardware with noise mitigation techniques. We select two periodic systems with different levels of complexity for these calculations, the distorted hydrogen chain and the iron crystal in the BCC and FCC phases, and evaluate the ground state energies.

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09
17.09.2021
Fast Stabiliser Simulation with Quadratic Form Expansions
Niel de Beaudrap, Dr Steven Herbert

We show how, with deft management of the quadratic form expansion representation, we may simulate individual stabiliser operations in O(n2) time matching the overall complexity of other simulation techniques. Our techniques provide economies of scale in the time to simulate simultaneous measurements of all (or nearly all) qubits in the standard basis and allow single-qubit measurements with deterministic outcomes to be simulated in constant time.

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10
10.09.2021
Something Old, Something New: Grammar-Based CCG Parsing with Transformer Models
Professor Stephen Clark

This report describes the parsing problem for Combinatory Categorial Grammar (CCG), showing how a combination of Transformer-based neural models and a symbolic CCG grammar can lead to substantial gains over existing approaches. The report provides a minimal introduction to CCG and CCG parsing, with many pointers to the relevant literature. It then describes the CCG supertagging problem and some recent work from Tian et al. (2020) which applies Transformer-based models to supertagging with great effect.

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11
10.09.2021
Every Classical Sampling Circuit is a Quantum Sampling Circuit
Dr Steven Herbert

We introduce “Q-marginals,” which are quantum states encoding some probability distribution in a manner suitable for use in Quantum Monte Carlo Integration (QMCI) and show that these can be prepared directly from a classical circuit sampling for the probability distribution of interest.

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12
10.09.2021
Noise-Aware Quantum Amplitude Estimation
Dr Steven Herbert, Roland Guichard, Darren Ng

In this paper. we derive from simple and reasonable assumptions a Gaussian noise model for NISQ Quantum Amplitude Estimation (QAE). We provide results from QAE run on various IBM superconducting quantum computers and Honeywell’s H1 trapped-ion quantum computer to show that the proposed model is a good fit for real world experimental data.

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13
09.09.2021
A Case Study of Variational Quantum Algorithms for a Job Shop Scheduling Problem
David Amaro, Matthias Rosenkranz, Nathan Fitzpatrick, Koji Hirano, Dr Mattia Fiorentini

Combinatorial optimisation models a vast range of industrial processes aiming at improving their efficiency. In this case study, we apply four variational quantum heuristics running on IBM’s superconducting quantum processors to the job shop scheduling problem. Our problem optimises a steel manufacturing process.

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14
08.09.2021
Variational Quantum Amplitude Estimation
Kirill Plekhanov, Matthias Rosenkranz, Dr Mattia Fiorentini, Dr Michael Lubasch

We propose to perform amplitude estimation with the help of constant-depth quantum circuits that variationally approximate states during amplitude amplification. In the context of Monte Carlo (MC) integration, we numerically show that shallow circuits can accurately approximate many amplitude amplification steps. We combine the variational approach with maximum likelihood amplitude estimation in VQAE.

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15
25.06.2021
Quantum Double Aspects of Surface Code Models
Alexander Cowtan, Shahn Majid

We revisit the Kitaev model for fault tolerant quantum computing on a square lattice with underlying quantum double D(G) symmetry, where G is a finite group. 

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16
18.06.2021
Filtering Variational Quantum Algorithms for Combinatorial Optimization
David Amaro, Carlo Modica, Matthias Rosenkranz, Dr Mattia Fiorentini, Marcello Benedetti and Dr Michael Lubasch

To make combinatorial optimisation more efficient, we introduce the Filtering Variational Quantum Eigensolver, which utilises filtering operators to achieve faster and more reliable convergence to the optimal solution. We explore the use of causal cones to reduce the number of qubits required on a quantum computer. Our methods perform better than the original VQE algorithm and QAOA.

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17
14.06.2021
Grammar Equations
Professor Bob Coecke and Vincent Wang

This work paves the way for a new theory of grammar that provides novel “grammatical truths.” We give a nogo-theorem for the fact that our wirings for words make no sense for preordered monoids, the form which grammatical calculi usually take. Instead, they require diagrams – or equivalently, (free) monoidal categories.

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18
11.06.2021
Quantum-Resistance in Blockchain Networks
Marcos Allende, Diego López León, Sergio Cerón, Antonio Leal, Adrián Pareja, Marcelo Da Silva, Alejandro Pardo, Duncan Jones, David Worrall, Ben Merriman, Jonathan Gilmore, Nick Kitchener, Salvador E. Venegas-Andraca

We have designed and developed a layer-two solution to secure the exchange of information between blockchain nodes over the internet and introduced a second signature in transactions using post-quantum keys. Our versatile solution can be applied to any blockchain network.

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19
19.05.2021
Quantum Monte Carlo Integration: The Full Advantage in Minimal Circuit Depth
Dr Steven Herbert

This paper proposes a method of quantum Monte-Carlo integration that retains the full quadratic quantum advantage without requiring any arithmetic or the quantum Fourier transform to be performed on the quantum computer. The heart of the proposed method is a Fourier series decomposition of the sum that approximates the expectation in Monte-Carlo integration, with each component then estimated individually using quantum amplitude estimation.

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20
17.05.2021
A CCG-Based Version of the DisCoCat Framework
Richie Yeung, Dimitri Kartsaklis

The DisCoCat model has proved to be a valuable tool for studying compositional aspects of language. However, the strong dependency of the model on a specific grammar formalism, gives rise to both theoretical and practical problems. We solve these problems by reformulating DisCoCat as a passage from Combinatory Categorial Grammar (CCG) to a category of semantics.

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21
13.04.2021
Qufinite ZX-calculus: A Unified Framework of Qudit ZX-calculi
Quanlong Wang

This paper provides a basis for a proof of completeness of qudit ZX-calcluli and their unified formalism qufinite ZX-calculus, which intuitively means quantum computing for qudits, or hybrid finite-dimensional quantum systems, can be done purely diagrammatically.

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22
09.04.2021
Estimation of Correlations and Non-Separability in Quantum Channels via Unitarity Benchmarking
Matthew Girling, Cristina Cırstoiu, David Jennings

The ability to transfer coherent quantum information between systems is a fundamental component of quantum technologies and leads to coherent correlations within the global quantum process. Motivated by recent techniques in randomised benchmarking, we develop a range of results for efficient estimation of correlations within a bipartite quantum channel.

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23
29.03.2021
Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics
Su Yeon Chang, Dr Steven Herbert, Sofia Vallecorsa, Elías F. Combarro, Dr Ross Duncan

In a collaboration with CERN, we propose a dual-PQC GAN for generative modelling applications in High-Energy Physics. This development enables the generation of samples from an ensemble of typical images – something not possible with conventional qGAN architectures.

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24
14.03.2021
Diagrammatic Differentiation for Quantum Machine Learning
Alexis Toumi, Richie Yeung, Giovanni de Felice

Diagrams are becoming a prominent tool in both machine learning (ML) and quantum computing. We adapt a key tool of ML, gradients to general diagrammatic theories. This will enable one to do (quantum) ML fully diagrammatically, substantially broadening the road towards general quantum advantage and quantum NLP in particular.

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25
11.03.2021
Variational Inference with a Quantum Computer
Marcello Benedetti, Brian Coyle, Dr Mattia Fiorentini, Dr Michael Lubasch, Matthias Rosenkranz

In this work, we propose quantum Born machines as variational distributions over discrete variables. Our techniques enable efficient variational inference with distributions beyond those that are efficiently representable on a classical computer.

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26
25.02.2021
QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer
Robin Lorenz, Dr Anna Pearson, Dr Konstantinos Meichanetzidis, Dimitri Kartsaklis, Professor Bob Coecke

In this paper, we present results on the first NLP experiments conducted on Noisy Intermediate-Scale Quantum computers for datasets of size ≥ 100 sentences. We use representations for sentences that have a natural mapping to quantum circuits to implement and successfully train two NLP models that solve simple sentence classification tasks on quantum hardware.

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27
22.02.2021
Kindergarten Quantum Mechanics Graduates
Professor Bob Coecke, Dominic Horsman, Aleks Kissinger, Quanlong Wang

This paper is a “spiritual child” of the 2005 lecture notes Kindergarten Quantum Mechanics, which showed how a simple, pictorial extension of Dirac notation allowed several quantum features to be easily expressed and derived, using language even a kindergartener can understand.

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28
06.01.2021
The Problem with Grover-Rudolph State Preparation for Quantum Monte-Carlo
Dr Steven Herbert

We prove that there is no quantum speed-up when using quantum Monte-Carlo integration to estimate the mean (and other moments) of analytically-defined log-concave probability distributions prepared as quantum states using the Grover-Rudolph method.

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29
08.12.2020
Foundations for Near-Term Quantum Natural Language Processing
Professor Bob Coecke, Giovanni de Felice, Dr Konstantinos Meichanetzidis, Alexis Toumi

We provide conceptual and mathematical foundations for near-term quantum natural language processing (QNLP) and do so in quantum computer scientist-friendly terms. We opted for an expository presentation style and provide references for supporting empirical evidence and formal statements concerning mathematical generality.

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30
07.12.2020
Grammar-Aware Question-Answering on Quantum Computers
Dr Konstantinos Meichanetzidis, Alexis Toumi, Giovanni de Felice, Professor Bob Coecke

We perform the first implementation of an NLP task on noisy intermediate-scale quantum (NISQ) hardware. Sentences are instantiated as parameterised quantum circuits. We encode word-meanings in quantum states and explicitly account for grammatical structure – which even in mainstream NLP is not commonplace – by faithfully hardwiring it as entangling operations.

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31
02.11.2020
Evaluating the Noise Resilience of Variational Quantum Algorithms
Enrico Fontana, Nathan Fitzpatrick, Dr David Muñoz Ramo, Dr Ross Duncan, Ivan Rungger

We simulate the effects of different noise types in state preparation. We find that the inclusion of redundant parameterised gates makes the circuits more noise resilient. We also report a circuit-dependent noise threshold above which the optimisation can converge to states with largely different physical properties from the target state.

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32
25.09.2020
Hardware-Efficient Variational Quantum Algorithms for Time Evolution
Marcello Benedetti, Dr Mattia Fiorentini, Dr Michael Lubasch

Parameterised quantum circuits are a promising technology for achieving a quantum advantage. An important application is the variational simulation of time evolution of quantum systems. To make the most of quantum hardware, variational algorithms need to be as hardware-efficient as possible. Here we present alternatives to the time-dependent variational principle that are hardware-efficient and do not require matrix inversion.

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33
14.09.2020
Practical Randomness and Privacy Amplification
Cameron Foreman, Sherilyn Wright, Alec Edgington, Mario Berta, Dr Florian J. Curchod

We present the first complete implementation of a randomness and privacy amplification protocol based on Bell tests. This allows the building of device-independent random number generators which output provably unbiased and private numbers. We then showcase our protocol on the quantum computers from the IBM-Q experience.

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34
19.08.2020
Momentum-Space Unitary Couple Cluster and Translational Quantum Subspace Expansion for Periodic Systems on Quantum Computers
David Zsolt Manrique, Irfan T. Khan, Kentaro Yamamoto, Vijja Wichitwechkarn, Dr David Muñoz Ramo

We have modified molecular VQE to work with Periodic Boundary Conditions (PBC), enabling us to simulate crystalline solids. We estimate the cost of these calculations and find them to be significantly more expensive than molecular VQE calculations, leading us to develop new methods to reduce both qubit count and circuit depth for NISQ deployment.

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35
31.07.2020
Using Reinforcement Learning to Perform Qubit Routing in Quantum Compilers
Matteo G. Pozzi, Dr Steven J. Herbert, Akash Sengupta, Robert D. Mullins

We demonstrate that the qubit-routing problem has a natural interpretation as a reinforcement learning problem. The results show state-of-the-art performance when qubit routing is treated as an abstracted problem and suggest that reinforcement learning may lead to further gains being made when addressing backend optimisation more generally.

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36
20.07.2020
A Generic Compilation Strategy for the Unitary Couple Cluster Ansatz
Alexander Cowtan, Will Simmons, Dr Ross Duncan

We describe a compilation strategy for Variational Quantum Eigensolver (VQE) algorithms which use the Unitary Coupled Cluster (UCC) ansatz. This strategy reduces cx depth by 75.4% on average and by up to 89.9% compared to naive synthesis for a variety of molecules, qubit encodings and basis sets.

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37
03.06.2020
Application-Motivated, Holistic Benchmarking of a Full Quantum Computing Stack
Daniel Mills, Seyon Sivarajah, Travis L. Scholten, Dr Ross Duncan

We propose “application-motivated” circuit classes for benchmarking: deep, shallow and square quantum circuits. Using systems made available by IBM Q, we examine their performance, showing that noise-aware compilation strategies may be beneficial and that device connectivity and noise levels play a crucial role in the performance of the system.

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38
08.05.2020
Quantum Natural Language Processing on Near-Term Quantum Computers
Dr Konstantinos Meichanetzidis, Stefano Gogioso, Giovanni De Felice, Nicolò Chiappori, Alexis Toumi, Professor Bob Coecke

In this work, we describe a full-stack pipeline for natural language processing on near-term quantum computers, aka QNLP. The language-modelling framework we employ is that of compositional distributional semantics (DisCoCat). Within this model, the grammatical reduction of a sentence is interpreted as a diagram, encoding a specific interaction of words according to the grammar. This interaction, together with a specific choice of word embedding, realises the meaning of a sentence.

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39
06.05.2020
DisCoPy: Monoidal Categories in Python
Giovanni de Felice, Alexis Toumi, Professor Bob Coecke

We introduce DisCoPy, an open source toolbox for computing with monoidal categories. The library provides an intuitive syntax for defining string diagrams and monoidal functors. Its modularity allows the efficient implementation of computational experiments in the various applications of category theory where diagrams have become a lingua franca.

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40
13.04.2020
Architecture-Aware Synthesis of Phase Polynomials for NISQ Devices
Arianne Meijer-van de Griend, Dr Ross Duncan

We propose a new algorithm to synthesise quantum circuits for phase polynomials, which takes into account the qubit connectivity. This work focuses on the architectures of current NISQ devices. The resulting algorithm generates circuits with a smaller CNOT depth than those currently used in Staq and Tket, while improving the runtime with respect to the former.

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41
10.04.2020
Fast and Effective Techniques for T-count Reduction via Spider Nest Identities
Niel de Beaudrap, Xiaoning Bian, Quanlong Wang

We describe techniques to reduce the T-count based on the effective application of “spider nest identities,” easily recognised products of parity-phase operations which are equivalent to the identity operation. We demonstrate the effectiveness of such techniques by obtaining improvements in the T-counts of a number of circuits in run-times, which are typically less than the time required to make a fresh cup of coffee.

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42
24.03.2020
TKET: A Retargetable Compiler for NISQ Devices
Seyon Sivarajah, Silas Dilkes, Alexander Cowtan, Will Simmons, Alec Edgington, Dr Ross Duncan

We present TKET, a quantum software development platform produced by Cambridge Quantum. The heart of TKET is a language-agnostic optimising compiler designed to generate code for a variety of NISQ devices, which has several features designed to minimise the influence of device error.

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43
13.01.2020
From Stochastic Spin Chains to Quantum Kardar-Parisi-Zhang Dynamics
Tony Jin, Alexandre Krajenbrink, Denis Bernard

We introduce the asymmetric extension of the Quantum Symmetric Simple Exclusion Process which is a stochastic model of fermions on a lattice hopping with random amplitudes. We analytically show that the time-integrated current of fermions defines a height field which exhibits a quantum non-linear stochastic Kardar-Parisi-Zhang dynamics.

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44
03.01.2020
Meaning Updating of Density Matrices
Professor Bob Coecke, Dr Konstantinos Meichanetzidis

In this paper we explore different update mechanisms for DisCoCirc, in the case where meaning is encoded in density matrices, which come with several advantages as compared to vectors.

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45
11.10.2019
Generalized Unitary Coupled Cluster Excitations for Multireference Molecular States Optimized by the Variational Quantum Eigensolver
Gabriel Greene-Diniz, Dr David Muñoz Ramo

The variational quantum eigensolver (VQE) requires specification of symmetries that describe the system, e.g. spin state and number of electrons. This opens the possibility of using VQE to obtain excited states. In this paper, various unitary coupled cluster (UCC) ansätze applied to excited states are investigated, using quantum circuits to represent single reference and multi-reference wavefunctions.

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46
10.10.2019
Dynamical Mean Field Theory Algorithm and Experiment on Quantum Computers
I. Rungger, N. Fitzpatrick, H. Chen, C. H. Alderete, H. Apel, A. Cowtan, A. Patterson, D. Muñoz Ramo, Y. Zhu, N. H. Nguyen, E. Grant, S. Chretien, L. Wossnig, N. M. Linke, R. Duncan

We present a quantum algorithm to perform dynamical mean field theory (DMFT) calculations for condensed matter systems on currently available quantum computers and demonstrate it on two quantum hardware platforms.

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47
18.06.2019
Parameterized Quantum Circuits as Machine Learning Models
Marcello Benedetti, Erika Lloyd, Stefan Sack

Hybrid quantum-classical systems make it possible to utilise existing quantum computers to their fullest extent. Within this framework, parameterised quantum circuits can be regarded as machine learning models with remarkable expressive power. This Review presents the components of these models and discusses their application to a variety of data-driven tasks, such as supervised learning and generative modelling.

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48
04.06.2019
Phase Gadget Synthesis for Shallow Circuits
Alexander Cowtan, Silas Dilkes, Dr Ross Duncan, Will Simmons, Seyon Sivarajah

In this paper, we give an overview of the circuit optimisation methods used by TKET. We focus on a novel technique based around phase gadgets presented in ZX-calculus, which makes it easy to reason about them. Taking advantage of this, we present an efficient method to translate the phase gadgets back to ∧X gates and single qubit operations suitable for execution on a quantum computer with significant reductions in gate count and circuit depth.

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49
23.05.2019
Structure Optimization for Parameterized Quantum Circuits
Mateusz Ostaszewski, Edward Grant, Marcello Benedetti

We propose an efficient method for simultaneously optimising both the structure and parameter values of quantum circuits with only a small computational overhead. Shallow circuits that use structure optimisation perform significantly better than circuits that use parameter updates alone, making this method particularly suitable for noisy intermediate-scale quantum computers.

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50
06.04.2019
The Mathematics of Text Structure
Professor Bob Coecke

In this paper, we give a mathematical foundation, referred to as DisCoCirc, for how sentences interact in texts in order to produce the meaning of that text. First we revisit DisCoCat. While in DisCoCat all meanings are fixed as states (i.e. have no input), in DisCoCirc, word meanings correspond to a type, or system, and the states of this system can evolve. Sentences are gates within a circuit which update the variable meanings of those words.

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51
12.03.2019
An Initialization Strategy for Addressing Barren Plateaus in Parametrized Quantum Circuits
Marcello Benedetti, Edward Grant, Leonard Wossnig, Mateusz Ostaszewski

In this technical note, we theoretically motivate and empirically validate an initialisation strategy which can resolve the barren plateau problem for practical applications. The technique involves randomly selecting some of the initial parameter values, then choosing the remaining values so that the circuit is a sequence of shallow blocks that each evaluates to the identity.

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52
21.02.2019
On the Qubit Routing Problem
Alexander Cowtan, Silas Dilkes, Dr Ross Duncan, Alexandre Krajenbrink, Will Simmons and Seyon Sivarajah

We introduce a new architecture-agnostic methodology for mapping abstract quantum circuits to realistic quantum computing devices with restricted qubit connectivity, as implemented by TKET. We present empirical results showing the effectiveness of this method in terms of reducing two-qubit gate depth and two-qubit gate count compared to other implementations.

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53
20.12.2018
Training of Quantum Circuits on a Hybrid Quantum Computer
D. Zhu, N. M. Linke, M. Benedetti, K. A. Landsman, N. H. Nguyen, C. H. Alderete, A. Perdomo-Ortiz, N. Korda, A. Garfoot, C. Brecque, L. Egan, O. Perdomo, C. Monroe

Generative modelling is a flavour of machine learning with applications ranging from computer vision to chemical design. It is expected to be one of the techniques most suited to take advantage of the additional resources provided by near-term quantum computers. This study represents the first successful training of a high-dimensional universal quantum circuit and highlights the promise and challenges associated with hybrid learning schemes.

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54
01.06.2018
Adversarial Quantum Circuit Learning for Pure State Approximation
Marcello Benedetti, Edward Grant, Leonard Wossnig, Simone Severini

In this work, we derive an adversarial algorithm for the problem of approximating an unknown quantum pure state. Although this could be done on universal quantum computers, the adversarial formulation enables us to execute the algorithm on near-term quantum computers.

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55
10.04.2018
Hierarchical Quantum Classifiers
Edward Grant, Marcello Benedetti , Shuxiang Cao, Andrew Hallam, Joshua Lockhart, Vid Stojevic, Andrew G. Green, Simone Severini

Quantum circuits with hierarchical structure have been used to perform binary classification of classical data encoded in a quantum state. We demonstrate that more expressive circuits in the same family achieve better accuracy and can be used to classify highly entangled quantum states, for which there is no known efficient classical method.

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56
21.01.2018
A Generative Modeling Approach for Benchmarking and Training Shallow Quantum Circuits
Marcello Benedetti, Delfina Garcia-Pintos, Yunseong Nam, Alejandro Perdomo-Ortiz

Hybrid quantum-classical algorithms provide ways to use noisy intermediate-scale quantum computers for practical applications. Expanding the portfolio of such techniques, we propose a quantum circuit learning algorithm that can be used to assist the characterisation of quantum devices and to train shallow circuits for generative tasks.

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57
31.08.2017
Quantum-Assisted Helmholtz Machines: A Quantum-Classical Deep Learning Framework for Industrial Datasets in Near-Term Devices
Gabriel Mazzucchi, Santiago F. Caballero-Benitez, Igor B. Mekhov

In this work, we introduce the quantum-assisted Helmholtz machine: a hybrid quantum-classical framework with the potential of tackling high-dimensional real-world machine learning datasets on continuous variables. We use deep learning to extract a low-dimensional binary representation of data, suitable for processing on relatively small quantum computers. The quantum hardware and deep learning architecture work together to train an unsupervised generative model.

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58
21.08.2017
Readiness of Quantum Optimization Machines for Industrial Applications
Alejandro Perdomo-Ortiz, Alexander Feldman, Asier Ozaeta, Sergei V. Isakov, Zheng Zhu, Bryan O’Gorman, Helmut G. Katzgraber, Alexander Diedrich, Hartmut Neven, Johan de Kleer, Brad Lackey, Rupak Biswas

We analyse the readiness of quantum annealing machines for real world application problems. These are typically not random and have an underlying structure that is hard to capture in synthetic benchmarks, thus posing unexpected challenges for optimisation techniques. We present a comprehensive computational scaling analysis of fault diagnosis in digital circuits, considering architectures beyond D-wave quantum annealers.

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59
29.06.2017
A Universal Completion of the ZX-calculus
Kang Feng Ng, Quanlong Wang

In this paper, we give a universal completion of the ZX-calculus for the whole of pure qubit quantum mechanics. This proof is based on the completeness of another graphical language, the ZW-calculus, with direct translations between these two graphical systems.

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60
18.09.2016
Quantum Speed-Ups for Semidefinite Programming
Fernando Brandao, Krysta Svore

We give a quantum algorithm for solving semidefinite programs (SDPs). The quantum algorithm is constructed by a combination of quantum Gibbs sampling and the multiplicative weight method. In particular, it is based on a classical algorithm of Arora and Kale for approximately solving SDPs. We present a modification of their algorithm to eliminate the need for solving an inner linear program which may be of independent interest.

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61
18.09.2016
On the Computational Complexity of Detecting Possibilistic Locality
Andrew Simmons

We consider the computational task of determining whether or not a given table of possibilities constitutes a departure from possibilistic local realism. By considering the case in which one party has access to measurements with two outcomes and the other three, it is possible to see at exactly which point this task becomes computationally difficult.

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62
25.08.2016
Thermoelectric Coefficients of N-Doped Silicon from First-Principles via the Solution of the Boltzmann Transport Equation
Dr Mattia Fiorentini, Nicola Bonini

We present a first-principles computational approach to calculate thermoelectric transport coefficients via the exact solution of the linearised Boltzmann transport equation, also including the effect of non-equilibrium phonon populations induced by a temperature gradient. We use density functional theory and density functional perturbation theory for an accurate description of the electronic and vibrational properties of a system.

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63
16.10.2015
Quantum Measurement-Induced Antiferromagnetic Order and Density Modulations in Ultracold Fermi Gases in Optical Lattices
Gabriel Mazzucchi, Santiago F. Caballero-Benitez, Igor B. Mekhov

We show that quantum backaction of weak measurement can be used for tailoring long-range correlations of ultracold fermions, realising quantum states with spatial modulations of the density and magnetisation, thus overcoming usual requirement for a strong interatomic interactions. We propose detection schemes for implementing antiferromagnetic states and density waves. We demonstrate that such long-range correlations cannot be realised with local addressing.

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64
15.07.2015
Efficient Implementation of Quantum Circuits with Limited Qubit Interactions
Stephen Brierley

The quantum circuit model allows gates between any pair of qubits, yet physical instantiations allow only limited interactions. We address this problem by providing an interaction graph together with an efficient method for compiling quantum circuits so that gates are applied only locally.

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